Inductive Logic Programming as Abductive Search

We present a novel approach to non-monotonic ILP and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ILP systems based on Inverse Entailment and is the first top-down ILP system that allows background theories and hypotheses to be normal logic programs. The approach relies on mapping an ILP problem into an equivalent ALP one. This enables the use of established ALP proof procedures and the specification of richer language bias with integrity constraints. The mapping provides a principled search space for an ILP problem, over which an abductive search is used to compute inductive solutions

Constraints on predicate invention

This chapter describes an inductive learning method that derives logic programs and invents predicates when needed. The basic idea is to form the least common anti-instance (LCA) of selected seed examples. If the LCA is too general it forms the starting poínt of a gneral-to-specific search which is guided by various constraints on argument dependencies and critical terms. A distinguishing feature of the method is its ability to introduce new predicates. Predicate invention involves three steps. First, the need for a new predicate is discovered and the arguments of the new predicate are determíned using the same constraints that guide the search. In the second step, instances of the new predicate are abductively inferred. These instances form the input for the last step where the definition of the new predicate is induced by recursively applying the method again. We also outline how such a system could be more tightly integrated with an abductive learning system

Inferring the function of genes from synthetic lethal mutations

Techniques for detecting synthetic lethal mutations in double gene deletion experiments are emerging as powerful tool for analysing genes in parallel or overlapping pathways with a shared function. This paper introduces a logic-based approach that uses synthetic lethal mutations for mapping genes of unknown function to enzymes in a known metabolic network. We show how such mappings can be automatically computed by a logical learning system called eXtended Hybrid Abductive Inductive Learning (XHAIL)

Logic Programming for Describing and Solving Planning Problems

A logic programming paradigm which expresses solutions to problems as stable models has recently been promoted as a declarative approach to solving various combinatorial and search problems, including planning problems. In this paradigm, all program rules are considered as constraints and solutions are stable models of the rule set. This is a rather radical departure from the standard paradigm of logic programming. In this paper we revisit abductive logic programming and argue that it allows a programming style which is as declarative as programming based on stable models. However, within abductive logic programming, one has two kinds of rules. On the one hand predicate definitions (which may depend on the abducibles) which are nothing else than standard logic programs (with their non-monotonic semantics when containing with negation); on the other hand rules which constrain the models for the abducibles. In this sense abductive logic programming is a smooth extension of the standard paradigm of logic programming, not a radical departure.Comment: 8 pages, no figures, Eighth International Workshop on Nonmonotonic Reasoning, special track on Representing Actions and Plannin

Introduction to the 26th International Conference on Logic Programming Special Issue

This is the preface to the 26th International Conference on Logic Programming Special IssueComment: 6 page

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology