Develops and analyzes a heuristic search algorithm that determines the nondominated set of solution graphs for a multiobjective AND/OR graph. This algorithm, MOAO*, is a multiobjective generalization of AO*. MOAO* uses sets of vector-valued heuristic estimates to give guidance to the search. The authors show that MOAO* satisfies termination, completeness, and admissibility conditions, generalizing results associated with AO*. Further, the authors prove that if the heuristic sets satisfy a monotonicity condition, then MOAO* possesses an efficiency property reminiscent of a well-known result associated with A*.>

Presents a categorized bibliography of heuristic search research materials gathered largely from the artificial intelligence (AI) literature. The intent is to provide an introduction to this information for researchers and practitioners in operations research.>

A multiobjective generalization of the heuristic search algorithm A* is presented.called A40A*.The research is motivated by the observation that most real-world problems involve multiple.

A new mathematical interpretation of alternative pairwise comparisons for a recently developed model of single-stage multiattribute decisionmaking and trade-off weight determination under uncertainty is presented. This model includes the following key features: 1) parameter values, i.e., lowest level attribute utility scores and trade-off weights, are described by set membership, and 2) use is made of directly expressed preferences among pairs of alternatives. The original mathematical interpretation of pairwise comparisons produced the following undesirable result: more precise lowest level attribute utility scores produce a more imprecise description of the trade-off weights. The new more conservative interpretation guarantees that more precise lowest level attribute utility scores will not increase the imprecision of the trade-off weights.