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Chapter
Logic-Based Explainable and Incremental Machine Learning
Mainstream machine learning methods lack interpretability, explainability, incrementality, and data-economy. We propose using logic programming to rectify these problems. We discuss the FOLD family of rule-based ...
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Chapter
Proof-Theoretic Foundations of Normal Logic Programs
There are several semantics in logic programming for negation as failure. These semantics can be realized with a combination of induction and coinduction, and this realization can be used to develop a goal-dir...
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Chapter
Prolog: Past, Present, and Future
We argue that various extensions proposed for Prolog—tabling, constraints, parallelism, coroutining, etc.—must be integrated seamlessly in a single system. We also discuss how goal-directed predicate answer se...
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Chapter and Conference Paper
Jury-Trial Story Construction and Analysis Using Goal-Directed Answer Set Programming
Answer Set Programming (ASP) is a well known paradigm for knowledge representation and for automating commonsense reasoning. Query-driven implementations of Predicate ASP, e.g., the s(CASP) system, permit top-...
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Chapter and Conference Paper
Modeling and Verification of Real-Time Systems with the Event Calculus and s(CASP)
Modeling a cyber-physical system’s requirement specifications makes it possible to verify its properties w.r.t. the expected behavior. Standard modeling approaches based on automata theory model these systems ...
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Chapter and Conference Paper
Synthesizing Imperative Code from Answer Set Programming Specifications
We consider the problem of obtaining an implementation of an algorithm from its specification. We assume that these specifications are written in answer set programming (ASP). ASP is an ideal formalism for wr...