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Design and Development of Hybrid Architecture Model Named Enhanced Mind Cognitive Architecture of pupils for Implementing the Learning Concepts in Society of Agents


  • Bharathiar University, Department of (ISE), PVP Polytechnic, Dr.AIT campus Outer Ring Road, Malathahalli, Nagarabhavi, Bangalore – 560056, Karnataka, India
  • Department of Computer Science, Research Progress Review Committee[RPRC], Dr. Ambedkar Institute of Technology, Visvesvaraya Technological University, Bengaluru – 560056, Karnataka, India


This paper depicts the results of a simulation to exhibit the standards and emergent intelligence connected with artificial life. The Economic theory can be connected to artificial life with a specific end goal to analyze the hybrid model adaptive or intelligent behaviors. This research is concerned with the standards whereby an animated skillful for its assets,thus exhibits careful conduct. This approach necessarily requires the outline and trial of a scope of basic and complex computational agents. The created small scale specialists in a fungus world test bed are intended to explore artificial personalities for creatures and engineered operators, drawing on qualities found in the common personalities. Qualities, for example, level of basic leadership, its cost capacity and utility conduct (the microeconomic level), physiological and objective situated conduct are examined. Specialist practices can be investigated utilizing a wide range of measurements;for instance, metabolic action, rivalry and social association regarding environment and microeconomics.

