Building Search Heuristics at the Knowledge Level

The development of highly effective heuristics for search problems is a difficult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Our approach targets at the implicit representation of the less clearly definable quality criteria by allowing the expert to limit their input to the system to explanations of the steps in the expert search process. These explanations are expressed in our Search Knowledge Interactive Language (SKIL). The explanations are used to construct a knowledge base representing search control knowledge. For the basis of our knowledge acquisition approach, we substantially extend the work done on Ripple-Down Rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. This extension allows the expert to enter his domain terms during the KA process, which integrates the knowledge elicitation and knowledge acquisition in one incremental process and allows the expert to provide a knowledge level model of his search process. We call the new framework Nested Ripple Down Rules. In this paper, we will show an approach to build effective search heuristics efficiently at the knowledge level. We employ SmS for the acquisition of expert chess knowledge for performing a highly pruned search. These experimental results in the chess domain are evidence for the practicality of our approach.