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...
This paper studies the evolution of the perceptions of players about the game they are involved in using the framework of hypergame theory. The focus is on developing methods that ...
Computers have already eclipsed the level of human play in competitive Scrabble, but there remains room for improvement. In particular, there is much to be gained by incorporating...
A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algori...
In this paper we study the topic of CBR systems learning from observations in which those observations can be represented as stochastic policies. We describe a general framework wh...
Kellen Gillespie, Justin Karneeb, Stephen Lee-Urba...