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» A stochastic language for modelling opponent agents
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AAAI
1997
15 years 7 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
15 years 7 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
15 years 7 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
15 years 7 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
15 years 7 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