Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial Intelligence - Special issue on knowledge representation
Operational rationality through compilation of anytime algorithms
Operational rationality through compilation of anytime algorithms
Optimal composition of real-time systems
Artificial Intelligence
Knowledge-based anytime computation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Anytime algorithm development tools
ACM SIGART Bulletin
Algorithms for Scheduling Real-Time Tasks with Input Error and End-to-End Deadlines
IEEE Transactions on Software Engineering
Optimal Sequencing of Contract Algorithms
Annals of Mathematics and Artificial Intelligence
Learning and Exploiting Relative Weaknesses of Opponent Agents
Autonomous Agents and Multi-Agent Systems
Handling duration uncertainty in meta-level control of progressive processing
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Real-time problem-solving with contract algorithms
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Point-based policy generation for decentralized POMDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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Contract algorithms offer a tradeoff between output quality and computation time provided that the amount of computation time is determined prior to their activation Originally they were introduced as an intermediate step in the composition of interruptible anytime algorithms However for many real-time tasks such as information gathering game playing, and a large class of planning problems contract algorithms offer an ideal mechanism to optimize decision quality This paper extends previous results regarding the meta-level control of contract algorithms by handling a more general type of performance description. The output quality of each contract algorithm is described by a probabilistic (rather than deterministic) conditional performance profile. Such profiles map input quality and computation time to a probability distribution of output quality. The composition problem is solved by an efficient off-line compilation technique that simplifies the run-time monitoring task.