Alle Publikationen
- <<
- <
- 1
2017
-
(2017): An Evolutionary Transfer Reinforcement Learning Framework for Multiagent Systems. In: IEEE Transactions on Evolutionary Computation 21 (4), S. 601-615. DOI: 10.1109/TEVC.2017.2664665
Abstract: In this paper, we present an evolutionary transfer reinforcement learning framework (eTL) for developing intelligent agents capable of adapting to the dynamic environment of multiagent systems (MASs). Specifically, we take inspiration from Darwin's theory of natural selection and Universal Darwinism as the principal driving forces that govern the evolutionary knowledge transfer process. The essential backbone of our proposed eTL comprises several meme-inspired evolutionary mechanisms, namely meme representation, meme expression, meme assimilation, meme internal evolution, and meme external evolution. Our proposed approach constructs social selection mechanisms that are modeled after the principles of human learning to identify appropriate interacting partners. eTL also models the intrinsic parallelism of natural evolution and errors that are introduced due to the physiological limits of the agents' ability to perceive differences, so as to generate "growth" and "variation" of knowledge that agents have of the world, thus exhibiting higher adaptivity capabilities on solving complex problems. To verify the efficacy of the proposed paradigm, comprehensive investigations of the proposed eTL against existing state-of-the-art TL methods in MAS, are conducted on the "minefield navigation tasks" platform and the "Unreal Tournament 2004" first person shooter computer game, in which homogeneous and heterogeneous learning machines are considered.
2001
-
(2001): Cognitive science meets multi-agent systems. A prolegomenon. In: Philosophical Psychology 14 (1), S. 5-28. DOI: 10.1080/09515080120033599
Abstract: In the current research on multi-agent systems (MAS), many theoretical issues related to sociocultural processes have been touched upon. These issues are in fact intellectually profound and should prove to be significant for MAS. Moreover, these issues should have equally significant impact on cognitive science, if we ever try to understand cognition in the broad context of sociocultural environments in which cognitive agents exist. Furthermore, cognitive models as studied in cognitive science can help us in a substantial way to better probe multi-agent issues, by taking into account essential characteristics of cognitive agents and their various capacities. In this paper, we systematically examine the interplay among social sciences, MAS, and cognitive science. We try to justify an integrated approach for MAS which incorporates different perspectives. We show how a new cognitive model, CLARION, can embody such an integrated approach through a combination of autonomous learning and assimilation.
- <<
- <
- 1