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Title:Cumulative Learning Using Functionally Similar States
Author(s):Mahmud, M.M. Hassan; Ray, Sylvian R.
Subject(s):Machine Learning Artificial Intelligence
Abstract:In this paper we propose a Cumulative Learning System for artificial agents that ses the idea of Functional Similarity between states. The general idea of Cumulative Learning is to build a cognitive architecture for an artificial agent that 'lives' for a long time and solves many related tasks during its lifetime. Two states (or situations) are said to be functionally similar (FS) with respect to an action if the action induces the same change on both the states. We define the notion of FS for Markov Environments, and then use that to develop a Predictive Model (PM) that given states and actions observed so far, predicts the next state when an action is taken in some novel state (state never observed before or often) - i.e. the PM is a novel type of forward model. We also describe a planning mechanism for goal directed MDPs with multiple goals that uses the PM to solve tasks quicker using information from solutions to similar tasks solved previously by the agent. After establishing some necessary theoretical properties of both we perform experiments that shows the efficacy of our method. We also outline how the current system, which can actually be categorized as a Lifelong Learning system, may be extended to a complete cumulative learning system.
Issue Date:2005-01
Genre:Technical Report
Other Identifier(s):UIUCDCS-R-2005-2511
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-17

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