Enterprise AI Analysis
ASP-Bench: From Natural Language to Logic Programs
This paper introduces ASP-Bench, a comprehensive benchmark for evaluating systems that translate natural language specifications into Answer Set Programs (ASPs). Comprising 128 problem instances, it provides systematic coverage of ASP features and includes semantic validators. The benchmark demonstrates the effectiveness of feedback-driven iterative refinement with solver feedback in agentic systems, achieving full saturation on both easy and hard problems. It also offers insights into modeling hardness based on reasoning aspects and agent activity patterns, proving to be a valuable resource for neurosymbolic AI research.
Executive Impact: Key Achievements
Understanding the core results that drive advancements in neurosymbolic AI and automated program synthesis.
128
Total Problem Instances
100%
Saturation Achieved
7.7
Avg. Python Exec Calls (Hard)
7
Reasoning Aspects Covered
Deep Analysis & Enterprise Applications
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Benchmark Design
Agentic Approach
Hardness Analysis
Strategic Debugging
ASP-Bench provides a robust framework with 128 problem instances, including easy and hard variants, to systematically cover core ASP constructs. Each problem features a reference validator for semantic verification, ensuring reliable evaluation beyond mere syntactic matching. This design addresses limitations of prior benchmarks by providing a diverse and well-characterized set of problems for NL-to-ASP translation.
The study utilizes an ASP-Agent based on the ReAct framework, employing iterative refinement with solver feedback. This autonomous LLM agent dynamically adapts its actions based on clingo error messages and failed test cases, iteratively debugging and improving ASP programs. This feedback-driven approach proved highly effective, achieving full saturation on all benchmark problems.
Problems are characterized by seven reasoning aspects: optimization, temporal reasoning, default logic, resource allocation, recursion, spatial reasoning, and quantitative complexity. Surprisingly, problem hardness (measured by python_exec calls) shows no significant correlation with the number of reasoning aspects. Instead, difficulty often stems from the depth and complexity within specific domains, rather than the breadth across multiple reasoning patterns.
The agent's strategic flexibility is highlighted by its debugging methodology. When initial attempts fail, it can pivot to diagnostic strategies like simplifying problems to smaller instances for verification, then scaling back up, and refining models with incremental time horizons. This iterative, feedback-driven approach contrasts with simple retries, leading to robust problem-solving.
100%
Problem Saturation Rate Achieved
The ASP-Agent, utilizing iterative refinement, achieved full saturation on all 128 problems (both easy and hard variants) within ASP-Bench, demonstrating the robustness of feedback-driven program synthesis.
Enterprise Process Flow
Problem Analysis
→
Initial Model Construction
→
Rule Addition & Refinement
→
Logic Verification
→
Solver Execution
→
Error Correction & Iteration
→
Solution Formatting
| Feature | ipython-mcp (Agentic Python) | mcp-solver-asp (Declarative) |
|---|---|---|
| Approach | Full Python expressiveness, procedural control | Item-by-item model construction, declarative editing |
| Tool Calls (Avg.) | 16.0 | 47.2 |
| Time (Avg.) | 216s | 297s |
| Accuracy | 100% (30/30) | 90% (27/30) |
| Debugging | Leverages Python's full debugging capabilities | Finer-grained undo/redo, explicit state inspection |
| Flexibility | High, constructs ASP as strings, loops for rules | Limited procedural abstractions, enforced structure |
Case Study: Tower of Hanoi (Problem 26)
Problem 26, a 4-disk, 4-peg Tower of Hanoi variant with a 'Pilgrim's Journey' constraint, revealed diverse agent strategies. The shortest run (6 calls) showed expert-like behavior, with immediate recognition of temporal planning and a correct model. The longest run (20 calls) demonstrated sophisticated debugging: simplifying the problem to 1-3 disks for verification, then scaling back and optimizing the model with incremental time horizons. This highlights the agent's resilience and adaptive problem-solving capabilities.
Highlight: The agent's ability to simplify, verify, and then scale back up demonstrates adaptive problem-solving.
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