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IJAR
2006
98views more  IJAR 2006»
13 years 5 months ago
A forward-backward Monte Carlo method for solving influence diagrams
Although influence diagrams are powerful tools for representing and solving complex decisionmaking problems, their evaluation may require an enormous computational effort and this...
Andrés Cano, Manuel Gómez, Seraf&iac...
UAI
2004
13 years 6 months ago
Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials c...
Barry R. Cobb, Prakash P. Shenoy
FLAIRS
2001
13 years 6 months ago
Practical Modeling of Bayesian Decision Problems -- Exploiting Deterministic Relations
Thewidespreaduse of influence diagramsto represent andsolve Bayesiandecision problemsis still limited by the inflexibility andrather restrictive semanticsof influence diagrams. In...
Anders L. Madsen, Kristian G. Olesen, Søren...
DSRT
2003
IEEE
13 years 10 months ago
Interest Management in Agent-Based Distributed Simulations
Distributed simulation enables participants situated in different geographical locations to share a common virtual world, which is called a Distributed Virtual Environment (DVE). ...
Lihua Wang, Stephen John Turner, Fang Wang
AAAI
2008
13 years 7 months ago
Perpetual Learning for Non-Cooperative Multiple Agents
This paper examines, by argument, the dynamics of sequences of behavioural choices made, when non-cooperative restricted-memory agents learn in partially observable stochastic gam...
Luke Dickens