Evolving Strategies for Competitive Multi-Agent Search (2306.10640v2)
Abstract: While evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more effectively. Using a real-world problem of innovation search in organizations as the motivating example, this article first formalizes human creative problem solving as competitive multi-agent search (CMAS). CMAS is different from existing single-agent and team search problems in that the agents interact through knowledge of other agents' searches and through the dynamic changes in the search landscape that result from these searches. The main hypothesis is that evolutionary computation can be used to discover effective strategies for CMAS; this hypothesis is verified in a series of experiments on the NK model, i.e.\ partially correlated and tunably rugged fitness landscapes. Different specialized strategies are evolved for each different competitive environment, and also general strategies that perform well across environments. These strategies are more effective and more complex than hand-designed strategies and a strategy based on traditional tree search. Using a novel spherical visualization of such landscapes, insight is gained about how successful strategies work, e.g.\ by tracking positive changes in the landscape. The article thus provides a possible framework for studying various human creative activities as competitive multi-agent search in the future.
- R. E. Korf, “Real-time heuristic search,” Artificial Intelligence, vol. 42, no. 2-3, pp. 189 – 211, 1990. [Online]. Available: http://www.sciencedirect.com/science/article/pii/0004370290900544
- K. O. Stanley and R. Miikkulainen, “Evolving Neural Networks Through Augmenting Topologies,” Evolutionary Computation, vol. 10, pp. 99–127, 2002. [Online]. Available: http://nn.cs.utexas.edu/keyword?stanley:ec02
- D. A. Levinthal and J. G. March, “A model of adaptive organizational search,” Journal of Economic Behavior and Organization, vol. 2, pp. 307–333, 1981.
- R. Katila, “New product search over time: Past ideas in their prime?” Academy of Management Journal, vol. 45, pp. 995–1010, 2002.
- R. Katila, E. Bahceci, and R. Miikkulainen, “Organizing for innovation: Exploratory, ambidextrous and exploitative units in competitive environments,” 2010, working paper.
- D. A. Levinthal, “Adaptation on rugged landscapes,” Management Science, vol. 43, no. 7, pp. pp. 934–950, 1997. [Online]. Available: http://www.jstor.org/stable/2634336
- G. Gavetti and D. Levinthal, “Looking forward and looking backward: Cognitive and experiential search,” Administrative Science Quarterly, vol. 45, pp. 113–137, 2000.
- H. Greve, “A behavioral theory of R&D expenditures and innovations: Evidence from shipbuilding,” Academy of Management Journal, vol. 46, pp. 685–702, 2003.
- C. Helfat, “Evolutionary trajectories in petroleum firm R&D,” Management Science, vol. 40, pp. 1720–1747, 1994.
- H. Greve and A. Taylor, “Innovations as catalysts for organizational change: Shifts in organizational cognition and search,” Administrative Science Quarterly, vol. 45, pp. 54–80, 2000.
- J. Rivkin, “Imitation of complex strategies,” Management Science, vol. 46, pp. 824–845, 2000.
- R. Katila, J. Rosenberger, and K. Eisenhardt, “Swimming with sharks: Technology ventures, defense mechanisms, and corporate relationships,” Administrative Science Quarterly, vol. 53, pp. 295–332, 2008.
- P. Hart, N. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” Systems Science and Cybernetics, IEEE Transactions on, vol. 4, no. 2, pp. 100 –107, july 1968.
- R. E. Korf, “Depth-first iterative-deepening: An optimal admissible tree search,” Artificial Intelligence, vol. 27, pp. 97–109, 1985.
- J. Lohn, D. Linden, G. Hornby, W. Kraus, A. Rodriguez-Arroyo, and S. Seufert, “Evolutionary design of an X-band antenna for NASA’s space technology 5 mission,” in National radio science meeting, 2004, pp. 2313–2316.
