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Abstract
Various powerful extensions of Logic Programming (LP) have been proposed in the last few decades. Some of the highly successful extensions include coinductive logic programming, tabled logic programming, constraint logic programming and answer set programming. Although all of these extensions have been developed by extending standard logic programming (Prolog) systems, each one has been developed in isolation. While each extension has resulted in very powerful applications in reasoning, considerably more powerful applications will become possible if all these extensions are combined into a single system. Realizing multiple or all extensions in a single framework is quite challenging because the techniques devised so far for implementing these extensions are quite complex.
In this dissertation, we develop simple, elegant and easy techniques for implementing coinductive logic programming and answer set programming atop an existing prolog engine. We present our implementations of these extensions of LP atop YAP prolog. With these implementations in place, we have realized an integrated framework that combines coinductive logic programming, tabled logic programming, constraint logic programming and answer set programming into a single logic programming system.
We present the practice of co-logic programming (co-LP for brevity), a paradigm that combines both inductive and coinductive logic programming. Co-LP is a natural generalization of logic programming and coinductive logic programming, which in turn generalizes other extensions of logic programming, such as infinite trees, lazy predicates, and concurrent communicating predicates. Co-LP has applications to rational trees, verifying infinitary properties, lazy evaluation, concurrent LP, model checking, bisimilarity proofs, etc.
All approaches to executing answer set programs have been primarily bottom-up (non goal-directed), which prevents any possibility of its merging with other extensions as their implementations are primarily top-down (goal-directed). We present a goal-directed top-down execution method for executing answer-set programs by extending co-LP.
The integrated system presented in this dissertation is capable of handling constraints, tabling, coinduction and non-monotonic reasoning, all at the same time. Applications to model checking and knowledge representation, presented in this dissertation, justify the power and elegance of combining these powerful extensions in a single system.
