Showing papers by "Michael J. Maher published in 2004"

Argumentation Semantics for Defeasible Logic

Abstract: Defeasible reasoning is a simple but efficient rule-based approach to nonmonotonic reasoning. It has powerful implementations and shows promise to be applied in the areas of legal reasoning and the modelling of business rules. This paper establishes significant links between defeasible reasoning and argumentation. In particular, Dung-like argumentation semantics is provided for two key defeasible logics, of which one is ambiguity propagating and the other ambiguity blocking. There are several reasons for the significance of this work: (a) establishing links between formal systems leads to a better understanding and cross-fertilization, in particular our work sheds light on the argumentation-theoretic features of defeasible logic; (b) we provide the first ambiguity blocking Dung-like argumentation system; (c) defeasible reasoning may provide an efficient implementation platform for systems of argumentation; and (d) argumentation-based semantics support a deeper understanding of defeasible reasoning, especially in the context of the intended applications.

A Local Search Approach to Modelling and Solving Interval Algebra Problems

Abstract: Local search techniques have attracted considerable interest in the artificial intelligence community since the development of GSAT and the min-conflicts heuristic for solving propositional satisfiability (SAT) problems and binary constraint satisfaction problems (CSPs) respectively. Newer techniques, such as the discrete Langrangian method (DLM), have significantly improved on GSAT and can also be applied to general constraint satisfaction and optimization. However, local search has yet to be successfully employed in solving temporal constraint satisfaction problems (TCSPs). This paper argues that current formalisms for representing TCSPs are inappropriate for a local search approach, and proposes an alternative CSP-based end-point ordering model for temporal reasoning. The paper looks at modelling and solving problems formulated using Allen's interval algebra (IA) and proposes a new constraint weighting algorithm derived from DLM. Using a set of randomly generated IA problems, it is shown that local search outperforms existing consistency-enforcing algorithms on those problems that the existing techniques find most difficult.

Catholic high school students' attitudes toward homosexuality: a snapshot of incoming college freshmen

Abstract: This study is a survey of incoming freshmen at a Midwestern Catholic university on their agreement with Church teachings on homosexuality. In general, females had more homo-positive attitudes than males, graduates of Catholic high schools had more homo-positive attitudes than graduates from non-Catholic high schools, and graduates from coeducational Catholic high schools had more homo-positive attitudes than graduates from unisex Catholic high schools. Also, if respondents agreed with the Church’s teaching against homosexual activity and that homosexuality is a disorder, they were less likely to agree with the Church’s teachings that gay and lesbian people have rights that the Church should protect.

Solving over-constrained temporal reasoning problems using local search

Abstract: Temporal reasoning is an important task in many areas of computer science including planning, scheduling, temporal databases and instruction optimisation for compilers. Given a knowledge-base consisting of temporal relations, the main reasoning problem is to determine whether the knowledge-base is satisfiable, i.e., is there a scenario which is consistent with the information provided. However, many real world problems are over-constrained (i.e. unsatisfiable). To date, there has been little research aimed at solving over-constrained temporal reasoning problems. Recently, we developed standard backtracking algorithms to compute partial scenarios, in the spirit of Freuder and Wallace's notion of partial satisfaction. While these algorithms were capable of obtaining optimal partial solutions, they were viable only for small problem sizes. In this paper, we apply local search methods to overcome the deficiencies of the standard approach to solving over-constrained temporal reasoning problems. Inspired by our recent success in efficiently handling reasonably large satisfiable temporal reasoning problems using local search, we have developed two new local search algorithms using a random restart strategy and a TABU search. Further, we extend our previous constraint weighting algorithm to handle over-constrained problems. An empirical study of these new algorithms was performed using randomly generated under- and over-constrained temporal reasoning problems. We conclude that 1) local search significantly outperforms standard backtracking approaches on over-constrained temporal reasoning problems; and 2) the random restart strategy and TABU search have a superior performance to constraint weighting for the over-constrained problems. We also conjecture that the poorer performance of constraint weighting is due to distortions of non-zero global minima caused by the weighting process.

Propositional Defeasible Logic has Linear Complexity

Abstract: Defeasible logic is a rule-based nonmonotonic logic, with both strict and defeasible rules, and a priority relation on rules. We show that inference in the propositional form of the logic can be performed in linear time. This contrasts markedly with most other propositional nonmonotonic logics, in which inference is intractable.