Accession Number : ADA255709


Title :   A Probabilistic Approach to Anytime Algorithm for Intelligent Real-Time Problem Solving


Descriptive Note : Final rept. 1 Dec 1990-31 May 1992


Corporate Author : ROCHESTER UNIV NY DEPT OF COMPUTER SCIENCE


Personal Author(s) : Tenenberg, Josh ; Allen, James


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a255709.pdf


Report Date : 04 Aug 1992


Pagination or Media Count : 38


Abstract : Our work on real time intelligent problem solving has focused on the tradeoff between deliberation and activity. Such a tradeoff is required, since an excess of deliberation will be defeated by the dynamical nature of the world and by errors in the predictive model, and a lack of deliberation will not provide the agent with sufficient flexibility to perform well in novel situations. Our framework for evaluating this tradeoff includes both an explicit and an implicit component. In the explicit work, we represent the uncertainties associated with inaccuracies in the model and the inability to completely monitor changes in the world by expanding our language to include probabilities, and making choices about when to act and when to deliberate further based upon these explicit uncertainty measures. In the implicit approach, we use reinforcement learning of a Markov Decision Process to place a strict bound on deliberation. The agent's knowledge is obtained through an active sensory system having limited bandwidth, overcoming the standard limitations of assuming complete knowledge, but requiring modifications to the standard learning algorithm. Learning time is decreased by the use of social learning mechanisms as well as task decomposition and dynamic policy merging.


Descriptors :   *PROBLEM SOLVING , *ARTIFICIAL INTELLIGENCE , ALGORITHMS , UNCERTAINTY , POLICIES , LIMITATIONS , ERRORS , WORK , LEARNING , SELECTION , BANDWIDTH , LANGUAGE , STANDARDS , PLANNING , MODELS , REAL TIME , MODIFICATION


Subject Categories : Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE