In the paper, we investigate the use of reinforcement learning in CBR for estimating and managing a legacy case base for playing the game of Tetris. Each case corresponds to a loc...
Distributed partially observable Markov decision problems (POMDPs) have emerged as a popular decision-theoretic approach for planning for multiagent teams, where it is imperative f...
Making backup is so cumbersome and expensive that individuals hardly ever backup their data and companies usually duplicate their data into a secondary server. This paper proposes...
We explore an application to the game of Go of a reinforcement learning approach based on a linear evaluation function and large numbers of binary features. This strategy has prov...
Soft biometrics, as a prescreening filter, contribute to a much smaller candidate pool and allow the overall query to perform better and faster. In this paper, we focus on the eff...