# Self-Supervised Inductive Logic Programming
**Authors**: Stassa Patsantzis
## Abstract
Inductive Logic Programming (ILP) approaches like Meta - Interpretive Learning (MIL) can learn, from few examples, recursive logic programs with invented predicates that generalise well to unseen instances. This ability relies on a background theory and negative examples, both carefully selected with expert knowledge of a learning problem and its solutions. But what if such a problem-specific background theory or negative examples are not available? We formalise this question as a new setting for Self-Supervised ILP and present a new MIL algorithm that learns in the new setting from some positive labelled, and zero or more unlabelled examples, and automatically generates, and labels, new positive and negative examples during learning. We implement this algorithm in Prolog in a new MIL system, called Poker. We compare Poker to state-of-the-art MIL system Louise on experiments learning grammars for Context-Free and L-System languages from labelled, positive example strings, no negative examples, and just the terminal vocabulary of a language, seen in examples, as a first-order background theory. We introduce a new approach for the principled selection of a second-order background theory as a Second Order Definite Normal Form (SONF), sufficiently general to learn all programs in a class, thus removing the need for a backgound theory tailored to a learning task. We find that Pokerâs performance improves with increasing numbers of automatically generated examples while Louise, bereft of negative examples, over-generalises.
Code â https://github.com/stassa/aaaiË26Ëexperiments/
Appendices â https://arxiv.org/abs/2507.16405
## Introduction
| Labelled examples $\mathbf{E^{+}{:}1^{n}0^{n}(n>0)}$ | Unlabelled examples $\mathbf{E^{?}{:}1^{n}0^{m}(n\geq m\geq 0)}$ (21 of 100 for $n\in[0,18]$ ) | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| $s(1^{1},0^{1}).$ | $s(1^{0},0^{0}).$ | $s(1^{3},0^{0}).$ | $s(1^{6},0^{0}).$ | $s(1^{9},0^{9}).$ | $s(1^{7},0^{7}).$ | $s(1^{6},0^{6}).$ | $s(1^{5},0^{3}).$ |
| $s(1^{2},0^{2}).$ | $s(1^{1},0^{0}).$ | $s(1^{4},0^{0}).$ | $s(1^{7},0^{0}).$ | $s(1^{8},0^{8}).$ | $s(1^{6},0^{1}).$ | $s(1^{5},0^{1}).$ | $s(1^{5},0^{5}).$ |
| $s(1^{3},0^{3}).$ | $s(1^{2},0^{0}).$ | $s(1^{5},0^{0}).$ | $s(1^{8},0^{0}).$ | $s(1^{7},0^{1}).$ | $s(1^{6},0^{2}).$ | $s(1^{5},0^{2}).$ | $s(1^{4},0^{1}).$ |
| | $one([1|X],X).$ | $zero([0|X],X).$ | $empty(X,X).$ |
| --- | --- | --- | --- |
| (Identity) $P(x,y)\leftarrow Q(x,y):$ (Chain) $P(x,y)\leftarrow Q(x,z),R(z,y):$ | $target(P)$ $\wedge\;background(Q)\vee empty(Q)$ $P\neq Q$ $\wedge\;(target(P)\vee invented(P))$ $\wedge\;not(target(Q))$ $\wedge\;not(empty(Q,R))$ $\wedge\;invented(P,Q)\rightarrow P\neq Q$ |
| --- | --- |
| With invented predicates ( $s_{1}/2$ ): Unfolded to remove invented predicates: | $s(A,B)\leftarrow one(A,C),zero(C,B).$ $s(A,B)\leftarrow one(A,C),zero(C,B).$ | $s(A,B)\leftarrow s_{1}(A,C),zero(C,B).$ $s_{1}(A,B)\leftarrow one(A,C),s(C,B)).$ $s(A,B)\leftarrow one(A,C),s(C,D),zero(D,B).$ | |
| --- | --- | --- | --- |
| Labelled Positive $s(1^{1},0^{1}).$ $s(1^{2},0^{2}).$ | Labelled Negative (21 of 175) $s(1^{4},0^{4}).$ $s(1^{5},0^{5}).$ | $s(1^{7},0^{7}).$ $s(1^{8},0^{8}).$ | $s(1^{0},0^{0}).$ $s(1^{1},0^{0}).$ | $s(1^{2},0^{1}).$ $s(1^{3},0^{0}).$ | $s(1^{3},0^{2}).$ $s(1^{4},0^{0}).$ | $s(1^{4},0^{2}).$ $s(1^{4},0^{3}).$ | $s(1^{5},0^{1}).$ $s(1^{5},0^{2}).$ | $s(1^{6},0^{0}).$ $s(1^{6},0^{1}).$ | $s(1^{7},0^{0}).$ $s(1^{7},0^{1}).$ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $s(1^{3},0^{3}).$ | $s(1^{6},0^{6}).$ | $s(1^{9},0^{9}).$ | $s(1^{2},0^{0}).$ | $s(1^{3},0^{1}).$ | $s(1^{4},0^{1}).$ | $s(1^{5},0^{0}).$ | $s(1^{5},0^{3}).$ | $s(1^{6},0^{2}).$ | $s(1^{8},0^{0}).$ |
Table 1: Poker inputs and outputs. Example strings pretty-printed from DCG notation (e.g. $s([1,1,0,0],[])\rightarrow s(1^{2},0^{2})$ ).
In the Standard Setting (Nienhuys-Cheng and de Wolf 1997) for Inductive Logic Programming (Muggleton 1991) (ILP) a background theory $B$ and positive and negative examples $E^{+},E^{-}$ are used as training data to learn a correct hypothesis $H$ that accepts, in conjunction with $B$ , all examples in $E^{+}$ , and no examples in $E^{-}$ . Typically, $E^{+},E^{-}$ are selected, and $B$ programmed, manually by the user according to their background knowledge of the learning target (i.e. the logic program to be learned). $B$ in particular is tailored to each learning target (Flener and Yilmaz 1999), and $E^{-}$ hand-picked to avoid over-generalisation. Given the right $B,E^{+}$ and $E^{-}$ , recent ILP systems can learn correct hypotheses with recursive clauses and invented predicates.
The ability to include background knowledge in training data is a desirable characteristic of ILP systems, but the practice of manually creating a target-specific background theory and selecting negative examples is a constant burden that limits the real-world application of ILP approaches.
In this paper we present a new way to alleviate the aforementioned burden on the user with a new algorithm for Self-Supervised ILP, more specifically, Self-Supervised Meta-Interpretive Learning (Muggleton et al. 2014) (MIL). The new algorithm, implemented in a new MIL system called Poker Named after, not the game, but Wittgensteinâs Poker; a friendly dig at Popper (Cropper and Morel 2021)., is âself-supervisedâ in the sense that it learns from both labelled positive and unlabelled examples and it automatically generates new positive and negative examples during learning. Because it generates new negative examples, Poker can learn from a background theory that is not tailored to the learning target without over-generalising. Poker returns not only a hypothesis, but also a labelling of unlabelled, and automatically generated, examples.
We illustrate the use of Poker in Table 1 where Poker is given 3 positive, labelled examples, $E^{+}$ , of the $\{1^{n}0^{n}:n>0\}$ ( $1^{n}0^{n}$ ) Context-Free Language (CFL), and 30 unlabelled examples, $E^{?}$ of the $\{1^{n}0^{m}:n\geq m\geq 0\}$ ( $1^{n}0^{m}$ ) CFL which includes $1^{n}0^{n}$ as a subset. Examples are atoms in Definite Clause Grammars (DCG) notation (Pereira and Shieber 1987) representing strings of $1^{n}0^{n}$ and $1^{n}0^{m}$ . The first-order background theory, $B$ , consists of just the terminal vocabulary of both languages, $\{1,0,\epsilon\}$ , defined as a set of DCG pre-terminals. $1^{n}0^{n}$ and $1^{n}0^{m}$ are more often denoted as $a^{n}b^{n}$ and $a^{n}b^{m}$ respectively, but replacing $a,b$ with $1,0$ makes it possible for $B$ to express any grammar with two terminal symbols suitably mapped to $1$ and $0$ . $B$ can also be constructed automatically from $E^{+},E^{?}$ . A second-order background theory, $\mathcal{M}$ , includes two metarules, Chain and Identity, Second-Order definite clauses with a set of constraints encoding a Second Order Definite Normal Form (SONF) SONFs are formalised in the Framework Section.. The background theory $B\cup\mathcal{M}$ is thus sufficiently general to express, as a DCG, a Context-Free Grammar (CFG) of any bit-string CFL or Regular language, i.e. one with a vocabulary of at most two characters and $\epsilon$ .
The maximal generality of $B\cup\mathcal{M}$ in Table 1 achieves two purposes. On the one hand it guarantees that $B\cup\mathcal{M}$ is general enough to learn the target grammar, i.e. a CFG of $1^{n}0^{n}$ . On the other hand, $B\cup\mathcal{M}$ is no longer target specific: instead of being tailored to one learning target, $1^{n}0^{n}$ , it can be reused to learn any bit-string CFG. At the same time, the generality of $B\cup\mathcal{M}$ introduces a problem: in the absence of negative examples, it is impossible to distinguish $1^{n}0^{n}$ from $1^{n}0^{m}$ (the language of $E^{?}$ ). Indeed, without negative examples it is impossible to distinguish any bit-string language from $\{0,1\}^{*}$ the maximally general language of all bit-strings. In order to learn $1^{n}0^{n}$ without over-generalising, therefore, negative examples are necessary. Poker can generate negative examples automatically, thus avoiding over-generalisation.
It is also possible to avoid over-generalisation by learning a hypothesis that only accepts $E^{+}$ , i.e. over-fitting $E^{+}$ . Poker returns a labelling of $E^{?}$ which, in Table 1, include $1^{n}0^{n}$ strings that are not in $E^{+}$ . In order to correctly label examples in $E^{?}$ Poker must therefore learn a hypothesis that generalises at least to the $1^{n}0^{n}$ strings in $E^{?}$ .
In summary, Pokerâs ability to automatically generate negative examples makes it possible to use a maximally general background theory that is no longer tailored to a single learning target. This ability frees the user from having to manually select a background theory and negative examples for each new learning target.
