Search

RoboCup Rescue simulation as a large-scale Multi agent system (MAS) is one of the challenging environments for keeping coordination between agents to achieve the objectives despite sensing and communication limitations. The dynamicity of the environment and intensive dependency between actions of different kinds of agents make the problem more complex. This point encouraged us to use learning-based methods to adapt our decision making to different situations. Our approach is utilizing reinforcement leaning. Using learning in rescue simulation is one of the current ways which has been the subject of several researches in recent years. In this paper we present an innovative learning method implemented for Police Force (PF) Agent. This method can cope with the main difficulties that exist in other learning approaches. Different methods used in the literature have been examined. Their drawbacks and possible improvements have led us to the method proposed in this paper which is fast and accurate. The Brain Emotional Learning Based Intelligent Controller (BELBIC) is our solution for learning in this environment. BELBIC is a physiologically motivated approach based on a computational model of amygdale and limbic system. The paper presents the results obtained by the proposed approach, showing the power of BELBIC as a decision making tool in complex and dynamic situation., {"references":["J.Habibi, M.Ahmadi, A.Nouri, M.Sayyadian, M.Nevisi, Utilizing\nDefferent Multiagent Methods in RobocupRescue Simulation,\nRobocup2003, 2003.","Kitano, et al.: RoboCup-Rescue: Search and Rescue in Large Scale\nDisasters as a Domain for Autonomous Agents Research, IEEE Conf on\nMan, Systems, and Cybernetics (1999).","Robocup-Rescue Simulation Manual, The Robocup Rescue Technical\nCommittee, 2000.","Michael Wooldridge, Nicholas R. Jennings, \"Intelligent Agents: Theory\nand Practice\", The Knowledge Engineering Review, 10:115-152 1995.","Alan H. Bond and Les Gasser. An analysis of problems and research in\nDAI. In Alan H. Bond and Les Gasser, editors, Readings in Distributed\nArtificial Intelligence, pages 3-35.Morgan Kaufmann Publishers, San\nMateo, CA, 1988.","Peter Stone_ Manuela Veloso, \"Multiagent Systems: A Survey from a\nMachine Learning Perspective\", In Autonomous Robotics volume 8,\nnumber 3. July, 2000.","R. M. Milasi, C. Lucas, and B. N. Araabi. Speed Control of an Interior\nPermanent Magnet Synchronous Motor Using BELBIC (Brain\nEmotional Learning Based Intelligent Controller). In M. Jamshidi, L.\nFoulloy, A. Elkamel, and J. S. Jamshidi (eds.), Intelligent Automations\nand Control- Trends, Principles, and Applications. Albuquerque, NM,\nUSA: TSI Press Series: Proceedings of WAC, 16, M. Jamshidi (series\neditor), 2004. 280- 286.","R. Mohammadi Milasi, C. Lucas, and B. N. Araabi. A Novel Controller\nfor a Power System Based BELBIC (Brain Emotional Learning Based\nIntelligent Controller). In M. Jamshidi, L. Foulloy, A. Elkamel, and J. S.\nJamshidi (eds.), Intelligent Automations and Control- Trends, Principles,\nand Applications. Albuquerque, NM, USA: TSI Press Series:\nProceedings of WAC, 16, M. Jamshidi (series editor), 2004. 409- 420.","G. Zandesh, J. Moghani, C. Lucas, D. Shahmirzadi, B.N. Araabi O.\nNamaki, H. Kord. Speed Control of a Switched Reluctance Motor Using\nBELBIC. WSEAS Transactions on Systems, 3(1), January 2004, 1-7.\n[10] J. Moren, C. Balkenius. A Computational Model of Emotional Learning\nin The Amygdala: From animals to animals. in Proc. 6th International\nconference on the simulation of adaptive behavior, Cambridge, Mass.,\nThe MIT Press, 2000.\n[11] C. Lucas, D. Shahmirzadi, N. Sheikholeslami. Introducing BELBIC:\nBrain Emotional Learning Based Intelligent Controller. International\nJournal of Intelligent Automation and Soft Computing, Vol. 10, No. 1,\n2004, pp. 11-22.\n[12] M. Fatourechi, C. Lucas, A. Khaki Sedigh. Emotional Learning as a\nNew Tool for Development of Agent based System. Informatica, 27(2),\nJune 2003, 137-144."]}

This book discusses biologically inspired robotic actuators designed to offer improved robot performance and approaching human-like efficiency and versatility. It assesses biological actuation and control in the human motor system, presents a range of technical actuation approaches, and discusses potential applications in wearable robots, i.e., powered prostheses and exoskeletons. Gathering the findings of internationally respected researchers from various fields, the book provides a uniquely broad perspective on bioinspired actuator designs for robotics. Its scope includes fundamental aspects of biomechanics and neuromechanics, actuator and control design, and their application in (wearable) robotics. The book offers PhD students and advanced graduate students an essential introduction to the field, while providing researchers a cutting-edge research perspective.