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AAMAS
2005
Springer
13 years 7 months 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
13 years 11 months ago
A stochastic language for modelling opponent agents
There are numerous cases where a reasoning agent needs to reason about the behavior of an opponent agent. In this paper, we propose a hybrid probabilistic logic language within wh...
Gerardo I. Simari, Amy Sliva, Dana S. Nau, V. S. S...
ATAL
2008
Springer
13 years 10 months ago
On the usefulness of opponent modeling: the Kuhn Poker case study
The application of reinforcement learning algorithms to Partially Observable Stochastic Games (POSG) is challenging since each agent does not have access to the whole state inform...
Alessandro Lazaric, Mario Quaresimale, Marcello Re...
ATAL
2005
Springer
14 years 1 months ago
Modeling opponent decision in repeated one-shot negotiations
In many negotiation and bargaining scenarios, a particular agent may need to interact repeatedly with another agent. Typically, these interactions take place under incomplete info...
Sabyasachi Saha, Anish Biswas, Sandip Sen
AAAI
1998
13 years 9 months ago
Opponent Modeling in Poker
Poker is an interesting test-bed for artificial intelligence research. It is a game of imperfect knowledge, where multiple competing agents must deal with risk management, agent m...
Darse Billings, Denis Papp, Jonathan Schaeffer, Du...