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» A stochastic language for modelling opponent agents
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AAAI
1997
13 years 11 months ago
Effective Bayesian Inference for Stochastic Programs
In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choi...
Daphne Koller, David A. McAllester, Avi Pfeffer
AAAI
1998
13 years 11 months ago
Structured Representation of Complex Stochastic Systems
This paperconsidersthe problem of representingcomplex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic proce...
Nir Friedman, Daphne Koller, Avi Pfeffer
AAAI
2000
13 years 11 months ago
Solving Combinatorial Auctions Using Stochastic Local Search
Combinatorial auctions (CAs) have emerged as an important model in economics and show promise as a useful tool for tackling resource allocation in AI. Unfortunately, winner determ...
Holger H. Hoos, Craig Boutilier
UAI
2000
13 years 11 months ago
Probabilistic State-Dependent Grammars for Plan Recognition
Techniques for plan recognition under uncertainty require a stochastic model of the plangeneration process. We introduce probabilistic state-dependent grammars (PSDGs) to represen...
David V. Pynadath, Michael P. Wellman
AAAI
2006
13 years 11 months ago
Decision Making in Uncertain Real-World Domains Using DT-Golog
DTGolog, a decision-theoretic agent programming language based on the situation calculus, was proposed to ease some of the computational difficulties associated with Markov Decisi...
Mikhail Soutchanski, Huy Pham, John Mylopoulos