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IJCAI
2007
14 years 5 days ago
A Hybridized Planner for Stochastic Domains
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
Mausam, Piergiorgio Bertoli, Daniel S. Weld
AAAI
2011
12 years 10 months ago
Policy Gradient Planning for Environmental Decision Making with Existing Simulators
In environmental and natural resource planning domains actions are taken at a large number of locations over multiple time periods. These problems have enormous state and action s...
Mark Crowley, David Poole
AMAI
2004
Springer
14 years 4 months ago
A Framework for Sequential Planning in Multi-Agent Settings
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
Piotr J. Gmytrasiewicz, Prashant Doshi
PE
2011
Springer
266views Optimization» more  PE 2011»
13 years 5 months ago
Lumping partially symmetrical stochastic models
Performance and dependability evaluation of complex systems by means of dynamic stochastic models (e.g. Markov chains) may be impaired by the combinatorial explosion of their stat...
Souheib Baarir, Marco Beccuti, Claude Dutheillet, ...
AIPS
2006
14 years 4 days ago
Solving Factored MDPs with Exponential-Family Transition Models
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
Branislav Kveton, Milos Hauskrecht