Partial constraint satisfaction
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AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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State Space Search with Prioritised Soft Constraints
Applied Intelligence
CD*: a real-time resolution optimal re-planner for globally constrained problems
Eighteenth national conference on Artificial intelligence
On preference-based search in state space graphs
Eighteenth national conference on Artificial intelligence
Distributed simulation of agent-based systems with HLA
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Search for Compromise Solutions in Multiobjective State Space Graphs
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Hierarchical path planning for situated agents in informed virtual geographic environments
Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques
An axiomatic approach to robustness in search problems with multiple scenarios
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A key assumption of all problem-solving approaches based on utility theory, including heuristic search, is that we can assign a utility or cost to each state. This in tum requires that all criteria of interest can be reduced to a common ratio scale. However, many real-world problems are difficult or impossible to formulate in terms of minimising a single criterion, and it is often more natural to express problem requirements In this of a set of constraints which a solution should satisfy. In this paper, we present a generalisation of the A* search algorithm, A* with bounded costs (ABC), which searches for a solution which best satisfies a set of prioritised soft constraints, and show that, given certain reasonable assumptions about the constraints, the algorithm is both complete and optimal. We briefly describe a route planner based on ABC and illustrate the advantages of our approach in a simple route planning problem.