Knowledge revision in systems based on an informed tree search strategy : application to cartographic generalisation

TL;DR

This paper proposes an automatic knowledge revision method for informed tree search systems by analyzing execution logs, demonstrated through an application in cartographic generalisation to improve solution efficiency and quality.

Contribution

It introduces a novel approach to automatically revise problem-specific knowledge in informed search systems using log analysis and knowledge space exploration.

Findings

Effective knowledge revision improves search efficiency.

Application in cartographic generalisation shows practical benefits.

Automated approach reduces manual knowledge maintenance.

Abstract

Many real world problems can be expressed as optimisation problems. Solving this kind of problems means to find, among all possible solutions, the one that maximises an evaluation function. One approach to solve this kind of problem is to use an informed search strategy. The principle of this kind of strategy is to use problem-specific knowledge beyond the definition of the problem itself to find solutions more efficiently than with an uninformed strategy. This kind of strategy demands to define problem-specific knowledge (heuristics). The efficiency and the effectiveness of systems based on it directly depend on the used knowledge quality. Unfortunately, acquiring and maintaining such knowledge can be fastidious. The objective of the work presented in this paper is to propose an automatic knowledge revision approach for systems based on an informed tree search strategy. Our approach consists in analysing the system execution logs and revising knowledge based on these logs by modelling the revision problem as a knowledge space exploration problem. We present an experiment we carried out in an application domain where informed search strategies are often used: cartographic generalisation.