- V. K. Valsalam, J. A. Bednar, and R. Miikkulainen, “Developing complex systems using evolved pattern generators,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 2, pp. 181–198, 2007. [Online]. Available: http://nn.cs.utexas.edu/keyword?valsalam:ieeetec07
- H. Hosseini, “The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm,” International Journal of Bio-Inspired Computation, vol. 1, no. 1/2, p. 71, 2009. [Online]. Available: http://www.inderscience.com/link.php?id=22775
- E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: A Gravitational Search Algorithm,” Information Sciences, vol. 179, no. 13, pp. 2232–2248, June 2009. [Online]. Available: http://dx.doi.org/10.1016/j.ins.2009.03.004
- J. Kennedy, R. Eberhart, et al., “Particle swarm optimization,” in Proc. of IEEE international conference on neural networks, vol. 4. Piscataway, NJ: IEEE, 1995, pp. 1942–1948.
- I. Zuckerman and A. Felner, “The MP-MIX algorithm: Dynamic search strategy selection in multiplayer adversarial search,” Computational Intelligence and AI in Games, IEEE Transactions on, vol. 3, no. 4, pp. 316 –331, dec. 2011.
- D. E. Knuth and R. W. Moore, “An analysis of alpha-beta pruning,” Artificial Intelligence, vol. 6, no. 4, pp. 293 – 326, 1975. [Online]. Available: http://www.sciencedirect.com/science/article/pii/0004370275900193
- R. E. Korf, “Multi-player alpha-beta pruning,” Artificial Intelligence, vol. 48, no. 1, pp. 99 – 111, 1991. [Online]. Available: http://www.sciencedirect.com/science/article/pii/000437029190082U
- J. B. Pollack, A. D. Blair, and M. Land, “Coevolution of a backgammon player,” in Proceedings of the 5th International Workshop on Artificial Life: Synthesis and Simulation of Living Systems (ALIFE-96), C. G. Langton and K. Shimohara, Eds. Cambridge, MA: MIT Press, 1996.
- H. Juille and J. B. Pollack, “Dynamics of co-evolutionary learning,” in In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior. MIT Press, 1996, pp. 526–534.
- J. Werfel, M. Mitchell, and J. Crutchfield, “Resource sharing and coevolution in evolving cellular automata,” Evolutionary Computation, IEEE Transactions on, vol. 4, no. 4, pp. 388 – 393, nov 2000.
- K. O. Stanley and R. Miikkulainen, “Competitive coevolution through evolutionary complexification,” Journal of Artificial Intelligence Research, vol. 21, pp. 63–100, 2004. [Online]. Available: http://nn.cs.utexas.edu/keyword?stanley:jair04
- K. Knight, “Are many reactive agents better than a few deliberative ones?” in Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1993, pp. 432–437. [Online]. Available: http://dl.acm.org/citation.cfm?id=1624025.1624086
- J. Epstein, “Agent-based computational models and generative social science,” Complexity, vol. 4, no. 5, pp. 41–60, May 1999.
- J. Holland and J. Miller, “Artificial Adaptive Agents in Economic Theory,” American Economic Review, vol. 81, no. 2, pp. 365 – 71, 1991. [Online]. Available: http://ideas.repec.org/a/aea/aecrev/v81y1991i2p365-71.html
- P. J. Hoen, K. Tuyls, L. Panait, S. Luke, and J. A. L. Poutré, “An overview of cooperative and competitive multiagent learning.” in LAMAS, 2005, pp. 1–46.
- K. Stanley, “Compositional pattern producing networks: A novel abstraction of development,” Genetic Programming and Evolvable Machines, vol. 8, no. 2, pp. 131–162, June 2007.
- H. J. Chiel, R. D. Beer, and J. C. Gallagher, “Evolution and analysis of model CPGs for walking: I. Dynamical modules,” Journal of Computational Neuroscience, vol. 7, no. 2, pp. 99–118, 1999.
