Journal of Computer and System Sciences
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Optimal composition of real-time systems
Artificial Intelligence
Coalitions among computationally bounded agents
Artificial Intelligence - Special issue on economic principles of multi-agent systems
A Bayesian approach to relevance in game playing
Artificial Intelligence - Special issue on relevance
Monitoring and control of anytime algorithms: a dynamic programming approach
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Principles and applications of continual computation
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Bargaining with limited computation: deliberation equilibrium
Artificial Intelligence
Reactive Control of Dynamic Progressive Processing
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Real-Time Problem-Solving with Contract Algorithms
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Costly valuation computation in auctions
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Provably bounded-optimal agents
Journal of Artificial Intelligence Research
The Journal of Supercomputing
Metacognition in computation: a selected research review
Artificial Intelligence
Approximation algorithms for budgeted learning problems
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Controlling deliberation in a Markov decision process-based agent
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Purely epistemic markov decision processes
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Field review: Metacognition in computation: A selected research review
Artificial Intelligence
Efficiently gathering information in costly domains
Decision Support Systems
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In most real-world settings, due to limited time or other resources, an agent cannot perform all potentially useful deliberation and information gathering actions. This leads to the metareasoning problem of selecting such actions. Decision-theoretic methods for metareasoning have been studied in AI, but there are few theoretical results on the complexity of metareasoning. We derive hardness results for three settings which most real metareasoning systems would have to encompass as special cases. In the first, the agent has to decide how to allocate its deliberation time across anytime algorithms running on different problem instances. We show this to be ATP-complete. In the second, the agent has to (dynamically) allocate its deliberation or information gathering resources across multiple actions that it has to choose among. We show this to be AfP-hard even when evaluating each individual action is extremely simple. In the third, the agent has to (dynamically) choose a limited number of deliberation or information gathering actions to disambiguate the state of the world. We show that this is AfP-hard under a natural restriction, and PSP ACE hard in general.