Knowledge revision in systems based on an informed tree search strategy : application to cartographic generalisation
This work addresses the efficiency and effectiveness of optimization systems in domains like cartography, but it is incremental as it builds on existing informed search strategies.
The paper tackles the problem of acquiring and maintaining problem-specific knowledge (heuristics) for systems using informed tree search strategies, which is often tedious, by proposing an automatic knowledge revision approach that analyzes execution logs and models revision as a knowledge space exploration problem, with an experiment in cartographic generalisation.
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.