- I. Harvey, P. Husbands, D. Cliff, A. Thompson, and N. Jakobi, “Evolutionary robotics: the sussex approach,” Robotics and autonomous systems, vol. 20, no. 2, pp. 205–224, 1997.
- B. Hutt and K. Warwick, “Synapsing variable length crossover: Biologically inspired crossover for variable length genomes,” in Artificial Neural Nets and Genetic Algorithms, D. Pearson, N. Steele, and R. Albrecht, Eds. Springer Vienna, 2003, pp. 198–202. [Online]. Available: http://dx.doi.org/10.1007/978-3-7091-0646-4˙36
- H. Moriguchi and S. Honiden, “Cma-tweann: efficient optimization of neural networks via self-adaptation and seamless augmentation,” in Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference. ACM, 2012, pp. 903–910.
- N. Sigal and B. Alberts, “Genetic recombination: The nature of a crossed strand-exchange between two homologous DNA molecules,” Journal of Molecular Biology, vol. 71, no. 3, pp. 789–793, November 1972. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/4648347?dopt=abstract
- D. E. Goldberg and J. Richardson, “Genetic algorithms with sharing for multimodal function optimization,” in Proceedings of the Second International Conference on Genetic Algorithms, J. J. Grefenstette, Ed. San Francisco: Morgan Kaufmann, 1987, pp. 148–154.
- N. Kohl, K. O. Stanley, R. Miikkulainen, M. Samples, and R. Sherony, “Evolving a real-world vehicle warning system,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2006. [Online]. Available: http://nn.cs.utexas.edu/keyword?kohl:gecco06
- K. O. Stanley, B. D. Bryant, and R. Miikkulainen, “Real-time neuroevolution in the NERO video game,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 6, pp. 653–668, 2005. [Online]. Available: http://nn.cs.utexas.edu/keyword?stanley:ieeetec05
- P. Anderson, “Complexity theory and organization science,” Organization Science, vol. 10, no. 3, pp. 216–232, 1999. [Online]. Available: http://orgsci.journal.informs.org/content/10/3/216.abstract
- G. Gavetti and D. Levinthal, “Looking forward and looking backward: Cognitive and experiential search,” Administrative Science Quarterly, vol. 45, no. 1, pp. pp. 113–137, 2000. [Online]. Available: http://www.jstor.org/stable/2666981
- R. Katila, E. Chen, and H. Piezunka, “All the right moves: How entrepreneurial firms compete effectively,” Strategic Entrepreneurship Journal, 2012, in press.
- J. G. March, “Exploration and exploitation in organizational learning,” Organization Science, vol. 2, no. 1, pp. pp. 71–87, 1991. [Online]. Available: http://www.jstor.org/stable/2634940
- T. Knudsen and D. Levinthal, “Two faces of search: Alternative generation and alternative evaluation,” Organization Science, vol. 18, pp. 39–54, 2007.
- J. Schrum and R. Miikkulainen, “Evolving multimodal networks for multitask games,” in Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2011). Seoul, South Korea: IEEE, September 2011, pp. 102–109, (Best Paper Award). [Online]. Available: http://nn.cs.utexas.edu/?schrum:cig11
- M. E. Alden, “MARLEDA: Effective distribution estimation through markov random fields,” Ph.D. dissertation, Department of Computer Sciences, The University of Texas at Austin, 2007, technical Report AI07-349. [Online]. Available: http://nn.cs.utexas.edu/keyword?alden:phd07
- S. G. Ficici and J. B. Pollack, “Pareto optimality in coevolutionary learning,” in Sixth European Conference on Artificial Life, J. Kelemen, Ed. Berlin: Springer, 2001.
- H. Mühlenbein and R. Höns, “The estimation of distributions and the minimum relative entropy principle,” Evolutionary Computation, vol. 13, no. 1, pp. 1–27, 2005.
- D. Carmel and S. Markovitch, “Opponent modeling in multi-agent systems,” Adaption And Learning In Multi-Agent Systems, pp. 40–52, 1996.