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        Grounding Action-Selection in Event-Based Anticipation

        Author
        Capdepuy, P.
        Polani, D.
        Nehaniv, C.L.
        Attention
        2299/3158
        Abstract
        Anticipation is one of the key aspects involved in flexible and adaptive behavior. The ability for an autonomous agent to extract a relevant model of its coupling with the environment and of the environment itself can provide it with a strong advantage for survival. In this work we develop an event-based anticipation framework for performing latent learning and we provide two mathematical tools to identify relevant relationships between events. These tools allow us to build a predictive model which is then embedded in an action-selection architecture to generate adaptive behavior. We first analyze some of the properties of the model in simple learning tasks. Its efficiency is evaluated in a more complex task where the agent has to adapt to a changing environment. In the last section we discuss extensions of the model presented.
        Publication date
        2007
        Published in
        Lecture Notes in Computer Science
        Published version
        https://doi.org/10.1007/978-3-540-74913-4_26
        Other links
        http://hdl.handle.net/2299/3158
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