%0 Conference Paper %1 oai:CiteSeerPSU:247844 %A Calderoni, Stephane %B World Multiconference on Systemics, Cybernetics and Informatics SCI-99 %D 1999 %K Control Learning Multiagent Reinforcement Systems, algorithms, genetic programming, %T Generic Control Ssystem in MultiAgent Domain %U http://citeseer.ist.psu.edu/247844.html %V 7 %X This paper reports on-going works dealing with collective learning in autonomous agents context. We propose a methodology to design robust and flexible adaptive behavior with both genetic and reinforcement learning techniques.The originality of this contribution relies on the ability of the agents to manage themselves their learning task. Indeed, rather than coming from the environment, as it is implemented in many programs, we consider that the reinforcement must be intrinsically deduced by the agent itself, from satisfaction and disapointment indicators. We show that in such a way, the agents are capable of robustness facing with unexpected situations. A collective regulation problem is presented to help in clarify the different issues tackled in this paper. A software toolkit has been developped as a support for these works. %Z The Pennsylvania State University CiteSeer Archives

@inproceedings{oai:CiteSeerPSU:247844, abstract = {This paper reports on-going works dealing with collective learning in autonomous agents context. We propose a methodology to design robust and flexible adaptive behavior with both genetic and reinforcement learning techniques.The originality of this contribution relies on the ability of the agents to manage themselves their learning task. Indeed, rather than coming from the environment, as it is implemented in many programs, we consider that the reinforcement must be intrinsically deduced by the agent itself, from satisfaction and disapointment indicators. We show that in such a way, the agents are capable of robustness facing with unexpected situations. A collective regulation problem is presented to help in clarify the different issues tackled in this paper. A software toolkit has been developped as a support for these works.}, added-at = {2008-06-19T17:35:00.000+0200}, annote = {The Pennsylvania State University CiteSeer Archives}, author = {Calderoni, Stephane}, biburl = {https://www.bibsonomy.org/bibtex/20565e7b0d05ba746bbf49d3e082bed59/brazovayeye}, booktitle = {World Multiconference on Systemics, Cybernetics and Informatics SCI-99}, citeseer-isreferencedby = {oai:CiteSeerPSU:26950}, interhash = {575086e6645f74d40e8bf3c3051a95fb}, intrahash = {0565e7b0d05ba746bbf49d3e082bed59}, keywords = {Control Learning Multiagent Reinforcement Systems, algorithms, genetic programming,}, language = {en}, oai = {oai:CiteSeerPSU:247844}, rights = {unrestricted}, timestamp = {2008-06-19T17:37:15.000+0200}, title = {Generic Control Ssystem in MultiAgent Domain}, url = {http://citeseer.ist.psu.edu/247844.html}, volume = 7, year = 1999 }