Evaluating Clingo ASP Solver
Supervisor: Prof. Lesperance
Type: Academic Project (Artificial Intelligence)
Overview
This project, under the supervision of Prof. Lesperance, focused on evaluating the Clingo Answer Set Programming (ASP) Solver. The Clingo ASP system is designed for grounding and solving logic-based problems, widely applicable in combinatorial problem-solving across various computer science domains.
Methodology
The methodology of the project was observational, involving:
- Software Utilization: Using the online version of Clingo available on the Potassco website, with features ranging from pre-built to custom problem solving.
- Problem Selection: A variety of problems with varying complexity were selected for testing Clingo’s features.
- Data Collection: Analyzing the types of solutions provided by Clingo, and assessing the resources required by the software in solving them.
Evaluation
The evaluation focused on:
- Effectiveness and Speed: Assessing Clingo’s performance in solving problems of different complexities quickly and efficiently.
- Usability: Examining the ease of learning and using Clingo’s syntax compared to other modeling languages.
- Comparison with SModels: Contrasting Clingo’s performance with another ASP system, SModels, to identify strengths and weaknesses.
Key Findings
- Clingo demonstrated high efficiency in solving a range of problems, from simple to complex, with minimal time requirements.
- It was observed that Clingo outperformed SModels in terms of speed, especially in handling larger problem sets.
- The study highlighted the robustness of Clingo in dealing with large and complex datasets, albeit some limitations in specific scenarios.
Personal Contributions
- Introduction and overview development
- Methodology design and implementation
- Software testing and analysis
- Problem selection for testing
- Usability assessment and comparative analysis
- Project presentation and reporting
Conclusion
The project concluded that Clingo is a highly capable tool for solving logical problems, offering quick and efficient solutions. It provides detailed statistical data, useful for various purposes such as algorithm analysis and solution optimization.
Project Report
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Project Slides
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