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» Using iterated reasoning to predict opponent strategies
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AAMAS
2005
Springer
13 years 4 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
AAAI
2000
13 years 6 months ago
Defining and Using Ideal Teammate and Opponent Agent Models
A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its c...
Peter Stone, Patrick Riley, Manuela M. Veloso
EWCBR
2008
Springer
13 years 6 months ago
Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning
This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve...
Bryan Auslander, Stephen Lee-Urban, Chad Hogg, H&e...
CEC
2008
IEEE
13 years 11 months ago
Coevolving strategic intelligence
— Strategic decision making done in parallel with the opposition makes it difficult to predict the opposition’s strategy. An important aspect in deciding a move is evaluating ...
Phillipa M. Avery, Garrison W. Greenwood, Zbigniew...
AWIC
2004
Springer
13 years 10 months ago
Case-Based Reasoning as a Prediction Strategy for Hybrid Recommender Systems
Abstract. Hybrid recommender systems are capable of providing better recommendations than non-hybrid ones. Our approach to hybrid recommenders is the use of prediction strategies t...
Mark van Setten, Mettina Veenstra, Anton Nijholt, ...