Solving Over-Constrained Temporal Reasoning Problems Using Local Search

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 ...

• Type: Conference
• Year: 2004
• Where: PRICAI
• Authors: Matthew Beaumont, John Thornton, Abdul Sattar, Michael J. Maher
• Keywords: Artificial Intelligence, Over-constrained Temporal Reasoning, PRICAI 2004, Reasoning Problems, Temporal Reasoning Problems