Approximate and Compensate: A Method for Risk-Sensitive Meta-Deliberation and Continual Computation

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Approximate and Compensate: A Method for Risk-Sensitive Meta-Deliberation and Continual Computation

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Title: Approximate and Compensate: A Method for Risk-Sensitive Meta-Deliberation and Continual Computation
Author: Parkes, David C.; Greenwald, Lloyd

Note: Order does not necessarily reflect citation order of authors.

Citation: Parkes, David C., and Lloyd Greenwald. 2001. Approximate and compensate: A method for risk-sensitive meta-deliberation and continual computation. In Using uncertainty within computation: Papers from the 2001 AAAI Fall Symposium: November 2-4, 2001, North Falmouth, Massachusetts, ed. C. Gomes, T. Walsh, and American Association for Artificial Intelligence, 101-108. Menlo Park, C.A.: AAAI Press.
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Abstract: We present a flexible procedure for a resource-bounded agent to allocate limited computational resources to on-line problem solving. Our APPROXIMATE AND COMPENSATE methodology extends a well-known greedy time-slicing approach to conditions in which performance profiles may be non-concave and there is uncertainty in the environment and/or problem-solving procedures of an agent. With this method, the agent first approximates problem-solving performance and problem parameters with standard parameterized models. Second, the agent computes a risk-management factor that compensates for the risk inherent in the approximation. The risk-management factor represents a mean-variance tradeoff that may be derived optimally off-line using any available information. Theoretical and experimental results demonstrate that APPROXIMATE AND COMPENSATE extends existing methods to new problems and expands the practical application of meta-deliberation.
Published Version: https://www.aaai.org/Papers/Symposia/Fall/2001/FS-01-04/FS01-04-016.pdf
Other Sources: http://www.eecs.harvard.edu/econcs/pubs/approxcomp.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4101699

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  • FAS Scholarly Articles [7362]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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