|Author||Yee, Richard C. ♦ Saxena, Sharad ♦ Utgoff, Paul E. ♦ Barto, Andrew G.|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Problem State ♦ Learning Technique ♦ Minimax Gameplaying ♦ Knowledge-free Approach ♦ End-game State ♦ Learning Process ♦ Useful Concept ♦ Learned Definition ♦ Problem Domain ♦ New Concept Definition ♦ Explanation-based Generalization ♦ Goal Regression ♦ Efficient Recognition Process ♦ Tic-tac-toe System ♦ Evaluating State ♦ Temporal Difference ♦ Negative Condition|
|Description||We describe a technique for improving problemsolving performance by creating concepts that allow problem states to be evaluated through an efficient recognition process. A temporal-difference (TD) method is used to bootstrap a collection of useful concepts by backing up evaluations from recognized states to their predecessors. This procedure is combined with explanation-based generalization (EBG) and goal regression to use knowledge of the problem domain to help generalize the new concept definitions. This maintains the efficiency of using the concepts and accelerates the learning process in comparison to knowledge-free approaches. Also, because the learned definitions may describe negative conditions, it becomes possible to use EBG to explain why some instance is not an example of a concept. The learning technique has been elaborated for minimax gameplaying and tested on a Tic-Tac-Toe system, T2. Given only concepts defining the end-game states and constrained to ...|
|Educational Role||Student ♦ Teacher|
|Age Range||above 22 year|
|Education Level||UG and PG ♦ Career/Technical Study|
|Learning Resource Type||Article|
|Publisher Institution||In Proceedings of the Eighth National Conference on AI, Menlo Park|
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