### Self-Supervised ILP by detection of contradictions
The intuition behind Pokerâs algorithm is that, if two atoms (in the First Order Logic sense) $e_{1}$ and $e_{2}$ are accepted by the same hypothesis $H$ (a logic program), i.e. $H\models\{e_{1},e_{2}\}$ , then to assume that $e_{1}$ is a positive and $e_{2}$ a negative example of $H$ is a contradiction (in the informal sense). Such a contradiction can be detected in the following manner. Suppose $T=\{H_{1},...,H_{m}\}$ is a set of hypotheses and $T\models\{e_{1},e_{2}\}$ . And suppose that we remove from $T$ each $H_{i}$ , where $H_{i}\models e_{2}$ leaving behind the set $T^{\prime}$ such that $T^{\prime}\not\models e_{2}$ . There are now two possibilities: either $T^{\prime}\models e_{1}$ , in which case there is no contradiction; or $T^{\prime}\not\models e_{1}$ , in which case there is a contradiction: $e_{1}$ and $e_{2}$ are both accepted by some subset of $T$ , now missing from $T^{\prime}$ ; therefore, $e_{2}$ is a positive, not a negative, example of the subset of $T$ that accepts $e_{1}$ .
Accordingly, Poker begins by constructing a set $T$ of initial hypotheses that accept the labelled examples $E^{+}$ . Poker can generate new unlabelled examples, added to $E^{?}$ , by executing $T$ as a generator. Poker then assumes that each unlabelled example $e^{?}\in E^{?}$ is negative and removes from $T$ each hypothesis $H$ that accepts $e^{?}$ in conjunction with $B$ . If the remaining $T$ now rejects any examples in $E^{+}$ , $e^{?}$ is re-labelled as positive and moved to $E^{+}$ . The labelling process thus iteratively specialises $T$ until it is consistent with $E^{+}$ . The labelling process is not without error but its accuracy increases monotonically with the cardinality of $E^{?}$ .
### Contributions
We make the following contributions:
- A new setting for Self-Supervised ILP.
- A new MIL algorithm for Self-Supervised ILP, and a new MIL system, Poker, implementing the new algorithm.
- A definition of Second-Order Definite Normal Forms (SONFs), a new kind of second-order background theory sufficiently general to learn all programs in a class.
- Two SONFs for CFGs and L-System grammars in DCG notation.
- A proof that Pokerâs accuracy increases monotonically with the number of unlabelled examples.
- Experiments investigating the effect of automatically generated examples on Pokerâs learning performance.
## Related Work
### Self-Supervised Learning
Self-Supervised Learning is introduced in (Raina et al. 2007), albeit under the rubric of âSelf-Taught Learningâ, where few, labelled, positive examples are used along with a larger number of unlabelled examples to learn discriminative features for âdownstreamâ supervised learning tasks, particularly image classification. Later approaches expect only unlabelled examples but generate negative examples automatically by means of âpretextâ tasks (Gui et al. 2023), such as image translations or rotations, as in Contrastive Learning (Hu et al. 2024). Choosing pretext tasks requires domain knowledge and generated examples are not used in downstream supervised learning tasks. Poker instead automatically generates both positive and negative examples and labels them consistently with labelled examples without the need of pretext tasks. Additionally Poker does not learn features for downstream learning tasks but directly uses its automatically generated examples to learn arbitrary logic programs. Logic programs learned by Poker are not classifiers but instead can be executed as either discriminators or generators as we demonstrate in the Experiments Section.
### Self-Supervised Learning in ILP
We are not aware of earlier work on self-supervised ILP. The problem of ILPâs over-reliance on manually defined background theories and negative examples has been tackled before.
Learning from a single example, a.k.a. one-shot learning, is comparable to self-supervised learning, in that only a single example is given that is assumed to be positive and there are no negative examples. Numerous works in the MIL literature demonstrate learning from positive-only, or single examples, e.g. (Lin et al. 2014; Dai et al. 2018). However, those systems rely on a problem-specific background theory selected manually and ad-hoc to avoid over-generalisation. In this paper instead we introduce the use of Second Order Definite Normal Forms as a new, principled way to manually select a second-order background theory for MIL.
DeepLog (Muggleton 2023) is a recent MIL system that explicitly learns from single examples, and a standard library of low-level primitives that apply to entire classes of problems, instead of a problem-specific background theory. DeePlog is limited to second-order background theories in dyadic logic (with literals of arity 2). Instead Pokerâs SONFs are not limited by arity, or other syntactic property.
Popper (Cropper and Morel 2021) is a system of the recent Learning from Failures setting for ILP that aims to learn logic programs without recourse to a user-provided higher-order background theory, such as required by Poker and other MIL systems. However, Popper still needs a problem-specific first-order background theory, extra-logical syntactic bias in the form of mode declarations, and negative examples. The need for negative examples is a limitation addressed in (Yang and Sergey 2025) The authors consider the need for negative examples a limitation of âclassicâ or âstandardâ ILP by which they seem to refer specifically to Popper; that is an over-generalisation.. The approach in (Yang and Sergey 2025) only applies to recursive data structures like lists or heaps. Instead Pokerâs algorithm learns arbitrary logic programs with recursion and invented predicates.
Meta Abd (Dai and Muggleton 2021) is a neuro-symbolic MIL system that trains a deep neural net in tandem with a MIL system modified for abductive learning, on unlabelled, sub-symbolic examples. Meta Abd relies on a manually defined, problem-specific first-order background theory in the form of definitions of adbucible predicates. Instead Poker does not require a problem-specific background theory.
## Framework
In the following, we assume familiarity with the logic programming terminology introduced e.g. in (Lloyd 1987).
### Meta-Interpretive Learning
| Identity Inverse Precon | $\exists P,Q\;\forall x,y$ : $P(x,y)\leftarrow Q(x,y)$ $\exists P,Q\;\forall x,y$ : $P(x,y)\leftarrow Q(y,x)$ $\exists P,Q\;\forall x,y$ : $P(x,y)\leftarrow Q(x),R(x,y)$ |
| --- | --- |
| Chain | $\exists P,Q,R\;\forall x,y,z$ : $P(x,y)\leftarrow Q(x,z),R(z,y)$ |
Table 2: Metarules commonly found in the MIL literature.
Meta-Interpretive Learning (Muggleton et al. 2014; Muggleton and Lin 2015) (MIL) is a setting for ILP where first-order logic theories are learned by SLD-Refutation of ground atoms $E^{+},E^{-}$ given as positive and negative training examples, respectively. $E^{+},E^{-}$ are resolved with a higher-order background theory that includes both first- and secod-order definite clauses, $B$ and $\mathcal{M}$ , respectively, the latter known as âmetarulesâ (Muggleton and Lin 2015). A first SLD-Refutation proof of $E^{+}$ produces an initial hypothesis that is then specialised by a second SLD-Refutation step, this time proving $E^{-}$ to identify, and discard, over-general hypotheses. The âmetarulesâ, are second-order definite clauses with second-order variables existentially quantified over the set of predicate symbols, which can optionally include automatically generated invented predicate symbols, and with first-order variables existentially or universally quantified over the set of first-order terms, in $B,E^{+}$ . During SLD-Refutation of $E^{+}$ metarulesâ second-order variables are unified to predicate symbols, constructing the clauses of a first-order hypothesis, $H$ . Table 2 lists metarules commonly found in the MIL literature.
### A Self-Supervised Learning setting for ILP
In the Normal Setting for ILP (Nienhuys-Cheng and de Wolf 1997), we are given a first-order background theory $B$ , positive and negative examples $E^{+},E^{-}$ , and must find a correct hypothesis, a logic program $H$ , such that $B\cup H\models E^{+}$ and $\forall e^{-}\in E^{-}:B\cup H\not\models e^{-}$ . We now formally define our new, Self-Supervised Learning setting for ILP, SS-ILP, where there are no negative examples, only labelled positive, or unlabelled examples, and $B$ is replaced by a higher-order background theory that is maximally general. We model our definition after the definition of the Normal Setting for ILP formalised in (Nienhuys-Cheng and de Wolf 1997).
**Definition 1**
*Inductive Logic Programming: Self - Supervised Setting
- Not Given:
$\mathbf{\Theta}=P/n$ , a predicate $P$ of arity $n$ that we call the target predicate. Let $B_{H}$ be the Herbrand Base of $\Theta$ . Recall that the Herbrand Base of a predicate $P/n$ is the set of all ground atoms $P/n$ with variables substituted for ground terms in the Domain of Discourse, $\mathcal{U}$ , (or a new constant $\alpha$ if there are no ground terms in $\mathcal{U}$ ) (Nienhuys-Cheng and de Wolf 1997).
$\mathbf{I}$ , a Herbrand interpretation over $B_{H}$ that we call the intended interpretation. Let $I^{+},I^{-}\subseteq B_{H}$ be the sets of atoms in $B_{H}$ that are true and false under $I$ , respectively, such that $I^{+}\cup I^{-}=B_{H}$ and $I^{+}\cap I^{-}=\emptyset$ .
- Given:
$\mathbf{E=\{E^{+},E^{?}\}\subseteq B_{H}}$ , a finite set of ground atoms $P/n$ . Let $E^{+}\subseteq I^{+}$ , and $E^{?}\subseteq I^{+}\cup I^{-}$ . Either $E^{+}$ or $E^{?}$ may be empty, but not both. We say that examples in $E^{+}$ are labelled and examples in $E^{?}$ are unlabelled, denoting an assumption that $E^{+}$ are all true under $I$ and uncertainty of which atoms in $E^{?}$ are true under $I$ .
$\mathbf{\mathcal{T}=B\cup\mathcal{M}}$ , a higher-order background theory, where $B,\mathcal{M}$ are finite sets of first- and second-order definite clauses, respectively, and such that $\mathcal{M}\cup B\cup E\models I^{+}\cup I^{-}$ , i.e. $\mathcal{T}$ is maximally general with respect to $\Theta$ .
- Find:
$\mathbf{H}$ , a logic program that we call a hypothesis, a finite set of first-order definite program clauses such that:
1. $B\cup\mathcal{M}\models H$
1. $B\cup H\models I^{+}$
1. $\forall a\in I^{-}:B\cup H\not\models a$
$\mathbf{L=L^{+}\cup L^{-}}$ , a set of two finite sets of atoms that we call a labelling, such that:
1. $L^{+}\subseteq I^{+}$
1. $L^{-}\subseteq I^{-}$
If $H,L^{+},L^{-}$ satisfy the above criteria we call $H$ a correct hypothesis and $L$ a correct labelling under the Self-Supervised setting for ILP.*
### Poker: an algorithm for Self-Supervised ILP
Poker assumes that the higher-order background theory, $\mathcal{T}$ , is a Second-Order Definite Normal Form, abbreviated SONF for ease of pronounciation. Informally, a SONF is a set of constrained metarules that generalise the set of first-order logic program definitions of all possible interpretations $I$ over the Herbrand Base $B_{H}$ of $\Theta$ . A formal definition of constrained metarules and SONFs, follows.
