140
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

A recursive genetic framework for evolutionary decision-making in problems with high dynamism

, &
Pages 2715-2731 | Received 03 Jan 2013, Accepted 23 Sep 2013, Published online: 28 Jan 2014

References

  • Balch, T. (1998). Behavioral diversity in learning robot teams. Atlanta, GA: Georgia Institute of Technology.
  • Baldwin, R., Cantey, W., Maisel, H., & McDermott, J. (1956). The optimum strategy in blackjack. Journal of the American Statistical Association, 51(275), 429–439.
  • Beer, M., D’Inverno, M., Luck, M., Jennings, N., Preist, C., & Schroeder, M. (1999). Negotiation in multi-agent systems. The Knowledge Engineering Review, 14(3), 285–289.
  • Bongard, J. (2000). The legion system: A novel approach to evolving heterogeneity for collective problem solving. In R. Poli, W. Banzhaf, W. Langdon, J. Miller, P. Nordin, & T. Fogarty (Eds.), Genetic Programming (pp. 16–28). Berlin: Springer.
  • Brauer, W., & Weiß, G. (1998). Multi-machine scheduling – a multi-agent learning approach. In Proceedings. International Conference on Multi Agent Systems (pp. 42–48). IEEE, La Villette, Paris, France.
  • Braun J.H. (1974). The development and analysis of winning strategies for the casino game of blackjack. New York, NY: Author.
  • Bull, L. (1997, July). Evolutionary computing in multi-agent environments: Partners. Paper presented at the Proceedings of the Seventh International Conference on Genetic Algorithms, East Lansing, MI.
  • Bull, L. (1998). Evolutionary computing in multi-agent environments: Operators. In V.W. Porto, N. Saravanan, D. Waagen, & A.E. Eiben (Eds.), Evolutionary Programming VII (pp. 43–52). Berlin: Springer.
  • Bull, L., & Fogarty, T.C. (1994). Evolving cooperative communicating classifier systems. Paper presented at the Proceedings of the Third Annual Conference on Evolutionary Programming, River Edge, NJ.
  • Bull, L., & Holland, O. (1997). Evolutionary computing in multi-agent environments: Eusociality. Paper presented at the Proceedings of the Second Annual Conference on Genetic Programming, San Francisco, CA.
  • Capobianco, M., Chesñevar, C.I., & Simari, G.R. (2005). Argumentation and the dynamics of warranted beliefs in changing environments. Autonomous Agents and Multi-Agent Systems, 11(2), 127–151.
  • Chang, Y.H., Ho, T., & Kaelbling, L.P. (2004). All learning is local: Multi-agent learning in global reward games. In S. Thrun, L. Saul, & B. Schölkopf (Eds.), Advances in neural information processing systems 16. Cambridge, MA: MIT Press.
  • Chen, X., Feng, L., & Soon Ong, Y. (2012). A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem. International Journal of Systems Science, 43(7), 1347–1366.
  • Dresner, K., & Stone, P. (2004, July). Multiagent traffic management: A reservation-based intersection control mechanism. Paper presented at the Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, New York, NY.
  • Forte, S.L., & Sines, R.D. (1996). U.S. Patent No. 5,586,766. Washington, DC: U.S. Patent and Trademark Office.
  • García, A.J., & Simari, G.R. (2004). Defeasible logic programming: An argumentative approach. Theory and practice of logic programming, 4(1–2), 95–138.
  • Goldberg, D.E. (1989). Genetic algorithms in search, optimization and machine learning (1st ed.). Boston, MA: Addison-Wesley Longman.
  • Good, B. (2000). Evolving multi-agent systems: Comparing existing approaches and suggesting new directions (master’s thesis). University of Sussex, Brighton.
  • Grefenstette, J. (1988). Credit assignment in rule discovery systems based on genetic algorithms. Machine Learning, 3(2), 225–245.
  • Hansson, S.O., Fermé, E.L., Cantwell, J., & Falappa, M.A. (2001). Credibility limited revision. Journal of Symbolic Logic, 66(4), 1581–1596.
  • Hara, A., & Nagao, T. (1999). Emergence of cooperative behavior using ADG: Automatically defined groups. Proceedings of the 1999 Genetic and Evolutionary Computation Conference (pp. 1039–1046), Orlando, FL.
  • Harati, A., & Ahmadabadi, M. (2004). Experimental analysis of knowledge based multiagent credit assignment. Studies in Fuzziness and Soft Computing, 152, 437–459.
  • Harati, A., Ahmadabadi, M., & Araabi, B. (2007). Knowledge-based multiagent credit assignment: A study on task type and critic information. Systems Journal, IEEE, 1(1), 55–67.
