Multiobjective heuristic state-space planning
Artificial Intelligence
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Answer set planning under action costs
Journal of Artificial Intelligence Research
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
Automated composition of web services by planning at the knowledge level
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
PLASMA: a plan-based layered architecture for software model-driven adaptation
Proceedings of the IEEE/ACM international conference on Automated software engineering
Modelling and automated composition of user-centric services
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
SAP speaks PDDL: exploiting a software-engineering model for planning in business process management
Journal of Artificial Intelligence Research
Automated runtime repair of business processes
Information Systems
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The importance of the problems of contingent planning with actions that have non-deterministic effects and of planning with goal preferences has been widely recognized, and several works address these two problems separately. However, combining conditional planning with goal preferences adds some new difficulties to the problem. Indeed, even the notion of optimal plan is far from trivial, since plans in nondeterministic domains can result in several different behaviors satisfying conditions with different preferences. Planning for optimal conditional plans must therefore take into account the different behaviors, and conditionally search for the highest preference that can be achieved. In this paper, we address this problem. We formalize the notion of optimal conditional plan, and we describe a correct and complete planning algorithm that is guaranteed to find optimal solutions. We implement the algorithm using BDD-based techniques, and show the practical potentialities of our approach through a preliminary experimental evaluation.