First, we define constrained metarules extending the definition of metarules in (Muggleton and Lin 2015) with a set of constraints on the substitution of second-order variables.
**Definition 2 (Constrained Metarules)**
*Let $B,\mathcal{M},E^{+}$ be as in Definition 1, and let $\mathcal{P}$ be the set of all predicate symbols in $B,E^{+}$ , and 0 or more automatically generated invented predicate symbols. Let $S=\{S_{E},S_{B},S_{I},S_{\epsilon}\}$ be a set of disjoint subsets of $\mathcal{P}$ , the sets of symbols in $E^{+}$ , $B$ , invented predicates, and the set $\{\epsilon\}\subseteq S_{B}$ , respectively, and let $P,Q$ be two distinct second-order variables, and $P_{1},...,P_{n}$ a list of $n$ such, in a metarule $M\in\mathcal{M}$ . A metarule constraint on $M$ is one of the following atomic formulae, with the associated interpretation:
- $P=Q$ : $P,Q$ must be instanted to the same symbol in $\mathcal{P}$ .
- $P\neq Q$ : $P,Q$ must not be instantiated to the same symbol in $\mathcal{P}$ .
- $target(P_{1},...,P_{n})$ : each $P_{i}$ must be instantiated to a symbol in $S_{E}$ .
- $invented(P_{1},...,P_{n})$ : each $P_{i}$ must be instiantiated to a symbol in $S_{I}$ .
- $background(P_{1},...,P_{n})$ : each $P_{i}$ must be instantiated to a symbol in $S_{B}$ .
- $empty(P_{1},...,P_{n})$ : $P$ must be instantiated to $\epsilon$ .
- $invented(P,Q)\rightarrow P\neq Q$ : If $P,Q$ are instantiated to symbols in $S_{I}$ , $P$ must not be the same symbol as $Q$ .
- $invented(P,Q)\rightarrow P\geq Q$ : If $P,Q$ are instantiated to symbols in $S_{I}$ , $P$ must be above than or equal to $Q$ in the standard alphabetic ordering.
- $invented(P,Q)\rightarrow P\leq Q$ : If $P,Q$ are instantiated to symbols in $S_{I}$ , $P$ must be below than or equal to $Q$ in the standard alphabetic ordering. If $C_{M}^{1},C_{M}^{2}$ are two constraints on metarule $M$ , then $C_{M}^{1}\wedge C_{M}^{2}$ is a constraint on $M$ , interpreted as â $C_{M}^{1}$ and $C^{2}_{M}$ must both be trueâ. If $C_{M}^{1},C_{M}^{2}$ are two constraints on metarule $M$ , then $C_{M}^{1}\vee C_{M}^{2}$ is a constraint on $M$ , interpreted as âone or both of $C_{M}^{1}$ , $C^{2}_{M}$ must be trueâ. If $C_{M}$ is a constraint on metarule $M$ , then $\neg C_{M}$ is a constraint on $M$ , interpreted as the opposite of $C_{M}$ . E.g. $\neg target(P)$ is interpreted as â $P$ must not be instantiated to a symbol in $S_{E}$ â. A constrained metarule is a formula $M:C_{M}$ , where $M\in\mathcal{M}$ and $C_{M}$ is a constraint on $M$ . Constraints on a metarule $M$ are satisfied by a first order definite clause $C$ if, and only if, $C$ is an instance of $M$ with second-order variables instantiated according to the constraints on $M$ .*
Metarule constraints are primarily meant to control recursion, particularly to eliminate unnecessary recursion, including left recursion, and to reduce redundancy in the construction of Pokerâs initial hypotheses. Metarule constraints can thus improve Pokerâs efficiency, and in that sense serve much the same function as heuristics exploiting the common structure of problems in a class, as e.g. in Planning (Geffner and Bonet 2013) and SAT-Solving (Biere et al. 2021).
We now formalise the definition of SONFs.
**Definition 3 (Second-Order Definite Normal Form)**
*Let $\Theta$ , $B_{H}$ , $I$ , $I^{+}$ , $I^{-}$ , and $\mathcal{M}$ be as in Definition 1, with the additional stipulation that $\mathcal{M}$ is a set of constrained metarules as in Definition 2. Let $\mathcal{I}$ be the set of all Herbrand interpretations $I$ over $B_{H}$ and, for each $I\in\mathcal{I}$ let $\Pi_{I}$ be the set of all first-order definite programs $\Sigma_{1},...,\Sigma_{n}$ , such that $\forall\Sigma_{i}\in\Pi_{I}:\Sigma_{i}\models I^{+}$ and $\forall\Sigma_{i}\in\Pi_{I},\forall e^{-}\in I^{-}:\Sigma_{i}\not\models e^{-}$ . Let $\Pi^{*}$ be the set of all $\Pi_{I}$ for all $I$ over $B_{H}$ . $\mathcal{M}$ is a Second-order Definite Normal Form, abbreviated to SONF, for $\Theta$ , if, and only if, $\mathcal{M}\models\Pi^{\prime}$ for some subset $\Pi^{\prime}$ of $\Pi^{*}$ . $\mathcal{M}$ is a strong SONF for $\Theta$ if $\Pi^{\prime}=\Pi^{*}$ . $\mathcal{M}$ is a weak SONF for $\Theta$ if $\Pi^{\prime}\subset\Pi^{*}$ (i.e. $\Pi^{\prime}$ is a subset of, but not equal to, $\Pi^{*}$ ).*
The purpose of SONFs is to replace the use of problem-specific sets of metarules, previously common in MIL practice, with a maximally general second-order background theory sufficient to learn any logic program definition of a target predicate $\Theta$ , for any interpretation $I$ of $\Theta$ .
| (Identity) $P(x,y)\leftarrow Q(x,y):$ |
| --- |
| $target(P)$ $\wedge\;background(Q)\vee empty(Q)$ |
| (Chain) $P(x,y)\leftarrow Q(x,z),R(z,y):$ |
| $P\neq Q$ $\wedge\;(target(P)\vee invented(P))$ $\wedge\;not(target(Q))$ |
| $\wedge\;not(empty(Q,R))$ $\wedge\;invented(P,Q)\rightarrow P\neq Q$ |
| (Tri-Chain) $P(x,y)\leftarrow Q(x,z),R(z,u),S(u,y):$ |
| $P\neq Q$ $\wedge\;Q\neq R$ $\wedge\;R\neq S$ |
| $\wedge\;(target(P)\vee invented(P)$ ) $\wedge\;not(target(Q)$ |
| $\wedge\;not(empty(Q,R,S))$ $\wedge\;invented(P,Q)\rightarrow P\neq Q$ |
Table 3: Chomsky-Greibach SONF for CFL DCGs.
| $s\rightarrow\epsilon$ | $s\rightarrow empty.$ | $s(x,y)\leftarrow empty(x,y).$ | Identity $P(x,y)\leftarrow Q(x,y)$ |
| --- | --- | --- | --- |
| $N_{0}\rightarrow N_{1}N_{2}$ | $n_{0}\rightarrow n_{1},n_{2}.$ | $n_{0}(x,y)\leftarrow n_{1}(x,z),n_{2}(z,y).$ | Chain $P(x,y)\leftarrow Q(x,z),R(z,y)$ |
| $N_{i}\rightarrow t$ | $n_{i}\rightarrow t.$ | $n_{i}(x,y)\leftarrow t(x,y).$ | Identity $P(x,y)\leftarrow Q(x,y)$ |
| $empty\rightarrow[\;].$ | $empty(x,x)$ | None (included in $B$ ) | |
| $t\rightarrow[t].$ | $t([t|x],x)$ | None (included in $B$ ) | |
Table 4: Chomsky Normal Form mapping to DCGs and C-GNF constrained metarules. $N_{i},n_{i}$ : non-terminals; $t$ : terminals.
#### Two Normal Forms
We illustrate the concept of Second Order Definite Normal Forms with two SONFs used in the Experiments Section: Chomsky-Greibach Normal Form (C-GNF) used to learn CFGs, and Lindenmayer Normal Form (LNF) used to learn L-System grammars. C-GNF is listed in Table 3. We list LNF and prove the completeness of C-GNF for CFLs, and LNF for L-systems, in the Appendix.
Derivation of a SONF is a process of abstraction that we do not currently know how to automate. Both C-GNF and LNF are therefore derived manually by the author. C-GNF is derived by an encoding of the rules of Chomsky and Greibach Normal Forms in our Second-Order Normal Form notation. LNF is derived from the common structure observed in manual definitions of L-System grammars as DCGs, coded by the author. Metarule constraints are obtained from experimentation to improve learning efficiency.
A guide for the construction of Second-Order Normal Forms is beyond the scope of the paper. However, we include Table 4 as an illustration of the derivation of C-GNF metarules (without constraints) from Chomsky Normal Form.
#### Pokerâs algorithm
We typeset Pokerâs algorithm for SS-ILP as Algorithm 1. We state our main theoretical result as Theorem 1. We include a proof of the Theorem in the Appendix.
Algorithm 1 Poker: SS-ILP by detection of contradictions
Input: $E^{+},E^{?},B,\mathcal{M}$ as in Definition 1; $\mathcal{M}$ is a SONF. $V$ , finite set of invented predicate symbols. Integer $k\geq 0$ , max. number of automatically generated examples. Integer $l\geq 0$ , max. number of clauses in initial hypotheses $H$ . Initialise: Hypothesis $T\leftarrow\emptyset$ . Labelling $L=\{E^{+},E^{-}\leftarrow\emptyset\}$ .
1: procedure Generalise ( $B,\mathcal{M},E^{+}$ )
2: $T\leftarrow\{H_{|H|\leq l}:\mathcal{M}\cup B\cup V\models H\wedge B\cup H\models e^{+}\in E^{+}\}$
3: end procedure
4: procedure Generate ( $B,T,E^{?}$ )
5: $T^{\prime}\leftarrow\bigcup T$ $\triangleright$ This step is optional. Alternatively $T^{\prime}\leftarrow T$
6: $E^{-}\leftarrow E^{?}\cup\{e_{i=1}^{k}:\;B\cup T^{\prime}\models e_{i}\}$
7: end procedure
8: procedure Label ( $B,T,E^{+},E^{-}$ )
9: for all $e\in E^{-}$ do
10: $T^{\prime}\leftarrow T\setminus\{H:\;H\in T\wedge B\cup H\models e\}$
11: if $B\cup T^{\prime}\models E^{+}$ then
12: $T\leftarrow T^{\prime}$
13: else
14: $E^{-}\leftarrow E^{-}\setminus\{e\}$
15: $E^{+}\leftarrow E^{+}\cup\{e\}$
16: end if
17: end for
18: end procedure
19: $T\leftarrow\bigcup T$ $\triangleright$ This step is optional.
