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AIPS
2010
13 years 9 months ago
When Policies Can Be Trusted: Analyzing a Criteria to Identify Optimal Policies in MDPs with Unknown Model Parameters
Computing a good policy in stochastic uncertain environments with unknown dynamics and reward model parameters is a challenging task. In a number of domains, ranging from space ro...
Emma Brunskill
AAMAS
2002
Springer
13 years 7 months ago
Multiagent Learning for Open Systems: A Study in Opponent Classification
Abstract. Open systems are becoming increasingly important in a variety of distributed, networked computer applications. Their characteristics, such as agent diversity, heterogenei...
Michael Rovatsos, Gerhard Weiß, Marco Wolf
ATAL
2005
Springer
14 years 29 days ago
Approximating state estimation in multiagent settings using particle filters
State estimation consists of updating an agent’s belief given executed actions and observed evidence to date. In single agent environments, the state estimation can be formalize...
Prashant Doshi, Piotr J. Gmytrasiewicz
AAAI
2011
12 years 7 months ago
Combining Learned Discrete and Continuous Action Models
Action modeling is an important skill for agents that must perform tasks in novel domains. Previous work on action modeling has focused on learning STRIPS operators in discrete, r...
Joseph Z. Xu, John E. Laird
ECCV
2008
Springer
14 years 9 months ago
An Incremental Learning Method for Unconstrained Gaze Estimation
Abstract. This paper presents an online learning algorithm for appearancebased gaze estimation that allows free head movement in a casual desktop environment. Our method avoids the...
Yusuke Sugano, Yasuyuki Matsushita, Yoichi Sato, H...