  • Haynes, T., & Sen, S. (1996). Cooperation of the fittest. Paper presented at the Late Breaking Papers at the Genetic Programming Conference, Stanford University, Stanford, CA.
  • Haynes, T.D., & Sen, S. (1997a). Co-adaptation in a team. International Journal of Computational Intelligence and Organizations 1(4), 231–233.
  • Haynes, T., & Sen, S. (1997b). Crossover operators for evolving a team. In Genetic Programming: Proceedings of the Second Annual Conference (pp. 162–167). San Francisco, CA: Morgan Kaufmann.
  • Horling, B., & Lesser, V. (2004). A survey of multi-agent organizational paradigms. The Knowledge Engineering Review, 19(4), 281–316.
  • Iba, H. (1996). Emergent cooperation for multiple agents using genetic programming. In H.-M. Voigt, W. Ebeling, I. Rechenberg, & H.-P. Schwefel (Eds.), Parallel problem solving from nature–PPSN IV ((Vol. 1141, pp. 32–41). Berlin: Springer.
  • Iba, H. (1998). Evolutionary learning of communicating agents. Information Sciences, 108(1), 181–205.
  • Jansen, T., & Wiegand, R.P. (2003). Exploring the explorative advantage of the cooperative coevolutionary (1+ 1) EA. In E. Cantú-Paz, J. Foster, K. Deb, L. Davis, R. Roy, U.-M. O’Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta, M. Potter, A. Schultz, K. Dowsland, N. Jonoska, & J. Miller (Eds.), Genetic and evolutionary computation – GECCO 2003 (Vol. 2723, pp. 197–197). Berlin: Springer.
  • Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Wooldridge, M.J., & Sierra, C. (2001). Automated negotiation: Prospects, methods and challenges. Group Decision and Negotiation, 10(2), 199–215.
  • Jones, J., & Goel, A. (2004). Hierarchical judgement composition: Revisiting the structural credit assignment problem. Proceedings of the AAAI Workshop on Challenges in Game AI (pp. 67–71), San Jose, CA.
  • Kakas, A., & Moraitis, P. (2003). Argumentation based decision making for autonomous agents. Paper presented at the Proceedings of the second international joint conference on Autonomous agents and multiagent systems, Melbourne, Australia.
  • Kosorukoff, A. (2001). Human based genetic algorithm. 2001 IEEE International Conference on Systems, Man, and Cybernetics, 5, 3464–3469.
  • Lee, Z., Wang, Y., & Su, S. (2004). A genetic algorithm based robust learning credit assignment cerebellar model articulation controller. Applied Soft Computing, 4(4), 357–367.
  • Lesser, V.R., Corkill, D.D., & Durfee, E.H. (1987). An update on the distributed vehicle monitoring test bed (Technical Report). Amherst, MA: University of Massachusetts.
  • Luke, S. (1998). Genetic programming produced competitive soccer softbot teams for RoboCup97. In J.R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, & R. Riolo (Eds.), Genetic Programming 1998: Proceedings of the Third Annual Conference (Vol. 1998, pp. 214–222). San Francisco, CA: Morgan Kaufmann.
  • Luke, S., Hohn, C., Farris, J., Jackson, G., & Hendler, J. (1998). Co-evolving soccer softbot team coordination with genetic programming. In H. Kitano (Ed.), RoboCup-97: Robot soccer world cup I (pp. 398–411). Berlin: Springer.
  • Ma, J., Liu, W., & Benferhat, S. (2010). A belief revision framework for revising epistemic states with partial epistemic states. Paper presented at the 24th American National Conference on Artificial Intelligence (AAAI’10), Atlanta, GA.
  • Miconi, T. (2001, July). A collective genetic algorithm. In L. Spector, E.D. Goodman, A. Wu, W.B. Langdon, H.M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M.H. Garzon, & E. Burke (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001) (pp. 876–883). San Francisco, CA: Morgan Kaufmann.
  • Miconi, T. (2003). When evolving populations is better than coevolving individuals: The blind mice problem. Paper presented at the Proceedings of the 18th international joint conference on Artificial intelligence, Acapulco, Mexico.
  • Millman, M. (1983). A statistical analysis of casino blackjack. The American Mathematical Monthly, 90(7), 431–436.
  • Nejad, H.T.N., Sugimura, N., & Iwamura, K. (2011). Agent-based dynamic integrated process planning and scheduling in flexible manufacturing systems. International Journal of Production Research, 49(5), 1373–1389.
  • Nitschke, G.S., Eiben, A.E., & Schut, M.C. (2012). Evolving team behaviors with specialization. Genetic Programming and Evolvable Machines, 13(4), 493–536. doi:10.1007/s10710-012-9166-5.
  • Nunes, L., & Oliveira, E. (2004). Learning from multiple sources. Paper presented at the Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 3, New York, NY.