20: $T\leftarrow T\setminus\{C\in T:T\models C\}$ $\triangleright$ This step is optional.
21: Return $T,L=\{E^{+},E^{-}\}$
**Theorem 1 (Hypothesis Correctness)**
*The probability that Algorithm 1 returns a correct hypothesis increases monotonically with the number of unlabelled examples.*
## Implementation
We implement Algorithm 1 in a new system, also called Poker, written in Prolog. Poker extends the Top Program Construction algorithm (Patsantzis and Muggleton 2021) (TPC). Procedure Generalise in Algorithm 1, taken from TPC, is implemented in Poker by a call to Vanilla, an inductive second-order Prolog meta-interpreter for MIL, introduced in (Trewern et al. 2024). Vanilla is developed with SWI-Prolog (Wielemaker et al. 2012) and uses SWI-Prologâs tabled execution, (Tamaki and Sato 1986), a.k.a. SLG-Resolution, to control recursion and improve performance.
## Experiments
Poker learns from three kinds of examples: user-given a) labelled, and b) unlabelled, examples, and, c) automatically generated examples. In preliminary experiments we find that the effect of (b), unlabelled examples, on Pokersâ learning performance depends on the generality of the unlabelled examples. This merits more thorough investigation. We limit the current investigation on the effect of automaticaly generated examples on Pokerâs performance, formalised in the following research question:
**Research Question 1**
*What is the effect of automatically generated examples on Pokerâs learning performance?*
We address this question with two sets of experiments, learning grammars for two sets of languages: Context-Free Languages (CFLs); and L-Systems. L-System grammars are meant to be run as generators, producing strings interpreted as movement and drawing commands sent to a (now usually simulated) robot. Accordingly, we evaluate learned L-System grammars as generators.
#### Baselines
There are, to our knowledge, no ILP systems that generate new examples during learning, other than Poker. We consider comparing Poker to ILP systems Aleph (Srinivasan 2001) and Popper (Cropper and Morel 2021). In preliminary experiments both perform poorly without negative examples. In private communication Popperâs authors confirm Popper should not be expected to learn CFGs without negative examples. We alight on the state-of-the-art MIL system Vanilla-Louise (Trewern et al. 2024) (hereby denoted as Louise, for brevity), as a simple baseline. Louise is also based on Vanilla and accepts constrained metarules, simplifying comparison.
### Experimental protocol
| $one\rightarrow[1]$ | $one([1|X],X)$ | $plus\rightarrow[+]$ | $plus([+|X],X)$ |
| --- | --- | --- | --- |
| $zero\rightarrow[0]$ | $zero([0|X],X)$ | $min\rightarrow[-]$ | $min([-|X],X)$ |
| $empty\rightarrow[\;]$ | $empty(X,X)$ | $f\rightarrow[f]$ | $f([f|X],X)$ |
| $g\rightarrow[g]$ | $g([g|X],X)$ | | |
| $x\rightarrow[x]$ | $x([x|X],X)$ | | |
| $y\rightarrow[y]$ | $y([y|X],X)$ | | |
Table 5: Background theories used in experiments.
In each set of experiments, we generate positive training examples and positive and negative testing examples from a manual definition of the learning target (the grammar to be learned) as a DCG.
We train Poker and Louise on increasing numbers, $l>0$ , of positive labelled examples. We vary the number of Pokerâs automatically generated examples, $k\geq 0$ , and record, a) for CFLs, the True Positive Rate (TPR) and True Negative Rate (TNR) of i) hypotheses learned by both systems and ii) labellings returned by Poker; and b) for L-Systems i) the accuracy of learned hypotheses as generators (âGenerative Accuracyâ) and ii) the cardinality of learned hypotheses, i.e. their number of clauses (âHypothesis Sizeâ). Testing examples are used to evaluate CFL hypotheses as acceptors: for each hypothesis, $H$ , we test how many positive testing examples are accepted, and how many negative ones rejected, by $H$ . We do not use testing examples to evaluate labellings or L-System hypotheses as generators. Instead, we count the number of examples labelled positive or negative, in a labelling, or generated by a hypothesis executed as a generator, that are accepted or rejected, respectively for positive and negative examples, by the manually defined learning target.
In CFL experiments, we measure TPR and TNR to avoid confusing measurements when $k=0$ .
Experiment results are listed in Figures 1 and 2 for L-Systems and CFLs, respectively. In each Figure, the x-axis plots the number of labelled positive examples, the y-axis plots mean TPR, TNR, Generative Accuracy or Hypothesis Size, while each line plots the change in that metric with each increment of $k$ , the number of automatically generated examples. For Louise, $k=0$ always. Error bars are standard error over 100 randomly sampled sets of training and testing examples in each experiment.
### Experiment 1: L-Systems
*[Error downloading image: 2507.16405v2/l_systems.png]*
Figure 1: Learning L-Systems: generative accuracy. *[Error downloading image: 2507.16405v2/binary_cfls.png]*
Figure 2: Learning CFGs. TPR: True Positive Rate. TNR: True Negative Rate.
We train Poker and Louise on example strings of the L-Systems for the Dragon Curve, Hilbert Curve, Koch Curve, and Sierpinski Triangle fractals, taken from (Prusinkiewicz and Lindenmayer 1996). The first-order background theory consists of a set of symbols, common for all L-Systems, but not necessarily with the same meaning for each, e.g. the symbol $f$ is a variable (nonterminal) in Dragon and Koch Curve, but a constant (terminal) in Hilbert Curve. This serves as a test of a systemâs robustness to noise. The second-order background theory is LNF, for all L-systems.
Results are shown in Figure 1. We observe that Pokerâs Generative Accuracy increases, and Hypothesis Size decreases, with the number of automatically generated examples. Louiseâs Generative Accuracy decreases and Hypothesis Size increases with the number of labelled examples, indicating increasing over-generalisation. This indicates that increasing numbers of automatically generated examples improve Pokerâs performance and avoid over-generalisation in language generation tasks.
### Experiment 2: Binary CFLs
We train Poker and Louise on examples of six CFLs: a) The language of bit-strings with an even number of 1âs (even parity), b) $\{a^{n}b^{n}:n>0\}$ , c) $\{w\in\{a,b\}^{*}:n_{a}(w)=n_{b}(w),n\geq 0\}$ (n aâs and n bâs in any order), d) $\{a^{n}b^{m}|n\geq m\geq 0\}$ , e) the language of palindromic bit-strings and f) the language of balanced parentheses. Symbols $a,b,)$ , and $($ are suitably replaced by 1 and 0, so that all experiments can share the same first- and second-order background theory, consisting of the alphabet $\{1,0,\epsilon\}$ , in DCG form, as listed in Table 5, and C-GNF Note that the Tri-Chain metarule is only used with Palindrome., respectively.
Results are listed in Figure 2. When $k=0$ , Pokerâs TPR is maximal while its TNR is minimal, because there are no negative examples. When $k>0$ both TPR and TNR increase with $k$ , until they are both maximised with the highest increments of $k$ . From this we conclude that when $k$ is low, Pokerâs labelling and hypothesis over-generalise, and when $k$ is sufficiently large, Poker learns a correct labelling and hypothesis. Thus, Pokerâs performance improves with the number of automatically generated examples. Louise over-generalises consistently over all experiments.
## Conclusions and Future Work
ILP systems can generalise well from few examples given a problem-specific background theory and negative examples, both manually selected with domain knowledge. We have endeavoured to address this onerous requirment with a new formal setting for Self-Supervised ILP, SS-ILP, and presented Poker, a new MIL system that learns in the new setting. Pokerâs algorithm learns from labelled and unlabelled examples, and automatically generates new positive and negative examples during learning. Instead of a problem-specific background theory Poker only needs a maximally general, Second Order Normal Form (SONF). We have presented two SONFs, for Context-Free and L-System Definite Clause Grammars. We have given a theoretical proof and empirical evidence that Pokerâs accuracy improves with increasing numbers of unlabelled examples, and showed how a baseline MIL system lacking Pokerâs ability to automatically generate negative examples over-generalises.
Our theoretical results can be extended with further proofs of the correctness of Pokerâs algorithm, including with respect to varying amounts of labelled and unlabelled examples; and of its computational efficiency. Our empirical results are restricted to grammar learning. Future work should investigate Pokerâs application to more diverse domains.
## Acknowledgments
We thank Em. Professor Stephen Muggleton and Drs Lun Ai and CĂ©line Hocquette for their kind feedback on this work.
## References
- A. Biere, M. Heule, H. van Maaren, and T. Walsh (Eds.) (2021) Handbook of satisfiability - second edition. Frontiers in Artificial Intelligence and Applications, Vol. 336, IOS Press. External Links: ISBN 978-1-64368-160-3 Cited by: Poker: an algorithm for Self-Supervised ILP.
- A. Cropper and R. Morel (2021) Learning programs by learning from failures. Machine Learning. External Links: ISSN 1573-0565 Cited by: Self-Supervised Learning in ILP, Baselines, footnote 1.
- W. Dai, S. Muggleton, J. Wen, A. Tamaddoni-Nezhad, and Z. Zhou (2018) Logical vision: one-shot meta-interpretive learning from real images. In Inductive Logic Programming, N. Lachiche and C. Vrain (Eds.), Cham, pp. 46â62. External Links: ISBN 978-3-319-78090-0 Cited by: Self-Supervised Learning in ILP.
- W. Dai and S. Muggleton (2021) Abductive knowledge induction from raw data. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, Z. Zhou (Ed.), pp. 1845â1851. Note: Main Track Cited by: Self-Supervised Learning in ILP.
- P. Flener and S. Yilmaz (1999) Inductive synthesis of recursive logic programs: achievements and prospects. The Journal of Logic Programming 41 (2), pp. 141 â 195. External Links: ISSN 0743-1066, Link Cited by: Introduction.
- H. Geffner and B. Bonet (2013) A concise introduction to models and methods for automated planning: synthesis lectures on artificial intelligence and machine learning. 1st edition, Morgan & Claypool Publishers. External Links: ISBN 1608459691 Cited by: Poker: an algorithm for Self-Supervised ILP.
- S. A. Greibach (1965) A new normal-form theorem for context-free phrase structure grammars. J. ACM 12 (1), pp. 42â52. External Links: ISSN 0004-5411, Link Cited by: Appendix B.