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  • Baxter J, Bartlett PJ. Infinite-horizon gradient-based policy search. Journal of Artificial Intelligence Research. 2001; 15:319–50.
  • Maes P, Brooks RA. Learning to coordinate behaviors. In the Proceedings of the eighth National conference on Artificial Intelligence (AAAI). 1990 Jul 29–Aug 3; 2:796–802.
  • Mahadevan S, Connell J. Automatic programming of behavior based robots using reinforcement learning. Artificial Intelligence. 1992; 55:311–65.
  • Matari´c MJ. Integration of representation into goaldriven behavior based robots. Institute of Electrical and Electronics Engineers (IEEE) Transactions on Robotics Automation. 1992 Jun; 8(3):304–12.
  • Matari´c MJ. Reward function for accelerated learning.In the Proceedings of 8th International Conference on Machine Learning; 1994. p. 181–9.
  • Matari´c MJ. Reinforcement learning in the multirobot domains. Autonomous Robots. 1997; 4(1):73–88.
  • Michaud F, Matari´c MJ. Learning from history for behaviorbased mobile robots in non-stationary conditions. Autonomous Robots and Machine Learning. 1998 Jul–Aug; 31(1–3):141–67.
  • Kohl N, Stone P. Policy gradient reinforcement learning for fast quadrupedal locomotion. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Robotics Automation(ICRA).2004; 3:2619–24.
  • Matari´c MJ. Learning in behavior-based multirobot systems: Policies, models, and other agents. Cognitive System Research (Special Issue on Multidisciplinary Studies of Multiagent Learning). 2001; 2(1):81–93.
  • Floreano D, Mondada F. Evolution of homing navigation in a real mobile robot. Institute of Electrical and Electronics Engineers (IEEE) Transactions on System., Man, Cybern.1996 Jun; 26(3):396–407.
  • Nolfi S, Floreano D. Learning and evolution. Autonomous Robots. 1999; 7(1):89–113.
  • Sutton RS, Barto AG. Reinforcement learning: an introduction.Cambridge, MA: MIT Press; 1998.
  • Whiteson S, Stone P. Evolutionary function approximation for reinforcement learning. The Journal of Machine Learning Research. 2006; 7:877–917.
  • Farahmand AM, Ahmadabadi MN, Lucas C, Araabi BN. Hybrid behavior co-evolution and structure learning in behavior-based systems. In the Proceedings of Institute of Electrical and Electronics Engineers (IEEE) International Conference on Evolutionary Computation (CEC), Vancouver, BC, Canada; 2006. p. 275–82.
  • Bertsekas DP, Tsitsiklis JN. Neuro-dyanmic programming.Belmont. MA: Athena Scientific; 1996.
  • Parker L. ALLIANCE: An architecture for fault-tolerant multirobot cooperation. Institute of Electrical and Electronics Engineers (IEEE) Transactions on Robotics Automation. 1998 Apr; 14(2):220–40.
  • Sutton RS, Barto AG. Reinforcement learning: an introduction.Cambridge, MA: MIT Press; 1998.
  • Adkins J. Metacognition: Designing for transfer. University of Saskatchewan, Canada; 2004.
  • Alonso E, Mondragó E. Agency, learning and animal-based reinforcement learning. In: Agents and Computational Autonomy Potential, Risks, and Solutions, Lecture Notes in Computer Science, Springer Berlin/Heidelberg. 2004; 2969:1–6.
  • Anderson J. Rules of the mind. Lawrence Erlbaum Associates, Hillsdale, NJ; 1993.
  • Baars BJ. A cognitive theory of consciousness. The Neurosciences Institute, San Diego, California, Cambridge University Press, New York; 1988.
  • Bartsch K. Wellman H. Young children’s attribution of action to beliefs and desires. Child Development. 1989; 60:946–64.
  • Bedau MA. Artificial life: organization, adaptation, and complexity from the bottom up. Trends in Cognitive Science. 2003; 7(11):505–12.
  • Braitenberg V. Vehicles, experiments in synthetic psychology.MIT Press; 1984.
  • Brooks RA. Cambrian intelligence: the early history of the new AI. Cambridge, MIT Press; 1999.
  • Cox MT. Metacognition in computation: a selected research review. Artificial Intelligence. 2005; 169(2):104–41.
  • Davis DN. Computational architectures for intelligence and motivation. 17th Institute of Electrical and Electronics Engineers (IEEE) International Symposium on Intelligent Control; 2002 Oct 30. p. 520–5.
  • Davis DN. Architectures for cognitive and A-life agents.Intelligent Agent Software Engineering; 2003. p. 22.
  • Plekhanova V editors. IDEA group publishing. Davis DN. Linking perception and action through motivation and affect. Journal of Experimental and Theoretical Artificial Intelligence. 2008; 20(1):37–60.
  • Diestel R. Graph theory 3rd edition. Springer-Verlag GmbH, Berlin, New York; 2005. p. 431.
  • Flavell JH. (1979). Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry.American Psychologist. 1979; 34(10): 906–11.
  • Franklin S. Artificial minds. MIT press; 1995.
  • Franklin S. Autonomous agents as embodied AI.Cybernetics and Systems. 1997; 28(6):499–520.
  • Gobet F, Lane PCR, Croker S, Cheng PCH, Jones G, Oliver I, Pine JM. Chunking mechanisms in human learning.Trends in Cognitive Science. 2001; 5:236–43.
  • Kahneman D, Miller DT. Norm theory: Comparing reality to its alternatives. Psychological Review. 1986; 93(2):136– 53.
  • McCarthy J. Programs with common sense. In the Proceedings of the Teddington Conference on the Mechanization of Thought Processes, Her Majesty’s Stationery Office, London; 1959.
  • McFarland T, Bosser T. Intelligent behavior in animals and robots. MIT press, Massachusetts; 1993.
  • Minsky M. The society of mind. Simon and Schuster, New York; 1985.
  • Nason S, Laird JE. Soar RL integrating reinforcement learning with soar. International Conference on Cognitive Modeling (ICCM 2004), Cognitive Systems Research, Science Direct. 2005 Mar; 6(1):51–9.
  • Newell A. Human problem solving. Prentice-Hall, NJ, USA; 1972.
  • Newell A. Unified theories of cognition. Cambridge, MA: Harvard University Press; 1990.
  • Rolls ET. The brain and emotion, Oxford University Press; 1999.
  • Savage T. The grounding of motivation in artificial animals: indices of motivational behavior. Cognitive Systems Research. 2003; 4:23–55.
  • Selfridge OG. Pandemonium: A paradigm for learning.Proceedings of Symposium on the Mechanization of Thought Processes, London; 1959.
  • Singh P. EM-ONE: Architecture for reflective commonsense thinking [PhD thesis]. MIT, USA; 2005.
  • Sloman A. How to think about cognitive systems: requirements and design. DARPA Workshop on Cognitive Systems, Warrenton, Virginia, USA; 2002 Nov 3–6.
  • Stillings N. Cognitive science. Cambridge, MA: MIT Press; 1995.
  • Sutton RS, Barto AG. Reinforcement learning: an introduction.MIT Press, Cambridge, MA; 1998.
  • Tesfatsion L, Judd K editors. Handbook of computational economics, volume 2: agent-based computational economics.Handbooks in Economics Series, North-Holland, Elsevier, Amsterdam, the Netherlands; 2006.
  • Thorndike EL. Animal intelligence: experimental studies.Macmillan, New York; 1911.
  • Toda M. Design of a fungus-eater. Behavioral Science.1962; 7:164–83.
  • Venkatamuni MV. A society of mind approach to cognition and metacognition in a cognitive architecture [PhD thesis].UK, University of Hull; August 2008.
  • Wahl S, Spada H. Children’s reasoning about intentions, beliefs and behaviour. Cognitive Science Quarterly. 2000; 1:5–34.
  • Yoshida E. The Influence of implicit norms on cognition and behaviour [MA thesis]. Canada: University of Waterloo; 2007.


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