  • Ontañón, S., & Plaza, E. (2007). Learning and joint deliberation through argumentation in multiagent systems. Paper presented at the Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, Honolulu, Hawaii.
  • Panait, L., & Luke, S. (2005). Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3), 387–434.
  • Quinn, M., Smith, L., Mayley, G., Husbands, P., Quinn, M., Smith, L., Mayley, G., & Husbands, P. (2003). Evolving controllers for a homogeneous system of physical robots: Structured cooperation with minimal sensors. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 361(1811), 2321–2343.
  • Rahwan, I., Parsons, S., & Reed, C. (2008). Argumentation in multi-agent systems. Berlin: Springer-Verlag.
  • Rotstein, N., García, A., & Simari, G. (2008). Defeasible argumentation support for an extended BDI architecture. In I. Rahwan, S. Parsons, & C. Reed (Eds.), Argumentation in multi-agent systems (Vol. 4946, pp. 145–163). Berlin: Springer.
  • Schneider, J.G., Wong, W.-K., Moore, A.W., & Riedmiller, M.A. (1999). Distributed value functions. Paper presented at the Proceedings of the Sixteenth International Conference on Machine Learning, Bled, Slovenia.
  • Sousa, P., Ramos, C., & Neves, J. (2004). The Fabricare system: A multi-agent-based scheduling prototype. Production Planning & Control, 15(2), 156–165.
  • Srivastava, R.P., & Goldberg, D.E. (2001). Verification of the theory of genetic and evolutionary continuation. Paper presented at the Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, San Francisco, CA.
  • Steeb, R., Cammarata, S., Hayes-Roth, F.A., Thorndyke, P.W., & Wesson, R.E. (1981). Distributed intelligence for air fleet control (No. RAND/R-2728-ARPA). Santa Monica, CA: RAND.
  • Sun, R. (2001). Cognitive science meets multi-agent systems: A prolegomenon. Philosophical Psychology, 14(1), 5–28.
  • Sun, R. (2004). Desiderata for cognitive architectures. Philosophical Psychology, 17(3), 341–373.
  • Sun, R. (2005). Prolegomena to integrating cognitive modeling and social simulation. In R. Sun (Ed.), Cognition and multi-agent interaction: From cognitive modeling to social simulation (pp. 3–26). Cambridge, MA: Cambridge University Press.
  • Sutton, R.S. (1984). Temporal credit assignment in reinforcement learning (Ph.D. dissertation). University of Massachusetts, Amherst, MA.
  • Tangamchit, P., Dolan, J., & Khosla, P. (2002). The necessity of average rewards in cooperative multirobot learning. Proceedings. ICRA’02. IEEE International Conference on Robotics and Automation, 2, 1296–1301.
  • Thorp, E.O. (2011). Beat the dealer: A winning strategy for the game of twenty-one. New York, NY: Random House Digital.
  • Vassilev Lakov, D., & Vassileva, M.V. (2005). Decision making soft computing agents. International Journal of Systems Science, 36(14), 921–930.
  • Wajid, U., & Mehandjiev, N. (2006, June). Agent interaction protocols and flexible agent interaction in dynamic environments. Paper presented at the 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, Manchester.
  • Walton, D.N., & Krabbe, E.C. (1995). Commitment in dialogue: Basic concepts of interpersonal reasoning. Albany, New York, NY: SUNY.
  • Wiegand, R.P. (2004). An analysis of cooperative coevolutionary algorithms. (Ph.D. dissertation). George Mason University, Fairfax, VA.
  • Wiegand, R.P., Liles, W.C., & De Jong, K.A. (2001). An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2611, 1235–1245.
  • Wiegand, R.P., Liles, W.C., & De Jong, K.A. (2002a). Analyzing cooperative coevolution with evolutionary game theory. Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC’02, 2, 1600–1605.
  • Wiegand, R.P., Liles, W.C., & De Jong, K.A. (2002b). Modeling variation in cooperative coevolution using evolutionary game theory. Paper presented at the Foundations of Genetic Algorithms (FOGA), Torremolinos, Spain.
  • Wooldridge, M. (2009). An introduction to multiagent systems. San Francisco, CA: Wiley.
  • Wooldridge, M., Bussmann, S., & Klosterberg, M. (1996). Production sequencing as negotiation. Paper presented at the Proceedings of the First International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology (PAAM-96), Salamanca, Spain.
  • Yager, R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man and Cybernetics, 18(1), 183–190.
  • Zutis, K., & Hoey, J. (2009). Who's counting? Real-time blackjack monitoring for card counting detection. In M. Fritz, B. Schiele, & J. Piater (Eds.), Computer vision systems (Vol. 5815, pp. 354–363). Berlin: Springer.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.