- J. Gui, T. Chen, J. Zhang, Q. Cao, Z. Sun, H. Luo, and D. Tao (2023) A survey on self-supervised learning: algorithms, applications, and future trends. IEEE Transactions on Pattern Analysis and Machine Intelligence 46, pp. 9052â9071. Cited by: Self-Supervised Learning.
- J. E. Hopcroft and J. D. Ullman (1969) Formal languages and their relation to automata. Addison-Wesley Longman Publishing Co., Inc., USA. Cited by: Appendix B.
- H. Hu, X. Wang, Y. Zhang, Q. Chen, and Q. Guan (2024) A comprehensive survey on contrastive learning. Neurocomputing 610, pp. 128645. External Links: ISSN 0925-2312 Cited by: Self-Supervised Learning.
- D. Lin, E. Dechter, K. Ellis, J. Tenenbaum, S. Muggleton, and M. Dwight (2014) Bias reformulation for one-shot function induction. In Proceedings of the 23rd European Conference on Artificial Intelligence, pp. 525â530. External Links: ISBN 9781614994190 Cited by: Self-Supervised Learning in ILP.
- A. Lindenmayer (1968a) Mathematical models for cellular interactions in development i. filaments with one-sided inputs. Journal of Theoretical Biology 18 (3), pp. 280â299. External Links: ISSN 0022-5193, Document, Link Cited by: Appendix C.
- A. Lindenmayer (1968b) Mathematical models for cellular interactions in development ii. simple and branching filaments with two-sided inputs. Journal of Theoretical Biology 18 (3), pp. 300â315. External Links: ISSN 0022-5193, Document, Link Cited by: Appendix C.
- J. W. Lloyd (1987) Foundations of logic programming. Symbolic Computation, Springer Berlin Heidelberg, Berlin, Heidelberg. External Links: ISBN 978-3-642-83191-1 Cited by: Framework.
- S. H. Muggleton (2023) Hypothesizing an algorithm from one example: the role of specificity. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 381 (2251), pp. 20220046. External Links: https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2022.0046 Cited by: Self-Supervised Learning in ILP.
- S. H. Muggleton, D. Lin, N. Pahlavi, and A. Tamaddoni-Nezhad (2014) Meta-interpretive learning: Application to grammatical inference. Machine Learning 94 (1), pp. 25â49. External Links: ISSN 08856125 Cited by: Introduction, Meta-Interpretive Learning.
- S. Muggleton and D. Lin (2015) Meta-Interpretive Learning of Higher-Order Dyadic Datalog : Predicate Invention Revisited. Machine Learning 100 (1), pp. 49â73. Cited by: Meta-Interpretive Learning, Poker: an algorithm for Self-Supervised ILP.
- S. Muggleton (1991) Inductive Logic Programming. New Generation Computing 8 (4), pp. 295â318. External Links: ISSN 1882-7055 Cited by: Introduction.
- S. Nienhuys-Cheng and R. de Wolf (1997) Foundations of Inductive Logic programming. Springer-Verlag, Berlin. Cited by: Appendix A, Introduction, 1st item, A Self-Supervised Learning setting for ILP.
- S. Patsantzis and S. H. Muggleton (2021) Top program construction and reduction for polynomial time meta-interpretive learning. Machine Learning. External Links: ISSN 1573-0565 Cited by: Implementation.
- F. Pereira and S. M. Shieber (1987) Prolog and natural-language analysis. Vol. 10, Center for the Study of Language and Information. Cited by: Introduction.
- P. Prusinkiewicz and A. Lindenmayer (1996) The algorithmic beauty of plants. Springer-Verlag, Berlin, Heidelberg. External Links: ISBN 0387946764 Cited by: Appendix C, Appendix C, Experiment 1: L-Systems.
- R. Raina, A. Battle, H. Lee, B. Packer, and A. Ng (2007) Self-taught learning: transfer learning from unlabeled data. In International Conference on Machine Learning, Cited by: Self-Supervised Learning.
- C. Rouveirol (1994) Flattening and saturation: two representation changes for generalization. Machine Learning 14 (2), pp. 219â232. External Links: ISSN 1573-0565, Link Cited by: Appendix C.
- A. Srinivasan (2001) Aleph version 4 and above. External Links: Link Cited by: Baselines.
- H. Tamaki and T. Sato (1986) OLD resolution with tabulation. In Third International Conference on Logic Programming, E. Shapiro (Ed.), Berlin, Heidelberg, pp. 84â98. External Links: ISBN 978-3-540-39831-8 Cited by: Implementation.
- J. Trewern, S. Patsantzis, and A. Tamaddoni-Nezhad (2024) Meta-interpretive learning as second-order resolution. In Proceedings of the Fourth International Joint Conferences on Learning and Reasoning IJCLR 24. In print., S. H. Muggleton and A. Tamaddoni-Nezhad (Eds.), Cited by: Appendix A, Implementation, Baselines.
- J. Wielemaker, T. Schrijvers, M. Triska, and T. Lager (2012) SWI-Prolog. Theory and Practice of Logic Programming 12 (1-2), pp. 67â96. External Links: ISSN 1471-0684 Cited by: Implementation.
- Z. Yang and I. Sergey (2025) Inductive synthesis of inductive heap predicates â extended version. External Links: 2502.14478, Link Cited by: Self-Supervised Learning in ILP.
## Appendix A Proof of Theorem 1
In this section we prove our main theoretical result in Theorem 1.
Lemma 1 states that all correct hypotheses are included in the set of initial hypotheses.
**Lemma 1 (Generality of Initial Hypotheses)**
*Let $\Theta$ , $I^{+}$ , $I^{-}$ be as in Definition 1. Let $B$ , $\mathcal{M}$ , $E^{+}$ , $V$ , $l$ , be as in the inputs of Algorithm 1 where $\mathcal{M}$ is a Second-Order Definite Normal Form for $\Theta$ and $E^{+}\subseteq I^{+}$ . Let $H^{*}$ be a correct hypothesis according to Definition 1 such that $\mathcal{M}\cup B\cup V\models H^{*}$ , $B\cup H^{*}\models E^{+}$ , and $|H^{*}|\leq l$ . And let $T$ be the set of initial hypotheses constructed by Procedure Generalise in Line 10 of Algorithm 1. Then, $\forall H^{*}:H^{*}\in T$ .*
* Proof*
Follows from the refutation completeness of Second-Order SLD-Resolution (Trewern et al. 2024) and the definition of Second-Order Definite Normal Forms in Definition 3. Because of the refutation completeness of Second-Order SLD-Resolution, Procedure Generalise derives each hypothesis $H$ such that $\mathcal{M}\cup B\cup V\models H$ and $B\cup H\models E^{+}$ where $|H|\leq l$ , in Line 2 of Algorithm 1. This is true for each $H=H^{*}$ because if $\mathcal{M}$ is a Second Order Definite Normal Form for $\Theta$ , then $\mathcal{M}\models H^{*}$ , according to Definition 3, therefore $\mathcal{M}\cup B\cup V\models H^{*}$ also, and $B\cup H^{*}\models E^{+}$ by the assumption in Lemma 1. â
Lemma 2 states that all hypotheses accepting a negative example are removed from the set of initial hypotheses.
**Lemma 2 (Correct Specialisation)**
*Let $B$ , $T$ , $E^{+}$ , $E^{-}$ be as in the inputs of Procedure Label in Algorithm 1 and let $T^{\prime}=T\setminus\{H:\;H\in T\wedge B\cup H\models e\}$ , the set constructed by Procedure Label in Line 10 of Algorithm 1. Then, $\forall e\in E^{-}:T^{\prime}\not\models e$ .*
* Proof*
Follows from the soundness of (first-order) SLD-Resolution (Nienhuys-Cheng and de Wolf 1997). If $T^{\prime}$ is the set $T$ , set-minus all $H\in T$ such that $B\cup H\models e$ , then because of the soundness of SLD-Resolution each such $H$ is correctly removed from $T$ on Line 10 of Algorithm 1 and there remains no H in $T^{\prime}$ such that $B\cup H\models e$ . â
Lemma 3 states that only hypotheses accepting negative examples are removed from the set of initial hypotheses.
**Lemma 3 (Correct Labelling)**
*Let $e\in E^{-}$ be a negative example, i.e. $e\in I^{-}$ , let $T,T^{\prime}$ be as in Lemma 2, and let $T^{\prime\prime}=\{H:\;H\in T\wedge B\cup H\models e\}$ be the set of hypotheses removed from the set of initial hypotheses $T$ , by Procedure Label in Line 10 of Algorithm 1. Then, for each hypothesis $H^{\prime\prime}\in T^{\prime\prime}$ , $H^{\prime\prime}$ is not a correct hypothesis, according to Definition 1.*
* Proof*
Follows from Lemma 2 and the definition of correct hypotheses in Definition 1. If $e\in I^{-}$ then according to Lemma 2, for each hypothesis $H^{\prime\prime}$ in $T^{\prime\prime}$ , it is true that $B\cup H^{\prime\prime}\models e$ , therefore it is not true that $\forall a\in I^{-}:B\cup H^{\prime\prime}\not\models a$ , and $H^{\prime\prime}$ is not a correct hypothesis according to Definition 1. â
Lemma 4 states the least number of negative examples needed to remove all hypotheses that accept negative examples from the set of initial hypotheses.
**Lemma 4 (Least Negative Examples)**
*Let $n$ be the number of all hypotheses, and let $m$ be the number of all correct hypotheses, in the set $T$ of initial hypotheses constructed by Procedure Generalise in Line 2 of Algorithm 1. Procedure Label in Line 10 of Algorithm 1 constructs the set $T^{\prime\prime}\subseteq T$ of all hypotheses $H^{\prime\prime}\in T$ such that $B\cup H^{\prime\prime}\models e$ , for each unlabelled example $e\in E^{-}$ . The least cardinality of the set $E^{-}$ sufficient to reduce $T$ to a set of only correct hypotheses is $n-m$ .*
* Proof*
Follows from Lemma 3. For each hypothesis $H^{\prime\prime}$ in the set $T$ of initial hypotheses, where $H^{\prime\prime}$ is not a correct hypothesis, Procedure Label removes $H^{\prime\prime}$ from $T$ given any negative example $e\in E^{-}$ such that $B\cup H^{\prime\prime}\models e$ , therefore a single negative example is sufficient to remove each such $H^{\prime\prime}$ . The number of such $H^{\prime\prime}$ is $n-m$ , therefore $n-m$ such examples are sufficient to remove each such $H^{\prime\prime}$ . â
We now restate, and prove, Theorem 1.
