A method for specializing logic programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
Match algorithms for generalized Rete networks
Artificial Intelligence
Automatic feature generation for problem solving systems
ML92 Proceedings of the ninth international workshop on Machine learning
Feature discovery for problem solving systems
Feature discovery for problem solving systems
Maintaining views incrementally
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
XSB as an efficient deductive database engine
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Some studies in machine learning using the game of checkers. II: recent progress
IBM Journal of Research and Development
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A good evaluation function is needed for a good game program, and good features, which are primitive metrics of a state, are needed for a good evaluation function. In order to obtain good features, automatic generation of features by machine learning is promising. However, the generated features are usually written in logic programs, whose evaluation is much slower than that of other native expressions due to the interpretive evaluation of the logic programs. In order to solve this problem, we propose a method which constructs a specialized evaluator using a combination of techniques: partial evaluation, Boolean tables, and incremental calculation. It exhaustively unfolds logical programs until they can be represented as simple Boolean tables. The constructed specialized evaluator is elRcient since it consults only these compiled tables. Experiments with Othello showed that speed can be increased approximately 2,000 times.