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NECO
2010
14 years 10 months ago
2010
Reinforcement learning models generally assume that a stimulus is presented that allows a learner to unambiguously identify the state of nature, and the reward received is drawn f...
ATAL
2007
Springer
15 years 6 months ago
2007
Springer
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
ATAL
2006
Springer
15 years 3 months ago
2006
Springer
A long-standing challenge in interactive entertainment is the creation of story-based games with dynamically responsive story-lines. Such games are populated by multiple objects a...
KDD
2002
ACM
16 years 7 days ago
2002
ACM
Recently, there has been increasing interest in the issues of cost-sensitive learning and decision making in a variety of applications of data mining. A number of approaches have ...
NIPS
1998
15 years 1 months ago
1998
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...