This document outlines a number of projects for which I am seeking to recruit graduate students. Of the projects detailed below, the rst three will be conducted in collaboration with Toby Walsh. In general I have a range of research interests to do with knowledge representation , automated reasoning and machine learning. Results are applied to problems in planning, natural language processing, ...

We investigate k-term DNF learning, the task of inducing a propositional DNF formula with at most k terms from a set of positive and negative examples. Though k-term DNF learning is NP-complete, it is at the core of most propositional learning algorithms. The main aim of this thesis is to evaluate stochastic local search algorithms to solve hard k-term DNF learning tasks. As a reference we firs...

New methods make it possible to do approximate steepest descent in O(1) time per move for k-bounded pseudo-Boolean functions using stochastic local search. It is also possible to use the average fitness over the Hamming distance 2 neighborhood as a surrogate fitness function and still retain the O(1) time per move. These are average complexity results. In light of these new results, we examine ...

In this paper tabu search for SAT is investi gated from an experimental point of view To this end TSAT a basic tabu search algorithm for SAT is introduced and compared with Selman et al Random Walk Strategy GSAT procedure in short RWS GSAT TSAT does not involve the ad ditional stochastic process of RWS GSAT This should facilitate the understanding of why simple local search methods for SAT work...

This paper investigates dynamics of a local search trajectory generated by running the Or-opt heuristic on the traveling salesman problem. This study evaluates the dynamics of the local search heuristic by estimating the correlation dimension for the search trajectory, and finds that the local heuristic search process exhibits the transition from high-dimensional stochastic to low-dimensional c...

Local search is one of the fundamental paradigms for solving computationally hard combinatorial problems, including the constraint satisfaction problem (CSP). It provides the basis for some of the most successful and versatile methods for solving the large and difficult problem instances encountered in many real-life applications. Despite impressive advances in systematic, complete search algor...

This paper experimentally investigates the dynamical behavior of a search process in a local heuristic search system for a combinatorial optimization problem. Or-opt heuristic algorithm is chosen as the study subject, and the well-known traveling salesman problem (TSP) serves as a problem testbed. This study constructs the search trajectory by using the time-delay method, evaluates the dynamics...

This paper proposes a stochastic, and complete, backtrack search algorithm for Propositional Satisfiability (SAT). In recent years, randomization has become pervasive in SAT algorithms. Incomplete algorithms for SAT, for example the ones based on local search, often resort to randomization. Complete algorithms also resort to randomization. These include, state-of-the-art backtrack search SAT al...