Agency, learning and animal-based reinforcement learning

E. Alonso, E. Mondragon

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

In this paper we contend that adaptation and learning are essential in designing and building autonomous software systems for real-life applications. In particular, we will argue that in dynamic, complex domains autonomy and adaptability go hand by hand, that is, that agents cannot make their own decisions if they are not provided with the ability to adapt to the changes occurring in the environment they are situated. In the second part, we maintain the need for taking up animal learning models and theories to overcome some serious problems in reinforcement learning.
Original languageEnglish
Title of host publicationAgents and Computational Autonomy
Subtitle of host publicationPotential, Risks, and Solutions
EditorsM Nickles, M Rovatsos, G Weiss
Place of PublicationBerlin, Germany
PublisherSpringer
Pages1-6
Number of pages6
ISBN (Print)9783540224778
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science
Volume2969

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  • Cite this

    Alonso, E., & Mondragon, E. (2004). Agency, learning and animal-based reinforcement learning. In M. Nickles, M. Rovatsos, & G. Weiss (Eds.), Agents and Computational Autonomy: Potential, Risks, and Solutions (pp. 1-6). (Lecture Notes in Computer Science; Vol. 2969). Springer.