UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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A decision graph is an AND/OR graph with a certain evaluation function. Decision graphs have been found a very useful representation for a variety of decision making problems. This article present a number of search algorithms computing an optimal solution from a given decision graph. These algorithms include one {\it depth-first heuristic-search algorithm}, one {\it best-first heuristic-search algorithm}, one {\it anytime-algorithm} and two {\it iterative-deepening depth-first heuristic-search algorithms}. Similar to the *-minimax search algorithms of Ballard, our depth-first heuristic-search algorithm is developed from the alpha--beta algorithm for minimax tree search. In addition, we show how heuristic knowledge can be used to improve search efficiency. Furthermore, we present an anytime algorithm which is conveniently obtained from the depth-first heuristic-search algorithm without incurring much overhead. The best-first heuristic-search algorithm is obtained by modifying the well known AO* algorithm for AND/OR graphs with additive costs. The iterative-deepening algorithms result from combining the iterative-deepening techniques with the depth-first search techniques. Some experimental data on some of these algorithms performance are given.