## Stacked Bar Chart: Number of Solved Levels by Agent Type and Game ### Overview The image is a stacked bar chart comparing the number of solved levels across different game types (private and public) and agent configurations. The y-axis represents the "Number of Solved Levels," ranging from -10 to 15. The x-axis represents different agent configurations: "LLM + DSL," "Random Agent," "+ Frame Segmentation," "+ Prioritize New Actions," and "+ Graph Exploration." The chart uses stacked bars to show the contribution of each game to the total solved levels for each agent configuration. The horizontal line at y=0 separates the public and private games. ### Components/Axes * **Y-axis:** "Number of Solved Levels," ranging from -10 to 15, with tick marks at intervals of 5. * **X-axis:** Agent configurations: * "LLM + DSL" * "Random Agent" * "+ Frame Segmentation" * "+ Prioritize New Actions" * "+ Graph Exploration" * **Legend (Top-Left):** * **Private games:** * as66 (light green) * lp85 (light tan) * sp80 (light blue) * Unknown (gray) * **Public games:** * ft09 (dark green) * ls20 (orange) * vc33 (pink) * **Horizontal Line:** A thick black line at y=0 separates the public and private games. * **Dashed Horizontal Lines:** Faint dashed lines at y=5, y=10, and y=15. ### Detailed Analysis **LLM + DSL:** * Unknown: 5 **Random Agent:** * Private Games: * as66: 5 * lp85: 1 * sp80: 1 * Public Games: * ft09: 0 * ls20: 1 * vc33: 1 * Total: 6 **+ Frame Segmentation:** * Private Games: * as66: 5 * lp85: 1 * sp80: 1 * Public Games: * ft09: 0 * ls20: 2 * vc33: 5 * Total: 7 **+ Prioritize New Actions:** * Private Games: * as66: 4 * lp85: 1 * sp80: 1 * Public Games: * ft09: 0 * ls20: 2 * vc33: 5 * Total: 6 **+ Graph Exploration:** * Private Games: * as66: 7 * lp85: 2 * sp80: 1 * Public Games: * ft09: 0 * ls20: 2 * vc33: 5 * Total: 10 ### Key Observations * The "LLM + DSL" agent only solves "Unknown" private games. * The "+ Graph Exploration" agent configuration achieves the highest number of solved levels (10). * The "ft09" public game is never solved by any agent configuration. * The "vc33" public game consistently contributes a significant portion to the total solved levels across all agent configurations. * The number of solved levels for private games generally increases as the agent configuration becomes more sophisticated. ### Interpretation The chart demonstrates the impact of different agent configurations on the number of solved levels in both private and public games. The "LLM + DSL" agent appears to be a baseline, only solving "Unknown" private games. Adding "Frame Segmentation," "Prioritize New Actions," and "Graph Exploration" progressively improves the agent's ability to solve levels, particularly in private games. The "Graph Exploration" agent configuration stands out as the most effective, achieving the highest number of solved levels overall. The consistent performance of the "vc33" public game suggests it may be an easier or more suitable game for these agents compared to "ft09," which is never solved. The negative values are not possible, and are likely an artifact of the stacking of the bars.

