Browsing by Author "Yang, Q."
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Item Exploiting Limited Interactions in Plan Optimization(1990) Yang, Q.; Nau, D.S.; Hendler, James A.; ISRPast Planning systems have generally focused on structures capable of working in all domains (domain-independent planning) or on specific heuristics for a particular applied domain (domain-dependent planning). An alternate approach is to abstract the kinds of goal and subgoal interactions that occur in some set of related problem domains, and develop planning techniques capable of performing relatively efficiently in all domains in which no other kinds of interactions occur. In this paper we will demonstrate this approach on a particular formulation of multiple-goal planning problems. In particular, we demonstrate that for cases where multiple-goal planning can be performed by generating individual separate plans for each goal independently and then optimizing the conjunction, we can define a set of limitations on the allowable interactions between goals that allow efficient planning to occur where the restrictions hold. We further argue that these restrictions are satisfied across a significant class of planning domains. We present algorithms which are efficient for special cases of multiple-goal planning, propose a heuristic search algorithm that performs well in a more general case, and describe a statistical study that demonstrates the efficiency of this search algorithm.Item Optimal Process Sequencing in CAPP Systems.(1988) Ssemakula, M.E.; Nau, D.S.; Rangachar, R.M.; Yang, Q.; ISRAutomated Process Planning forms an important link in CIM Systems. Process Sequencing is one of the most important phases of process planning and is influenced by factors such as part geometry, available manufacturing resources and generated cutting forces. This paper describes a new AI based approach to optimizing this function using heuristic search techniques.Item The Preprocessing of Search Spaces for Branch and Bound Search.(1988) Yang, Q.; Nau, D.S.; ISRHeuristic search procedures are useful in a large number of problems of practical importance. Such procedures operate by searching several paths in a search space at the same time, expanding some paths more quickly than others depending on which paths look most promising. Often large amounts of time are required in keeping track of the information control knowledge. For some problems, this overhead can be greatly reduced by preprocessing the problem in appropriate ways. In particular, we discuss a data structure called a threaded decision graph, which can be created by preprocessing the search space for some problems, and which captures the control knowledge for problem solving. We show how this can be done, and we present an analysis showing that by using such a method, a great deal of time can be saved during problem solving processes.