Machine Learning Methods for Planning
Morgan Kaufmann, 1993 - Business & Economics - 540 pages
Research on planning systems has shown that domain knowledge is crucial for effectively coping with complex, changing environments. Unfortunately, acquiring and incorporating the necessary domain knowledge can be a significant problem when building a practical planning system. The knowledge engineering process is typically time-consuming and expensive. Furthermore, if a human expert is not available it may be extremely difficult to obtain the necessary knowledge.
One solution is for a system to automatically acquire domain-specific knowledge through learning. The idea of a planning system that can improve its performance with experience is very attractive. Furthermore, advances in machine learning have provided a deeper understanding of learning mechanisms relevant to acquiring such knowledge. For this reason, there is a great deal of interest in this area of artificial intelligence.
This book brings together, in one volume, a set of chapters from the primary researchers in the field, presenting a picture of its current state and its likely areas for application. The chapters describe a variety of learning methods
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