Oliver Ray

Nonmonotonic Abductive Inductive Learning

Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and negative examples with respect to background knowledge expressed as logic programs. Negation as Failure (NAF) is a key feature of logic programming which provides a means for nonmonotonic commonsense reasoning under incomplete information. But, so far, most ILP research has been aimed at Horn programs which exclude NAF, and has failed to exploit the full potential of normal programs that allow NAF. By contrast, Abductive Logic Programming (ALP), a related task concerned with explaining observations with respect to a prior theory, has been well studied and applied in the context of normal logic programs. This paper shows how ALP can be used to provide a semantics and proof procedure for nonmonotonic ILP that utilises practical methods of language and search bias to reduce the search space. This is done by lifting an existing method called Hybrid Abductive Inductive Learning (HAIL) from Horn clauses to normal logic programs. To demonstrate its potential benefits, the resulting system, called XHAIL, is applied to a process modelling case study involving a nonmonotonic temporal Event Calculus (EC).

Hybrid Abductive Inductive Learning: A Generalisation of Progol

The learning system Progol5 and the underlying inference method of Bottom Generalisation are firmly established within Inductive Logic Programming (ILP). But despite their success, it is known that Bottom Generalisation, and therefore Progol5, are restricted to finding hypotheses that lie within the semantics of Plotkin’s relative subsumption. This paper exposes a previously unknown incompleteness of Progol5 with respect to Bottom Generalisation, and proposes a new approach, called Hybrid Abductive Inductive Learning, that integrates the ILP principles of Progol5 with Abductive Logic Programming (ALP). A proof procedure is proposed, called HAIL, that not only overcomes this newly discovered incompleteness, but further generalises Progol5 by computing multiple clauses in response to a single seed example and deriving hypotheses outside Plotkin’s relative subsumption. A semantics is presented, called Kernel Generalisation, which extends that of Bottom Generalisation and includes the hypotheses constructed by HAIL.

SOLAR: An automated deduction system for consequence finding

SOLAR (SOL for Advanced Reasoning) is a first-order clausal consequence finding system based on the SOL (Skip Ordered Linear) tableau calculus. The ability to find non-trivial consequences of an axiom set is useful in many applications of Artificial Intelligence such as theorem proving, query answering and nonmonotonic reasoning. SOL is a connection tableau calculus which is complete for finding the non-subsumed consequences of a clausal theory. SOLAR is an efficient implementation of SOL that employs several methods to prune away redundant branches of the search space. This paper introduces some of the key pruning and control strategies implemented in SOLAR and demonstrates their effectiveness on a collection of benchmark problems.

Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition

This paper presents a qualitative logic-based method for the steady-state analysis and revision of metabolic networks with inhibition. The approach is able to automatically revise an initial metabolic model -- through the addition and removal of whole reactions or individual substrates, products and inhibitors -- in order to ensure the existence of a steady-state behaviour consistent with a set of experimental observations. We show how this can be done in a nonmonotonic logic programming setting and discuss the challenges that arise when metabolic cycles or mutual inhibitions occur in the underlying network.

Learning Rules from User Behaviour

Pervasive computing requires infrastructures that adapt to changes in user behaviour while minimising user interactions. Policy-based approaches have been proposed as a means of providing adaptability but, at present, require policy goals and rules to be explicitly defined by users. This paper presents a novel, logic-based approach for automatically learning and updating models of users from their observed behaviour. We show how this task can be accomplished using a nonmonotonic learning system, and we illustrate how the approach can be exploited within a pervasive computing framework.

Extracting Requirements from Scenarios with ILP

Requirements Engineering involves the elicitationof high-level stakeholder goals and their refinementinto operational system requirements. A key difficulty is that stakeholders typically convey their goals indirectly through intuitive narrative-style scenarios of desirable and undesirable system behaviour, whereas goal refinement methods usually require goals to be expressed declaratively using, for instance, a temporal logic. Currently, the extraction of formal requirements from scenario-based descriptions is a tedious and error-prone process that would benefit from automated tool support. We present an ILP methodology for inferring requirements from a set of scenarios and an initial but incomplete requirements specification. The approach is based on translating the specification and scenarios into an event-based logic programming formalism and using a non-monotonic ILP system to learn a set of missing event preconditions. The contribution of this paper is a novel application of ILP to requirements engineering that also demonstrate the need for non-monotonic learning.

Analyzing pathways using ASP-based approaches

This paper contributes to a line of research which aims to combine numerical information with logical inference in order to find the most likely states of a biological system under various (actual or hypothetical) constraints. To this end, we build upon a state-of-the-art approach that employs weighted Boolean constraints to represent and reason about biochemical reaction networks. Our first contribution is to show how this existing method fails to deal satisfactorily with networks that contain cycles. Our second contribution is to define a new method which correctly handles such cases by exploiting the formalism of Answer Set Programming (ASP). We demonstrate the significance of our results on two case-studies previously studied in the literature.

Using Abduction and Induction for Operational Requirements Elaboration

Abstract Requirements Engineering involves the elicitation of high-level stakeholder goals and their refinement into operational system requirements. A key difficulty is that stakeholders typically convey their goals indirectly through intuitive narrative-style scenarios of desirable and undesirable system behaviour, whereas goal refinement methods usually require goals to be expressed declaratively using, for instance, a temporal logic. In actual software engineering practice, the extraction of formal requirements from scenario-based descriptions is a tedious and error-prone process that would benefit from automated tool support. This paper presents an Inductive Logic Programming method for inferring operational requirements from a set of example scenarios and an initial but incomplete requirements specification. The approach is based on translating the specification and the scenarios into an event-based logic programming formalism and using a non-monotonic reasoning system, called eXtended Hybrid Abductive Inductive Learning, to automatically infer a set of event pre-conditions and trigger-conditions that cover all desirable scenarios and reject all undesirable ones. This learning task is a novel application of logic programming to requirements engineering that also demonstrates the utility of non-monotonic learning capturing pre-conditions and trigger-conditions.

A Nonmonotonic Logical Approach for Modelling and Revising Metabolic Networks

his paper describes a new logic-based approach for representing and reasoning about metabolic networks.First it shows how biological pathways can be elegantly represented in a logic programming formalism able to model full chemical reactions with substrates and products in different cell compartments, and which are catalysed by iso-enzymes or enzyme-complexes that are subject to inhibitory feedbacks.Then it shows how a nonmonotonic reasoning system called XHAIL can be used as a practical method for learning and revising such metabolic networks from observational data.Preliminary results are described in which the approach is validated on a state-of-the-art model of Aromatic Amino Acid biosynthesis.

Automatic revision of metabolic networks through logical analysis of experimental data

This paper presents a nonmonotonic ILP approach for the automatic revision of metabolic networks through the logical analysis of experimental data. The method extends previous work in two respects: by suggesting revisions that involve both the addition and removal of information; and by suggesting revisions that involve combinations of gene functions, enzyme inhibitions, and metabolic reactions. Our proposal is based on a new declarative model of metabolism expressed in a nonmonotonic logic programming formalism. With respect to this model, a mixture of abductive and inductive inference is used to compute a set of minimal revisions needed to make a given network consistent with some observed data. In this way, we describe how a reasoning system called XHAIL was able to correctly revise a state-of-the-art metabolic pathway in the light of real-world experimental data acquired by an autonomous laboratory platform called the Robot Scientist.