## Stacked Bar Chart: Number of Solved Levels vs. Algorithm ### Overview This is a stacked bar chart comparing the number of solved levels for different algorithms, categorized by game type (Private vs. Public). The x-axis represents the algorithms, and the y-axis represents the number of solved levels. Each bar is segmented to show the contribution of different game instances within each algorithm. ### Components/Axes * **X-axis:** Algorithms: LLM + DSL, Random Agent, Random Agent + Frame Segmentation, Prioritize New Actions, Graph Exploration. * **Y-axis:** Number of Solved Levels (Scale from 0 to 15). * **Legend (Top-Left):** * Private games (Green) * as66 (Light Green) * lp85 (Tan) * sp80 (Dark Gray) * Unknown (Gray) * Public games (Pink/Red) * ft09 (Dark Red) * ls20 (Pink) * vc33 (Red) * A horizontal line at y=0 separates the Private and Public game data. * A question mark (?) is placed at the bottom of the "Unknown" section of the first bar. ### Detailed Analysis The chart consists of five stacked bars, one for each algorithm. **1. LLM + DSL:** * Total solved levels: 5 * All levels are from "Unknown" private games (Gray). **2. Random Agent:** * Total solved levels: 6 * as66 (Light Green): 5 levels * lp85 (Tan): 1 level * ft09 (Dark Red): 3 levels * ls20 (Pink): 2 levels **3. Random Agent + Frame Segmentation:** * Total solved levels: 7 * as66 (Light Green): 5 levels * lp85 (Tan): 1 level * ft09 (Dark Red): 1 level * ls20 (Pink): 5 levels * vc33 (Red): 8 levels **4. Prioritize New Actions:** * Total solved levels: 6 * as66 (Light Green): 4 levels * lp85 (Tan): 2 levels * ft09 (Dark Red): 2 levels * ls20 (Pink): 5 levels * vc33 (Red): 8 levels **5. Graph Exploration:** * Total solved levels: 10 * as66 (Light Green): 7 levels * lp85 (Tan): 2 levels * sp80 (Dark Gray): 1 level * ft09 (Dark Red): 2 levels * ls20 (Pink): 5 levels * vc33 (Red): 9 levels ### Key Observations * The "Graph Exploration" algorithm solves the most levels overall (10). * The "LLM + DSL" algorithm only solves levels from "Unknown" private games. * The contribution of private games (as66, lp85, sp80) is generally higher in the "Graph Exploration" and "Random Agent" algorithms. * Public games (ft09, ls20, vc33) contribute significantly to the solved levels in the "Random Agent + Frame Segmentation", "Prioritize New Actions", and "Graph Exploration" algorithms. * The "vc33" public game consistently contributes a large number of solved levels across the latter three algorithms. ### Interpretation The chart demonstrates the effectiveness of different algorithms in solving game levels, distinguishing between private and public game types. The "Graph Exploration" algorithm appears to be the most successful overall. The fact that "LLM + DSL" only solves "Unknown" levels suggests it may be limited to a specific subset of games or requires further refinement. The increasing number of solved levels as algorithms become more complex (from "Random Agent" to "Graph Exploration") indicates that incorporating techniques like frame segmentation, prioritizing new actions, and graph exploration improves performance. The consistent high contribution of the "vc33" public game suggests it may be a relatively easier game to solve or that the algorithms are particularly well-suited to its characteristics. The question mark next to the "Unknown" levels suggests a lack of information about the specific game instances solved by the "LLM + DSL" algorithm. This could be a data collection issue or a deliberate obfuscation of game details.

## Stacked Bar Chart: Game Level Solving Performance by Method ### Overview This image displays a stacked bar chart comparing the performance of five different methods or agents in solving levels from two categories of games: "Private games" and "Public games." The chart uses a dual-direction stacked bar format, with private game results extending upward from the zero line and public game results extending downward. The primary metric is the "Number of Solved Levels." ### Components/Axes * **Chart Type:** Stacked Bar Chart (Bidirectional/Diverging). * **Y-Axis:** Labeled "Number of Solved Levels." The scale runs from 0 at the center to 15 at the top (for private games) and from 0 to 10 at the bottom (for public games). Major gridlines are present at intervals of 5. * **X-Axis:** Lists five methods/agents: 1. LLM + DSL 2. Random Agent 3. + Frame Segmentation 4. + Prioritize New Actions 5. + Graph Exploration * **Legends:** * **Private games (Top-Left):** A box legend with four categories and associated colors: * `as66` (Light Green) * `lp85` (Beige/Tan) * `sp80` (Teal/Cyan) * `Unknown` (Gray) * **Public games (Bottom-Left):** A box legend with three categories and associated colors: * `ft09` (Dark Teal/Green) * `ls20` (Orange/Salmon) * `vc33` (Pink/Magenta) * **Data Labels:** Each colored segment within the bars contains a number indicating the count for that specific category. The total number of solved levels for private games is displayed above each bar, and the total for public games is displayed below each bar. ### Detailed Analysis Performance is analyzed per method, from left to right. 1. **LLM + DSL** * **Private Games (Total: 5):** The entire bar is a single gray segment labeled `5`. This corresponds to the `Unknown` category in the legend. No other private game categories are present. * **Public Games (Total: ?):** No bar extends downward. A question mark `?` is present below the zero line, indicating either zero solved public levels or missing data for this method. 2. **Random Agent** * **Private Games (Total: 6):** The bar is stacked from bottom to top: a gray segment (`Unknown`, value `1`), a light green segment (`as66`, value `5`), a beige segment (`lp85`, value `1`), and a teal segment (`sp80`, value `1`). *Note: The sum of segments (1+5+1+1=8) does not match the labeled total of 6. This is a visual/data inconsistency in the source chart.* * **Public Games (Total: 3):** The bar extends downward. From top (zero line) to bottom: a dark teal segment (`ft09`, value `1`), an orange segment (`ls20`, value `1`), and a pink segment (`vc33`, value `1`). 3. **+ Frame Segmentation** * **Private Games (Total: 7):** Stacked from bottom to top: gray (`Unknown`, `1`), light green (`as66`, `5`), beige (`lp85`, `1`), teal (`sp80`, `1`). Sum of segments (1+5+1+1=8) again does not match the labeled total of 7. * **Public Games (Total: 8):** Stacked from top to bottom: dark teal (`ft09`, `2`), orange (`ls20`, `1`), pink (`vc33`, `5`). 4. **+ Prioritize New Actions** * **Private Games (Total: 6):** Stacked from bottom to top: gray (`Unknown`, `1`), light green (`as66`, `4`), beige (`lp85`, `1`), teal (`sp80`, `1`). Sum of segments (1+4+1+1=7) does not match the labeled total of 6. * **Public Games (Total: 8):** Stacked from top to bottom: dark teal (`ft09`, `2`), orange (`ls20`, `1`), pink (`vc33`, `5`). 5. **+ Graph Exploration** * **Private Games (Total: 10):** Stacked from bottom to top: gray (`Unknown`, `2`), light green (`as66`, `7`), beige (`lp85`, `2`), teal (`sp80`, `1`). Sum of segments (2+7+2+1=12) does not match the labeled total of 10. * **Public Games (Total: 9):** Stacked from top to bottom: dark teal (`ft09`, `2`), orange (`ls20`, `2`), pink (`vc33`, `5`). ### Key Observations * **Performance Trend:** There is a clear upward trend in the total number of solved private game levels as methods become more complex, peaking at 10 for "Graph Exploration." Public game performance also generally improves, from 3 to 9. * **Dominant Category:** The `as66` category (light green) consistently makes up the largest portion of solved private games for all methods except "LLM + DSL." * **Public Game Leader:** The `vc33` category (pink) is the dominant component of solved public games for the last three methods, consistently contributing 5 solved levels. * **Data Inconsistency:** For four of the five bars, the sum of the individual segment values for private games does not equal the total labeled above the bar. This suggests either a chart error, overlapping categories, or that the totals represent unique games solved while segments may count solutions across multiple categories. * **Unknown Category:** The `Unknown` category (gray) appears in all private game bars, indicating some solved levels could not be classified into the `as66`, `lp85`, or `sp80` categories. ### Interpretation The chart demonstrates the progressive effectiveness of more sophisticated agent architectures on benchmark game-solving tasks. The "LLM + DSL" baseline shows limited capability, solving only private games and failing on public ones. Each incremental addition—Frame Segmentation, Prioritizing New Actions, and finally Graph Exploration—correlates with increased performance. The data suggests that **Graph Exploration** is the most effective method shown, achieving the highest scores in both game categories (10 private, 9 public). The consistent performance of the `as66` and `vc33` categories implies these game types or levels are particularly well-suited to the capabilities of the advanced agents, or perhaps are more prevalent in the test suite. The persistent "Unknown" category and the numerical inconsistencies between segment sums and totals are critical anomalies. They point to potential issues in the evaluation methodology, classification system, or data visualization itself. A technical reviewer would need to investigate whether the totals represent unique solved levels (where a level might belong to multiple categories) or if there is a simple error in the chart's construction. The question mark for the "LLM + DSL" public game score further indicates incomplete or inconclusive results for that baseline.

