Issue 10 Volume 41 May 16, 2003

admissible heuristic, is used to avoid evaluating all states. We combine these two approaches in a novel way that exploits
symbolic model-checking techniques and demonstrates their usefulness for decision-theoretic planning.
Author
Algorithms; Decision Theory; Heuristic Methods; Markov Processes; Symbols
20030034795 Massachusetts Univ., Amherst, MA, USA
Transition-Independent Decentralized Markov Decision Processes
Becker, Raphen; Silberstein, Shlomo; Lesser, Victor; Goldman, Claudia V.; Morris, Robert, Technical Monitor; [2003]; 8 pp.;
In English; Second International Joint Conference on Autonomous Agents and Multi-Agent Systems; Original contains black
and white illustrations
Contract(s)/Grant(s): NAG2-1463; NAG2-1394; NSF IIS-99-07331; No Copyright; Avail: CASI; A02, Hardcopy
There has been substantial progress with formal models for sequential decision making by individual agents using the
Markov decision process (MDP). However, similar treatment of multi-agent systems is lacking. A recent complexity result,
showing that solving decentralized MDPs is NEXP-hard, provides a partial explanation. To overcome this complexity barrier,
we identify a general class of transition-independent decentralized MDPs that is widely applicable. The class consists of
independent collaborating agents that are tied up by a global reward function that depends on both of their histories. We
present a novel algorithm for solving this class of problems and examine its properties. The result is the first effective
technique to solve optimally a class of decentralized MDPs. This lays the foundation for further work in this area on both exact
and approximate solutions.
Author
Decision Making; Markov Processes
20030038363 Lawrence Livermore National Lab., Livermore, CA
Stochastic Engine Initiative: Improving Prediction of Behavior in Geologic Environments We Cannot Directly Observe
Aines, R.; Nitao, J.; Newmark, R.; Carle, S.; Ramirez, A.; Sep. 30, 2001; In English
Report No.(s): DE2002-15002143; UCRL-ID-148221; No Copyright; Avail: National Technical Information Service (NTIS)
The stochastic engine uses modern computational capabilities to combine simulations with observations. We integrate the
general knowledge represented by models with specific knowledge represented by data, using Bayesian inferencing and a
highly efficient staged Metropolis-type search algorithm. From this, we obtain a probability distribution characterizing the
likely configurations of the system consistent with existing data. The primary use will be optimizing knowledge about the
configuration of a system for which sufficient direct observations cannot be made. Programmatic applications include
underground systems ranging from environmental contamination to military bunkers, optimization of complex nonlinear
systems, and timely decision-making for complex, hostile environments such as battlefields or the detection of secret facilities.
NTIS
Stochastic Processes; Hydrogeology; Computerized Simulation; Radioactive Isotopes; Migration
20030038798 Institut des Hautes Etudes Scientifiques, Bures-sur-Yvette
Random Walk in Random Groups
Gromov, M.; Jan. 2002; 78 pp.
Report No.(s): PB2003-102078; IHES/M/02/03; No Copyright; Avail: CASI; A05, Hardcopy
Contents include the following: Random groups associated with graphs; Small cancellation; Diffusion and contraction;
Scaling limits and entropy.
NTIS
Random Walk; Groups
66
SYSTEMS ANALYSIS AND OPERATIONS RESEARCH
Includes mathematical modeling of systems; network analysis; mathematical programming; decision theory; and game theory.
20030032278 Instituto Nacional de Pesquisas Espacias, Sao Jose dos Campos, Brazil
Generalized Extremal Optimization: A New Stochastic Algorithm for Optimal Design
LuisdeSouza, Fabiano; 2003; 144 pp.; In Portuguese; Original contains color illustrations
Report No.(s): INPE-9564-TDI/836; Copyright; Avail: CASI; A07, Hardcopy
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