Inductive Logic Programming for Heuristic Search

Pathfinding problems are found through computing, chemistry, mathematics, and robotics. Solving pathfinding problems is typically achieved through heuristic search, which is guided by a heuristic function that can be learned using deep neural networks. However, since deep neural networks are typically not explainable, the extraction of new knowledge from these learned heuristic functions is cumbersome. On the other hand, to the best of our knowledge, it has yet to be shown how heuristic functions represented as logic programs, which have been shown to be explainable, can be learned. In this work, we present an algorithm to learn heuristic functions represented as logic programs using dynamic programming and inductive logic programming. Furthermore, we build on dynamic programming concepts to improve the learned logic programs by reusing predicates learned for solving simpler pathfinding problem instances to solve more complex instances. We use the 8-puzzle to demonstrate the effectiveness of our algorithm. Code — https: //github. com/Rojina99/HeurSearchILP

Authors

• Rojina Panta
• Vedant Khandelwal AI Institute, University of South Carolina
• Celeste Veronese
• Amit Sheth AI Institute, University of South Carolina
• Daniele Meli University of Verona
• Forest Agostinelli

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