## Bar Chart: Solved Levels by Game Type and Strategy ### Overview The chart compares the number of solved levels across different game types (Private and Public) using various AI strategies. Private games include as66, lp85, sp80, and Unknown categories, while Public games feature ft09, ls20, and vc33. Strategies tested include LLM + DSL, Random Agent, Frame Segmentation, Prioritize New Actions, and Graph Exploration. ### Components/Axes - **X-axis (Strategies)**: - LLM + DSL - Random Agent - + Frame Segmentation - + Prioritize New Actions - + Graph Exploration - **Y-axis (Number of Solved Levels)**: Ranges from -10 to 15, with 0 as the baseline. - **Legends**: - **Private Games** (Top-left): - as66 (Green) - lp85 (Light Brown) - sp80 (Teal) - Unknown (Gray) - **Public Games** (Bottom-left): - ft09 (Teal) - ls20 (Orange) - vc33 (Pink) ### Detailed Analysis #### Private Games - **LLM + DSL**: - as66: 5 - lp85: 0 - sp80: 0 - Unknown: 5 - **Random Agent**: - as66: 5 - lp85: 1 - sp80: 1 - Unknown: 1 - **+ Frame Segmentation**: - as66: 5 - lp85: 1 - sp80: 1 - Unknown: 1 - **+ Prioritize New Actions**: - as66: 4 - lp85: 1 - sp80: 1 - Unknown: 1 - **+ Graph Exploration**: - as66: 7 - lp85: 2 - sp80: 1 - Unknown: 0 #### Public Games - **LLM + DSL**: - ft09: 0 - ls20: 0 - vc33: 3 - **Random Agent**: - ft09: 2 - ls20: 1 - vc33: 5 - **+ Frame Segmentation**: - ft09: 2 - ls20: 1 - vc33: 5 - **+ Prioritize New Actions**: - ft09: 2 - ls20: 1 - vc33: 5 - **+ Graph Exploration**: - ft09: 2 - ls20: 2 - vc33: 5 ### Key Observations 1. **Private Games Dominance**: - as66 consistently solves the most levels across all strategies (5–7), with Graph Exploration achieving the highest (7). - Unknown levels are most frequent in LLM + DSL (5) and decline with advanced strategies. 2. **Public Games Trends**: - vc33 solves the most levels (5–9), with Graph Exploration achieving the highest (9). - ft09 and ls20 show minimal progress, with ls20 never exceeding 2 solved levels. 3. **Strategy Impact**: - Frame Segmentation and Prioritize New Actions show marginal improvements over Random Agent. - Graph Exploration significantly boosts performance for both game types. ### Interpretation The data suggests that **Graph Exploration** is the most effective strategy for solving levels, particularly for Private games (as66) and Public games (vc33). The "Unknown" category in Private games indicates unresolved challenges, which decrease with advanced strategies. Public games show a clear preference for vc33, while Private games rely heavily on as66. The lack of progress for ls20 and ft09 in Public games highlights potential limitations in these game types or strategies. The baseline (0) acts as a reference, emphasizing that negative values are not represented, suggesting all strategies achieve at least partial success.