**Theorem 2 (Hypothesis Correctness)**
*The probability that Algorithm 1 returns a correct hypothesis increases monotonically with the number of unlabelled examples.*
* Proof*
Follows from Lemmas 1 - 4. According to Lemma 1, because of the refutation completeness of Second-Order SLD-Resolution the set of initial hypotheses $T$ includes one or more correct hypotheses $H^{*}$ . It further follows from the refutation completeness of Second-Order SLD Resolution that $T$ also includes zero or more hypotheses $H^{\prime\prime}$ which are not correct hypotheses. We shal refer to hypotheses other than correct hypotheses as incorrect hypotheses for the purpose of this proof. According to Lemmas 2 - 3, Procedure Label removes only each incorrect hypothesis $H^{\prime\prime}$ in $T$ such that $B\cup H^{\prime\prime}\models e$ for each unlabelled example $e\in E^{-}$ where $e$ is a negative example, i.e. $e\in I^{-}$ by Definition 1. According to Lemma 4 the number of incorrect hypotheses $H^{\prime\prime}$ that must be removed from $T$ to leave behind only correct hypotheses is $n-m$ , where $n=|T|$ and $m$ is the number of correct hypotheses $H^{*}\in T$ . As the cardinality of $E^{-}$ increases, the chance that it includes a negative example increases. With each negative example in $E^{-}$ the number of negative examples in $E^{-}$ increases towards $n-m$ , sufficient to remove each incorrect hypothesis $H^{\prime\prime}$ from $T$ . No new hypotheses are added to $T$ after $T$ is constructed by Procedure Generalise on line 2 of Algorithm 1, therefore the number $n-m$ of incorrect hypothesers $H^{\prime\prime}$ in $T$ never increases, instead it decreases monotonically, approaching 0. As the number of incorrect hypotheses decreases monotonically the probability that $T$ includes only correct hypotheses, therefore that Algorithm 1 returns a correct hypothesis increases monotonically. â
## Appendix B Chomsky-Greibach Second-Order Definite Normal Form
| (Identity) $P(x,y)\leftarrow Q(x,y):$ |
| --- |
| $target(P)$ $\wedge\;background(Q)\vee empty(Q)$ |
| (Chain) $P(x,y)\leftarrow Q(x,z),R(z,y):$ |
| $P\neq Q$ $\wedge\;(target(P)\vee invented(P))$ $\wedge\;not(target(Q))$ |
| $\wedge\;not(empty(Q,R))$ $\wedge\;invented(P,Q)\rightarrow P\neq Q$ |
| (Tri-Chain) $P(x,y)\leftarrow Q(x,z),R(z,u),S(u,y):$ |
| $P\neq Q$ $\wedge\;Q\neq R$ $\wedge\;R\neq S$ |
| $\wedge\;(target(P)\vee invented(P)$ ) $\wedge\;not(target(Q)$ |
| $\wedge\;not(empty(Q,R,S))$ $\wedge\;invented(P,Q)\rightarrow P\neq Q$ |
Table 6: Chomsky-Greibach Normal Form for CFL DCGs.
| $s\rightarrow\epsilon$ | $s\rightarrow empty.$ | $s(x,y)\leftarrow empty(x,y).$ | Identity $P(x,y)\leftarrow Q(x,y)$ |
| --- | --- | --- | --- |
| $N_{0}\rightarrow N_{1}N_{2}$ | $n_{0}\rightarrow n_{1},n_{2}.$ | $n_{0}(x,y)\leftarrow n_{1}(x,z),n_{2}(z,y).$ | Chain $P(x,y)\leftarrow Q(x,z),R(z,y)$ |
| $N_{i}\rightarrow t$ | $n_{i}\rightarrow t.$ | $n_{i}(x,y)\leftarrow t(x,y).$ | Identity $P(x,y)\leftarrow Q(x,y)$ |
| $empty\rightarrow[\;].$ | $empty(x,x)$ | None (1st order BK) | |
| $t\rightarrow[t].$ | $t([t|x],x)$ | None (1st order BK) | |
Table 7: Chomsky Normal Form mapping to DCGs and C-GNF constrained metarules. $N_{i},n_{i}$ : non-terminals; $t$ : terminals.
We present the Chomsky-Greibach Second Order Definite Normal Form (C-GNF) used in experiments with Context-Free Grammars (CFGs) in Section 5.2 and prove that it is, indeed, a Second Order Definite Normal Form for CFGs in Definite Clause Grammars (DCG) notation. C-GNF is listed initially in Table 3 and listed again in Table 6 in this Appendix for ease of reference.
C-GNF is based on a combination of Chomsky Normal Form (CNF) and Greibach Normal Form (GNF), two normal forms for CFGs known from the study of formal languages and automata (Hopcroft and Ullman 1969). Every CFG can be re-written into CNF, or GNF, or, indeed, both. GNF in particular eliminates left-recursions, and the consequent inefficiencies when parsing CFGs with push-down automata (Greibach 1965).
Every production in a CFG in CNF is in one of the forms shown in the first column of Table 4, where $N_{0},N_{1},N_{2},N_{i}$ are nonterminals, $N_{i}$ is a pre-terminal, $t$ is a terminal, and $\epsilon$ is the empty string.
Nonterminal symbols in the right-hand side of a CNF production cannot be the start symbol, a constraint trivially satisfied, when converting a CFG into CNF, by creating a new start symbol $S_{0}$ and expanding it to the original start symbol $S_{0}\rightarrow S$ . Since this is a trivial constraint we ignore it in C-GNF.
Additionally pre-terminal productions of the form $N_{i}\rightarrow t$ in CNF, are left to be defined in the first-order background theory of an SS-ILP learning problem Such nonterminals can be extracted automatically from the ground terms in training examples..
Otherwise, C-GNF metarule constraints enforce CNF, as listed in Table 6. In the Table, a second-order variable marked as a target (e.g. $target(P)$ ) is to be instantiated to the start symbol of a DCG which is also the predicate symbol of the labelled training examples. Second-order variables marked as background ( $e.g.background(Q)$ ) are instantiated to symbols in the first-order background theory, defining pre-terminals, with terminal symbols extracted from ground terms in labelled and unlabelled examples.
C-GNF allows second-order variables to be instantiated to invented predicate symbols. Predicate invention is necessary for the construction of nonterminals with exactly two symbols on the right-hand side.
GNF productions are all of the form $N_{0}\rightarrow t,N_{1},...$ i.e. the first symbol on the right-hand side of each production must be a terminal, thus no left-recursion is possible. C-GNF metarule constraints adopt a variant of GNF to enforce anti-left recursion assumptions while allowing for terminals to be defined in the first-order background theory (as pre-terminals), and for invented predicates. Thus e.g. $P\neq Q$ , a constraint on Chain and Tri-Chain, allows $Q$ to be instantiated to a pre-teraminal symbol, or an invented symbol, but not the symbol of the head literal of a metarule instance. Expanding to a pre-terminal is as incapable of producing left-recursions as expanding to a terminal, therefore this constraint suffices to eliminate immediate left-recursions, however it is still possible for left-recursions to occur between invented predicates.
The constraint $invented(P,Q)\rightarrow P\neq Q$ eliminates clauses of invented predicates that may result in âobliqueâ left-recursions. Consider a pair of clauses $inv_{1}\rightarrow inv_{2},...,$ and $inv_{2}\rightarrow inv_{1},...,$ . These two clauses would enter an infinite left-recursion when interpreted top-down by a standard Prolog execution strategy, even though neither is immediately left-recursive. Such constructions are eliminated by the above constraint.
### Completeness of C-GNF for Context-Free DCGs
We now show that C-GNF is a Second-Order Definite Normal Form for DCG definitions of Context-Free Languages (CFLs).
**Proposition 1**
*C-GNF is a Second Order Definite Normal Form for all two-character, Context-Free Definite Clause Grammars.*
* Proof*
We give a constructive proof. The simplest way to show that a set $\mathcal{M}$ of constrained metarules is a Normal Form for a target predicate $\Theta$ is to show that $\mathcal{M}$ can be used to construct a maximally general logic program definition of $\Theta$ . Let $\Theta$ be a predicate $s(X,Y)$ where each of $X,Y$ is a list of characters in $\{0,1\}$ , in other words $\Theta$ represents the language of all bit-strings. Progam $P$ in Table 8 is a maximally general definition of $\Theta$ as a DCG. It is obvious, thus requires no further proof, that $P$ can indeed accept all bit-strings of any length and with characters $1$ and $0$ in any order.
| Identity Chain Chain | $s(x,y)\leftarrow empty(x,y)$ $s(x,y)\leftarrow one(x,z),s(z,y)$ $s(x,y)\leftarrow zero(x,z),s(z,y)$ | $s\rightarrow empty$ $s\rightarrow one$ $s\rightarrow zero$ |
| --- | --- | --- |
Table 8: Maximally-General bit-string grammar. Program $P$ in Table 8 can be constructed by substituting the Second-Order variables in the constrained metarules Chain and Identity, while satisfiying the metarule constraints imposed on Chain and Identity by C-GNF, as follows. - To construct clause $s(x,y)\leftarrow empty(x,y)$ apply a substitution $\vartheta_{1}=\{P/s,Q/empty\}$ to Identity: $P(x,y)\leftarrow Q(x,y)\vartheta=s(x,y)\leftarrow empty(x,y)$ .
- To construct clause $s(x,y)\leftarrow one(x,z),s(z,y)$ apply a substitution $\vartheta_{2}=\{P/s,Q/one,R/s\}$ to Chain: $P(x,y)\leftarrow Q(x,z),R(z,y)\vartheta=s(x,y)\leftarrow one(x,z),s(z,y)$ .
- To construct clause $s(x,y)\leftarrow zero(x,z),s(z,y)$ apply $\vartheta_{3}=\{P/s,Q/zero,R/s\}$ to Chain: $P(x,y)\leftarrow Q(x,z),R(z,y)\vartheta=s(x,y)\leftarrow zero(x,z),s(z,y)$ .
- $\vartheta_{1}=\{P/s,Q/empty\}$ satisfies the constraint $target(P)$ $\wedge\;background(Q)$ $\vee empty(Q)$ imposed on Identity substitutions by C-GNF because $target(s)$ is true and $empty(empty)$ is true.
- $\vartheta_{2}=\{P/s,Q/one,R/s\}$ satisfies the constraint $P\neq Q$ $\wedge\;(target(P)\vee invented(P))$ $\wedge\;not(target(Q))$ $\wedge\;not(empty(Q,R))$ $\wedge\;invented(P,Q)$ $\rightarrow P\neq Q$ imposed on Chain by C-GNF because $s\neq one$ is true, and $target(s)\vee invented(s)$ is true because $target(s)$ is true, and $not(target(one))$ is true and $not(empty(one,s))$ is true, and $invented(s,one)\rightarrow s\neq one$ is true because $invented(s,one)$ is false.
