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Conflict-Driven inductive logic programming

THEORY AND PRACTICE OF logic programming 2023年第2期23卷 387-414页

作者： Law, Mark ILASP Ltd Grantham England

The goal of inductive logic programming (ILP) is to learn a program that explains a set of examples. Until recently, most research on ILP targeted learning Prolog programs. The ILASP system instead learns answer set programs (ASP). Learning such expressive programs widens the applicability of ILP considerably;for example, enabling preference learning, learning common-sense knowledge, including defaults and exceptions, and learning non-deterministic theories. Early versions of ILASP can be considered meta-level ILP approaches, which encode a learning task as a logic program and delegate the search to an ASP solver. More recently, ILASP has shifted towards a new method, inspired by conflict-driven SAT and ASP solvers. The fundamental idea of the approach, called Conflict-driven ILP (CDILP), is to iteratively interleave the search for a hypothesis with the generation of constraints which explain why the current hypothesis does not cover a particular example. These coverage constraints allow ILASP to rule out not just the current hypothesis, but an entire class of hypotheses that do not satisfy the coverage constraint. This article formalises the CDILP approach and presents the ILASP3 and ILASP4 systems for CDILP, which are demonstrated to be more scalable than previous ILASP systems, particularly in the presence of noise.

关键词： non-monotonic inductive logic programming answer set programming conflict driven solving

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Learning weak constraints in answer set programming

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THEORY AND PRACTICE OF logic programming 2015年第4-5期15卷 511-525页

作者： Law, Mark Russo, Alessandra Broda, Krysia Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.

关键词： non-monotonic inductive logic programming Preference Learning Answer Set programming

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Iterative Learning of Answer Set Programs from Context Dependent Examples

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THEORY AND PRACTICE OF logic programming 2016年第5-6期16卷 834-848页

作者： Law, Mark Russo, Alessandra Broda, Krysia Imperial Coll Dept Comp London SW7 2AZ England

In recent years, several frameworks and systems have been proposed that extend inductive logic programming (ILP) to the Answer Set programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples;however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy.

关键词： non-monotonic inductive logic programming Answer Set programming Iterative Learning

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Iterative Learning of Answer Set Programs from Context Dependent Examples

Iterative Learning of Answer Set Programs from Context Depen...

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32nd International Conference on logic programming (ICLP)

作者： Law, Mark Russo, Alessandra Broda, Krysia Imperial Coll Dept Comp London SW7 2AZ England

关键词： non-monotonic inductive logic programming Answer Set programming Iterative Learning

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