Time-dependent utility and action under uncertainty
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Planning and control
Decision analysis and expert systems
AI Magazine
Computation and action under bounded resources
Computation and action under bounded resources
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Ideal reformulation of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Display of information for time-critical decision making
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Continual computation policies for utility-directed prefetching
Proceedings of the seventh international conference on Information and knowledge management
Towards Flexible Teamwork in Persistent Teams: Extended Report
Autonomous Agents and Multi-Agent Systems
Optimal Sequencing of Contract Algorithms
Annals of Mathematics and Artificial Intelligence
Dynamic Composition of Information Retrieval Techniques
Journal of Intelligent Information Systems
Real-time problem-solving with contract algorithms
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Reactive control of dynamic progressive processing
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Usability guidelines for interactive search in direct manipulation systems
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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Automated problem solving is viewed typically as the expenditure of computation to solve one or more problems passed to a reasoning system. In response to each problem received, effort is applied to generate a solution and problem solving ends when the solution is rendered. We discuss the notion of continual computation that addresses a broader conception of problem by considering the ideal use of the idle time between problem instances. The time is used to develop solutions proactively to one or more expected challenges in the future. We consider analyses for traditional all-or-nothing algorithms as well as more flexible computational procedures. After exploring the allocation of idle time for several settings, we generalize the analysis to consider the case of shifting computation from a current problem to solve future challenges. Finally, we discuss a sample application of the use of continual computation in the setting of diagnostic reasoning.