- $\vartheta_{3}=\{P/s,Q/zero,R/s\}$ satisfies the aforementioned constraint imposed on Chain by C-GNF because $s\neq zero$ is true, and $target(s)\vee invented(s)$ is true because $target(s)$ is true, and $not(target(zero))$ is true and $not(empty(zero,s))$ is true, and $invented(s,zero)\rightarrow s\neq zero$ is true because $invented(s,zero)$ is false. â
Clauses in $P$ are instances of only two of the metarules in C-GNF, Chain and Identity but this does not detract from its generality. Indeed, the purpose of the third metarule, Tri-Chain, is to facilitate the construction of productions where the second body literal is a literal of the target predicate (e.g. $s\rightarrow one,s,one$ , a production that might appear in a grammar of palindromic bit-strings). An equivalent production could be constructed by two instances of chain, (e.g. $s\rightarrow one,inv_{1}$ , $inv_{1}\rightarrow s,one$ ) if not for the constraint $not(target(Q))$ on Chain used to avoid left-recursions (e.g. $inv_{1}\rightarrow s,one$ violates that constraint). It is still possible to construct a more complex program with multiple clauses of invented predicates in place of a single instance of Tri-Chain but at a higher computational cost. Thus, the inclusion of Tri-Chain improves efficiency, rather than completeness.
#### Extending C-GNF to more than two characters
It is easy to see that $P$ can further be extended to accept all strings of an arbitrary number $n$ of characters: it suffices to add, for each character, a new non-terminal instance of Chain, and a pre-terminal in the first-order background theory, for each new character, i.e. a total of $2n$ new productions. Therefore C-GNF is a Second Order Normal Form for any target predicate representing a Context-Free Language.
## Appendix C Lindenmayer Second Order Definite Normal Form
| (LS-Base) $P(x,y,y)\leftarrow Q(x,y):$ (LS-Constant) $P(x,y,z)\leftarrow Q(y,u),Q(x,v),P(v,u,z):$ (LS-Variable) $P(x,y,z)\leftarrow Q(y,u),R(x,v),P(v,u,z):$ | $target(P)$ $\wedge\;empty(Q)$ $target(P)$ $\wedge\;background(Q)$ $\wedge\;not(empty(Q))$ $target(P)$ $\wedge\;background(Q)$ $\wedge\;not(target(R))$ |
| --- | --- |
| (Chain) $P(x,y)\leftarrow Q(x,z),R(z,y):$ | $invented(P)$ $\wedge\;background(Q)$ $\wedge\;not(target(R))$ |
| $\wedge\;not(empty(Q,R))$ $\wedge\;invented(P,R)\rightarrow R\geq P$ | |
| (Tri-Chain) $P(x,z)\leftarrow Q(x,y),R(y,u),S(u,z):$ | $P\neq S$ $\wedge\;invented(P)$ $\wedge\;background(Q,R)$ |
| $\wedge\;not(target(S))$ $\wedge\;not(empty(Q,R,S))$ | |
| $\wedge\;invented(P,S)\rightarrow S\geq P$ | |
Table 9: Lindenmayer Normal Form for L-System grammars as DCGs.
Lindenmayer Systems, more commonly known as L-Systems are a grammar formalism used to describe shapes with branching and self-similar structures, like fractals and plants (Prusinkiewicz and Lindenmayer 1996). L-Systems can be executed as generators for strings of symbols interpreted as drawing commands for a Turtle graphics interpreter, e.g. the one in the Python package turtle https://docs.python.org/3/library/turtle.html. That way, the output of an L-system can be visualised as a shape drawn by a âTurtleâ.
Table 9 lists Lindenmayer Normal Form (LNF) the Second Order Normal Form used in experiments with L-System DCGs in Section 5.3. Like L-Systems, LNF is named after Aristide Lindenmayer, who first described L-Systems (Lindenmayer 1968a, b).
L-Systems are slightly different to phrase-structure grammars like CFGs in that each production is applied to an input string simultaneously, transforming the string end-to-end and completing one âgenerationâ at a time (Prusinkiewicz and Lindenmayer 1996). The string produced in each generation is passed to the grammar again in the next generation and again processed by all productions simultaneously.
### Constructing an L-System DCG
| (1a) L-System notation Variables: $f,g$ Constants: $+,-$ | (1b) DCG notation $s([+|Ss])\rightarrow plus,s(Ss)$ $s([-|Ss])\rightarrow minus,s(Ss)$ |
| --- | --- |
| $\delta$ : 90 $\degree$ | $s([f,+,g|Ss])\rightarrow f,s(Ss)$ |
| $f\rightarrow f+g$ | $s([f,-,g|Ss])\rightarrow g,s(Ss)$ |
| $g\rightarrow g-f$ | $s([\;])\rightarrow[\;]$ |
$$
s([+|x],y,z)\leftarrow plus(y,u),s(x,u,z). s([-|x],y,z)\leftarrow minus(y,u),s(x,u,z). s([f,+,g|x],y,z)\leftarrow f(y,u),s(x,u,z). s([f,-,g|x],y,z)\leftarrow g(y,u),s(x,u,z). s([],X,Y)\leftarrow X=Y. \tag{2}
$$
$$
s(x,y,z)\leftarrow plus(y,u),plus(x,v),s(v,u,z) s(x,y,z)\leftarrow minus(y,u),minus(x,v),s(v,u,z) s(x,y,z)\leftarrow f(y,u),inv\_1\_29(x,v),s(v,u,z) inv\_1\_29(x,y)\leftarrow f(x,z),plus(z,u),g(u,y) s(x,y,z)\leftarrow g(y,u),inv\_1\_36(x,v),s(v,u,z) inv\_1\_36(x,y)\leftarrow f(x,z),minus(z,u),g(u,y) s(x,y,y)\leftarrow empty(x,y) \tag{3}
$$
$$
plus([+|x],x) plus\rightarrow[+] \degree minus([-|x],x) minus\rightarrow[-] \degree f([f|x],x) f\rightarrow[f] g([g|x],x) g\rightarrow[g] \tag{5}
$$
Table 10: Dragon Curve L-System used in Section 5. Symbols $+,-$ are not mathematical functions but drawing commands for a Turtle graphis interpreter.
Similar to phrase-structure grammars L-Systems are rewrite systems: their productions determine how characters in a string are replaced with other characters, or strings. L-System strings consist of two kinds of characters, constants and variables, analogous to terminals and non-terminals, respectively. Constants are only replaced by themselves while variables are replaced by arbitrary strings of constants and variables, depending on the L-System.
When considering the possible structure of an L-System DCG, the requirement that all productions must be applied to a string simultaneously suggests a DCG where every production is recursive, save one that expands to the empty string; that way, a string must be consumed in full in order to be accepted by a grammar. This intuition informs the recursive structure of metarules in LNF.
The distinction of symbols in constants (terminals) and variables (nonterminals) suggests at least two metarules are needed: one for constants and one for variables. Those are the metarules LS-Constant and LS-Variable, respectively, listed in Table 9. In both these metarules, the first body literal $Q(y,u)$ is a literal of a DCG pre-terminal (defined in a first-order background theory) that corresponds to the symbol consumed in the input, i.e. either a constant or a variable. If the input symbol is a constant the second body literal $Q(x,v)$ in LS-Constant is a literal of the same pre-terminal as $Q(y,u)$ , thus instances of the LS-Constant metarule can only represent productions that replace a constant with itself. If the input symbol is a variable, the second body literal, $R(x,v)$ in LS-variable, can be a literal of the same or a different predicate than in $Q(y,u)$ . In particular, $R(x,v)$ can be a literal of an invented predicate (but not the target predicate, i.e. $s/3$ itself; LNF only allows tail-recursion). If $R(x,v)$ is a literal of an invented predicate, clauses of this predicate must be instances of either Chain or Tri-Chain.
The combination of Chain and Tri-Chain allows the construction of a sequence of clauses of invented predicates capable of pushing into the output a string of any length. In particular, instances of Chain can add at most two symbols to the output and terminate the recursion, or add one symbol and continue the recursion, while instances of Tri-Chain can add at most three symbols to the output and terminate the recursion, or add two symbols and continue the recursion. In both cases, recursion is continued with a tail-recursive call from the final body literal of an instance of Chain or Tri-Chain, this literal having a new invented predicate symbol that must be higher in alphabetic order than the invented predicate symbol in the head of the same Chain or Tri-Chain instance (constraints $invented(P,R)\rightarrow R\geq P$ , in Chain, and $invented(P,S)\rightarrow S\geq P$ in Tri-Chain). This strong constraint eliminates redundant constructions of invented predicates that differ syntactically only in the names, and the order, of their invented symbols.
### A Dragon System DCG
The effect of imposing LNF on hypotheses constructed by Poker (Procedure Generalise in Algorithm 1) is illustrated in Table 10. In the Table, the top row lists the Dragon Curve L-System in the notation common in L-System literature, in the column marked (1a), and manually encoded as a DCG in the column marked (1b); this DCG was used to generate examples for experiments in the Experiments Section. The second row of the Table, marked (2), lists the plain definite clause notation (without DCG âsyntactic sugarâ) to clarify the pattern of variable sharing between literals.
The third row in Table 10, marked (3), lists a correct hypothesis learned by Poker It should be noted that Poker did not learn this hypothesis all in one go, but piecemeal. Two sub-sets of that hypothesis, each with 4 clauses, were constructed separately, as in Line 2 in Algorithm 1, then combined by taking their union as in Line 19 of the Algorithm. The ability to learn programs in chunks allows Poker to learn larger programs with many invented predicates efficiently.. This hypothesis is (success-set) equivalent to the DCG in column (1b) of the top row of the Table, and therefore also the set of definite clauses in row (2). Syntactically the program in row (3) differs to that in row (2) in that the list of constants in the first argument of each head literal in the program in row (2) is replaced, in row (3), with body literals of the corresponding pre-terminals with suitable variable bindings. Thus, the program in row (3) is, in effect, a flattened version of the program in row (2) of the Table, in the sense of flattening described in (Rouveirol 1994) This kind of flattening is used in the ILP literature to remove function symbols, such as the Prolog list-constructor symbol $|$ , from literals of learned hypotheses, so that the language of hypotheses is a function-free First Order language. The goal of this transformation is improved learning efficiency. In MIL a function-free hypothesis language is forced by the nature of Metarules which have no variables quantified over non-constant function symbols.. This flattened program is listed with invented predicate symbols as constructed by Poker, to illustrate the use of predicate invention when learning an L-System DCG. In particular, predicate invention makes it possible to use metarules with a constant number of literals to learn grammars with a variable number of literals, albeit in âfoldedâ form. Indeed, the program in Row (3) is a folded form of the program in row (4).
