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AAMAS
2005
Springer
15 years 13 days ago
Learning and Exploiting Relative Weaknesses of Opponent Agents
Agents in a competitive interaction can greatly benefit from adapting to a particular adversary, rather than using the same general strategy against all opponents. One method of s...
Shaul Markovitch, Ronit Reger
ATAL
2006
Springer
15 years 4 months ago
Learning empathy: a data-driven framework for modeling empathetic companion agents
Affective reasoning plays an increasingly important role in cognitive accounts of social interaction. Humans continuously assess one another's situational context, modify the...
Scott W. McQuiggan, James C. Lester
101
Voted
AAAI
1993
15 years 1 months ago
Complexity Analysis of Real-Time Reinforcement Learning
This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of...
Sven Koenig, Reid G. Simmons
100
Voted
AAAI
2000
15 years 1 months ago
Interactive Training for Synthetic Characters
Compelling synthetic characters must behave in ways that reflect their past experience and thus allow for individual personalization. We therefore need a method that allows charac...
Song-Yee Yoon, Robert C. Burke, Bruce Blumberg, Ge...
IAT
2010
IEEE
14 years 10 months ago
Multiagent Meta-level Control for a Network of Weather Radars
It is crucial for embedded systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems. In t...
Shanjun Cheng, Anita Raja, Victor R. Lesser