Generalized State Values in an Anticipatory Learning Classifier System Export

@incollection{citeulike:2499576, abstract = {This paper introduces generalized state values to the anticipatory learning classifier system ACS2. Previous studies showed that the evolving generalized state value in ACS2 might be overgeneral for a proper policy representation. Thus, the policy representation is separated from the model representation. A function approximation module is added that approximates state values. Actual action choice then depends on the learned generalized state values predicted by the means of the predictive model yielding anticipatory behavior. It is shown that the function approximation module accurately generalizes the state value function in the investigated MDP. Improvement of the approach by the means of further anticipatory interaction between predictive model learner and state value learner is suggested. We also propose the implementation of task dependent anticipatory attentional mechanisms exploiting the representation of the generalized state-value function. Finally, the anticipatory framework may be extended to support multiple motivations integrated in a motivational module which could be influenced by emotional biases.}, author = {Butz, Martin V. and Goldberg, David E.}, citeulike-article-id = {2499576}, citeulike-linkout-0 = {http://www.springerlink.com/content/bx4v195akeevc2el}, journal = {Anticipatory Behavior in Adaptive Learning Systems}, keywords = {anticipatory\_learning, lcs}, pages = {282--301}, posted-at = {2008-03-10 08:17:42}, priority = {0}, title = {Generalized State Values in an Anticipatory Learning Classifier System}, url = {http://www.springerlink.com/content/bx4v195akeevc2el}, year = {2003} }

TY - CHAP ID - citeulike:2499576 L3 - citeulike-article-id:2499576 N2 - This paper introduces generalized state values to the anticipatory learning classifier system ACS2. Previous studies showed that the evolving generalized state value in ACS2 might be overgeneral for a proper policy representation. Thus, the policy representation is separated from the model representation. A function approximation module is added that approximates state values. Actual action choice then depends on the learned generalized state values predicted by the means of the predictive model yielding anticipatory behavior. It is shown that the function approximation module accurately generalizes the state value function in the investigated MDP. Improvement of the approach by the means of further anticipatory interaction between predictive model learner and state value learner is suggested. We also propose the implementation of task dependent anticipatory attentional mechanisms exploiting the representation of the generalized state-value function. Finally, the anticipatory framework may be extended to support multiple motivations integrated in a motivational module which could be influenced by emotional biases. JF - Anticipatory Behavior in Adaptive Learning Systems EP - 301 TI - Generalized State Values in an Anticipatory Learning Classifier System SP - 282 KW - anticipatory_learning KW - lcs AU - Butz, Martin AU - Goldberg, David PY - 2003/// UR - http://www.springerlink.com/content/bx4v195akeevc2el ER -