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.
|Title of host publication||Agents and Computational Autonomy|
|Subtitle of host publication||Potential, Risks, and Solutions|
|Editors||M Nickles, M Rovatsos, G Weiss|
|Place of Publication||Berlin, Germany|
|Number of pages||6|
|Publication status||Published - 2004|
|Name||Lecture Notes in Computer Science|
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.