2018/02/18 by Farhad Shakerin, Gopal Gupta, Shakerin, Farhad +1 · 1 citation
Computer Science · #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Logic, Reasoning, and Knowledge #Logic, programming, and type systems #Multi-Agent Systems and Negotiation #Natural Language Processing Techniques
paper · pdf · doi:10.48550/arxiv.1802.06462
openalex publication_date 2018/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Significant research has been conducted in recent years to extend Inductive\nLogic Programming (ILP) methods to induce Answer Set Programs (ASP). These\nmethods perform an exhaustive search for the correct hypothesis by encoding an\nILP problem instance as an ASP program. Exhaustive search, however, results in\nloss of scalability. In addition, the language bias employed in these methods\nis overly restrictive too. In this paper we extend our previous work on\nlearning stratified answer set programs that have a single stable model to\nlearning arbitrary (i.e., non-stratified) ones with multiple stable models. Our\nextended algorithm is a greedy FOIL-like algorithm, capable of inducing\nnon-monotonic logic programs, examples of which includes programs for\ncombinatorial problems such as graph-coloring and N-queens. To the best of our\nknowledge, this is the first heuristic-based ILP algorithm to induce answer set\nprograms with multiple stable models.\n