An Efficient Knowledge Management Method for ATMS-based Problem Solving : Treatment of Detailed Assumptions and Knowledge Compilation

Abstract

In many problem solving systems based on generate-and-test paradigms, most of the knowledge is owned by the tester. This imbalance of knowldege distribution causes a lot of problems, such as the occurrence of useless inferences, increase in overhead and so on, even though the ATMS is employed to realize efficient problem solving. The knowledge owned by the tester cannot be applied for the scheduler immediately in all the problem solvers based on generate-and-test architecture. However, once the knowledge is cached by the ATMS, it is easy to convert the knowledge into more useful one for the scheduler. We can also obtain directly applicable knowledge for the scheduler by compiling the knowledge owned by the tester. Using this method, we can decrease the interaction and overhead between scheduler and ATMS. In practice, we can consider that ATMS have no overhead. Furthermore, we can reconstruct the scheduler with the compiled knowledge, to embed the declarative knowledge owned by the tester into the scheduler as the procedural knowledge. This paper describes three kinds of augmentation of ATMS-based problem solving systems together with evaluation of the methods.