The fourth row in Table 10, marked (4), lists the hypothesis learned by Poker, as in row (3), but this time unfolded automatically to remove invented predicates. In this form it is easier to observe the assembly of output strings by the sequence of invented predicate clauses in the previous row. Preterminals, representing constants used in the Dragon Curve L-System, are listed in the bottom-most row of the Table, along with their Turtle language interpretation as drawing commands.
### Completeness of LNF for L-Systems
| 1 2 3 | $s\rightarrow c_{1},c_{1},s$ $s\rightarrow c_{2},c_{2},s$ $s\rightarrow v_{1},c_{1},s$ | $s(x,y,z)\leftarrow c_{1}(y,u),c_{1}(x,v),s(v,u,z)$ $s(x,y,z)\leftarrow c_{2}(y,u),c_{2}(x,v),s(v,u,z)$ $s(x,y,z)\leftarrow v_{1}(y,u),c_{1}(x,v),s(v,u,z)$ |
| --- | --- | --- |
| 4 | $s\rightarrow v_{1},c_{2},s$ | $s(x,y,z)\leftarrow v_{1}(y,u),c_{2}(x,v),s(v,u,z)$ |
| 5 | $s\rightarrow v_{1},v_{1},s$ | $s(x,y,z)\leftarrow v_{1}(y,u),v_{1}(x,v),s(v,u,z)$ |
| 6 | $s\rightarrow v_{1},v_{2},s$ | $s(x,y,z)\leftarrow v_{1}(y,u),v_{2}(x,v),s(v,u,z)$ |
| 7 | $s\rightarrow v_{2},v_{1},s$ | $s(x,y,z)\leftarrow v_{2}(y,u),v_{1}(x,v),s(v,u,z)$ |
| 8 | $s\rightarrow v_{2},v_{2},s$ | $s(x,y,z)\leftarrow v_{2}(y,u),v_{2}(x,v),s(v,u,z)$ |
| 9 | $s\rightarrow v_{2},c_{1},s$ | $s(x,y,z)\leftarrow v_{2}(y,u),c_{1}(x,v),s(v,u,z)$ |
| 10 | $s\rightarrow v_{2},c_{2},s$ | $s(x,y,z)\leftarrow v_{2}(y,u),c_{2}(x,v),s(v,u,z)$ |
| 11 | $s\rightarrow empty$ | $s(x,y,y)\leftarrow empty(x,y)$ |
| Constants: | DCG $c_{1}\rightarrow[c_{1}]$ $c_{2}\rightarrow[c_{2}]$ | Definite Clauses $c_{1}([c_{1}|x],x)$ . $c_{2}([c_{2}|x],x)$ . |
| --- | --- | --- |
| Variables: | $v_{1}\rightarrow[v_{1}]$ | $v_{1}([v_{1}|x],x)$ . |
| $v_{2}\rightarrow[v_{2}]$ | $v_{2}([v_{2}|x],x)$ . | |
Table 11: Maximally general grammar for L-Systems with two constant and two variable symbols constructed according to LNF.
We now give a proof that LNF is a Second Order Definite Normal Form for L-Systems. As with C-GNF we give a proof by construction, of a maximally general L-System DCG listed in Table 11. Our DCG assumes an L-System with only two constants, and two variable symbols, $c_{1},c_{2}$ and $v_{1},v_{2}$ , respectively. These can be trivially interpreted as any desired characters, such as $+,-,f,g$ in the Dragon Curve L-System in Table 10.
**Proposition 2**
*LNF is a Second Order Definite Normal Form for all Definite Clause Grammars of L-System languages with two variables and two constants.*
As for C-GNF we give a constructive proof.
* Proof*
The grammar in Table 11 includes one instance of the LS-Constant metarule for each of the two constants, $c_{1},c_{2}$ , consuming from the input, and then pushing into the output, the corresponding constant. It includes four instances of the LS-Variable metarule for each of the two variables, $v_{1},v_{2}$ , each of which consumes the corresponding variable, and outputs one other symbol, each of the two constants of variables. An instance of LS-Base completes the DCG. It should be obvious, and so should need no further poof, that the DCG in Table 11 accepts all strings of the symbols in $\{c_{1},c_{2},v_{1},v_{2}\}*$ of arbitrary length and arbitrary order, i.e. this grammar is a maximally general grammar for L-Systems with two constants and two variables (suitably substituted for $c_{1},c_{2}$ and $v_{1},v_{2}$ , respectively). The DCG in Table 11 can be constructed by substituting the Second-Order variables in the constrained metarules LS-Base, LS-Constant, LS-Variable, Chain and Tri-Chain, while satisfying the metarule constraints imposed on the aforementioned metarules by LNF, below. In the following, for brevity, we treat the construction of similar clauses together by means of variable indices in place of the indices 1, 2 in symbols $c_{1},c_{2},v_{1},v_{2}$ . - To construct clauses 1, 2 in Table 11 (i.e. clauses corresponding to productions consuming constant symbols) apply a substitution $\vartheta_{i}=\{P/s,Q/c_{i}\}$ to LS-Constant, $\forall i\in[1,2]$ . $P(x,y,z)$ $\leftarrow$ $Q(y,u)$ , $Q(x,v)$ , $P(v,u,z)\vartheta_{i}$ = $s(x,y,z)$ $\leftarrow$ $c_{i}(y,u)$ , $c_{i}(x,v)$ , $s(v,u,z)$ , where $i$ is 1 for clause 1, and 2 for clause 2.
- To construct clauses 3-4 and 9-10 in Table 11 (i.e. clauses corresponding to productions consuming variable symbols and replacing them with constant symbols) apply a substitution $\varphi_{j}^{k}=\{P/s$ , $Q/v_{j}$ , $R/c_{k}\}$ to LS-Variable, $\forall j,k\in[1,2]$ . $P(x,y,z)$ $\leftarrow$ $Q(y,u)$ , $R(x,v)$ , $P(v,u,z)\varphi_{j}^{k}$ = $s(x,y,z)$ $\leftarrow$ $v_{j}(y,u)$ , $c_{k}(x,v)$ , $s(v,u,z)$ .
- To construct clauses 5-8 in Table 11 (i.e. clauses corresponding to productions consuming variable symbols and replacing them with variable symbols) apply a substitution $\sigma_{j}^{k}=\{P/s$ , $Q/v_{j}$ , $R/v_{k}\}$ to LS-Variable, $\forall$ $j,k$ $\in[1,2]$ . $P(x,y,z)$ $\leftarrow$ $Q(y,u)$ , $R(x,v)$ , $P(v,u,z)\sigma_{j}^{k}$ = $s(x,y,z)$ $\leftarrow$ $v_{j}(y,u)$ , $v_{k}(x,v)$ , $s(v,u,z)$ .
- To construct clause 11 in Table 11 apply a substitution $\omega=\{P/s,Q/empty\}$ to LS-Base. $P(x,y,y)\leftarrow Q(x,y)\omega=s(x,y,y)\leftarrow empty(x,y)$ .
- Substitution $\vartheta_{i}=\{P/s,Q/c_{i}\}$ satisfies the constraint $target(P)$ $\wedge$ $background(Q)$ $\wedge$ $not(empty(Q))$ imposed on LS-Constant by LNF because $target(s)$ is true and $background(c_{i})$ $\wedge$ $not(empty(c_{i}))$ is true for $c_{i}$ $\in$ $\{c_{1},c_{2}\}$ .
- Substitution $\varphi_{j}^{k}=\{P/s$ , $Q/v_{j}$ , $R/c_{k}\}$ satisfies the constraint $target(P)$ $\wedge$ $background(Q)$ $\wedge$ $not(target(R))$ imposed on LS-Variable by LNF because $target(s)$ is true and $background(v_{j})$ is true for $v_{j}$ $\in$ $\{v_{1},v_{2}\}$ and $not(target(c_{k}))$ is true for $c_{k}$ $\in$ $\{c_{1},c_{2}\}$ .
- Substitution $\sigma_{j}^{k}=\{P/s$ , $Q/v_{j}$ , $R/v_{k}\}$ satisfies the constraint $target(P)$ $\wedge$ $background(Q)$ $\wedge$ $not(target(R))$ imposed on LS-Variable by LNF because $target(s)$ is true and $background(v_{j})$ is true for $v_{j}$ $\in$ $\{v_{1},v_{2}\}$ and $not(target(v_{k}))$ is true for $v_{k}$ $\in$ $\{v_{1},v_{2}\}$ .
- Substitution $\omega=\{P/s,Q/empty\}$ satisfies the constraint $target(P)$ $\wedge$ $empty(Q)$ imposed on LS-Base by LNF because $target(s)$ is true and $empty(empty)$ is true. â
As with C-GNF, the non-inclusion of instances of Chain and Tri-Chain in the grammar listed in Table 11 does not detract from its generality. The purpose of Chain and Tri-Chain is to allow learning DCG productions with variable numbers of body literals in âfoldedâ form, as discussed above.
#### Extending LNF to L-System languages with more than two variables and constants
We have shown that LNF is a Second Order Definite Normal Form for DCGs of L-System languages with two constants and two variables. The same result can be extended to L-Systems with any number of constants or variables by adding, or removing, one instance of LS-Constant for each added or removed constant symbol, and $k$ instances of LS-Variable for each added or removed variable symbol, where $k$ is the cardinality of the set of symbols in the represented L-System.
## Appendix D Experimental setup
All experiments reported in the Experiments section were carried out on a server with two AMD EPYC 7551 CPUs with a clock speed of 2.0GHz, 32 cores, and 256GB of RAM, running Linux Fedora 41.
The values of Poker and Louise hyperparameters used in the experiments reported in the Experiments section can be found in the joint Code Appendix, where experiment scripts are defined as Prolog predicates that set hyperparameter values and generate training and testing exampes.
Hyperparameters were selected in preliminary experiments carried out on a GPD Win Max 2 2024 mini laptop with an AMD Ryzen 7 8840U processor with 16 cores clocked at 3.3 GHz and with 64 GB of RAM, running Linux Fedora 39. These preliminary experiments were executed over 1 to 10 randomly sampled sets of training and testing examples in each experiment (compared with 100 in reported experiments).