Learning and adaptation strategies for evolving artifact capabilities
Keywords:multi-agent systems, social simulations, intelligent agents, learning and adaptation
In this study we address enhancing the ability of social agents embedded in multi-agent based simulations to achieve their goals by using objects in their environment as artifacts. Reformulated as a discrete optimization problem solved with evolutionary computation methods, social agents are empowered to learn and adapt through observations of their own behavior, others in the environment and their community at large. An implemented case study is provided incorporating the model into the multi-agent simulation of the Village EcoDynamics Project developed to study the early Pueblo Indian settlers from A.D. 600 to 1300. Eliminating the existing presumption that agents automatically know the productivity of the landscape as part of their settling and farming practices, agents use the landscape as an artifact, learning to predict its productivity from a few attributes such as the area's slope and aspect. Given the dynamic nature of the landscape and its inhabitants, agents also evolve various combinations of learning strategies adapting to meet their needs. The result is the demonstration of a mechanism for incorporating artifact use learning and evolution in social simulations, leading to the emergence of favorable learning strategies.
. T. Plummer, “Flaked stones and old bones: biological and cultural evolution at the dawn of technology.,” American Journal of Physical Anthropology, vol.125, pp.118–164, 2004.
. T. G. Power. Play and Exploration in Children and Animals. Mahwah, NJ: Lawrence Erlbaum Associates, 2000.
. A. K. Gardiner, D. F. Bjorklund, and M. L. G. ad Sarah K. Gray, “Choosing and using tools: Prior experience and task difficulty influence preschoolers’ tool-use strategies,” Cognitive Development,vol.27, no.3, pp.240–254, 2012.
. K. Neldner, E. Reindl, C. Tennie, J. Grant, K. Tomaselli, and M. Nielsen, “A cross-cultural investigation of young children’s spontaneous invention of tool use behaviours,” Royal Society Open Science, vol.7, no.5, p.192240, 2020.
. K. Bacher, S. Allen, A. K. Lindholm, L. Bejder, and M. Krützen,“Genes or culture: Are mitochondrial genes associated with tool use in bottlenose dolphins (Tursiop sp.)?,” Behavior Genetics, vol.40,pp.706–714, 2010.
. B. Preston, “Cognition and tool use.,” Mind and Language, vol.13,no.4, pp.513–547, 1998.
. A. B. Wood, T. E. Horton, and R. S. Amant, “Effective tool use in a habile agent.,” in Proceedings of the IEEE Systems and Information Engineering Design Symposium (E. J. Bass, ed. ), pp.75–81, IEEE,2005.
. A. Omicini, A. Ricci, and M. Viroli, “Agens Faber: Toward a theory of artefacts for MAS.,” Electronic Notes in Theoritical Computer Sciences, vol.150, no.3, pp.21–36, 2006.
. D. L. Acay, G. Tildar, and L. Sonenberg, “Extending agent capabilities: Tools vs Agents,” in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol.2, pp.259–265, IEEE Computer Society, Los Alamitos, CA, 2008.
. E. Bicici and R. S. Amant, “Reasoning about the functionality of tools and physical artifacts,” Department of Computer Science, North Carolina State University, Tech Rep, vol.22, 2003.
. Y. Wu and Y. Demiris, “Learning dynamical representations of tools for tool-use recognition,” in IEEE International Conference on Robotics and Biometics, IEEE, 2011.
. K. P. Tee, J. Li, L. T. P. Chen, K. W. Wan, and G. Ganesh, “Towards emergence of tool use in robots: Automatic tool recognition and use without prior tool learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), pp.6439–6446, IEEE, 2018.
. A. Stoytchev, “Behavior-grounded representation of tool affordances.,” in Proceedings of the IEEE International Conference on Robotics and Automation, pp.3060–3065, 2005.
. Z. Kobti, A. Snowdon, S. Rahaman, T. Dunlop, and R. Kent, “A cultural algorithm to guide driver learning in applying child vehicle safety restraint,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp.1111–1118, 2006.
. F. Mokom and Z. Kobti, “Evolution of artifact capabilities,” in Proceedings of the IEEE Congress on Evolutionary Computation, (New Orleans, Louisiana), pp.476–483, IEEE Press, June 2011.
. R. Jain and T. Inamura, “Bayesian learning of tool affordances based on generalization of functional feature to estimate effects of unseen tools,” Artificial Life and Robotics, vol.18, no.1-2, pp.95–103,2013.
. S. Brown and C. Sammut, “A relational approach to tool-use learning in robots,” in Inductive Logic Programming, pp.1–15,Springer, 2013.
. F. Mokom and Z. Kobti, “Improving artifact selection via agent migration in multi-population cultural algorithms,” in 2014 IEEE Symposium on Swarm Intelligence, pp.1–8, IEEE, 2014.
. A. Omicini, A. Ricci, and M. Viroli, “Artifacts in the A&A metamodel for multi-agent systems,” Autonomous Agents and Multi-Agent Systems, vol.17, no.3, pp.432–456, 2008.
. E. Bonabeau, “Agent-based modeling: Methods and techniques for simulating human systems,” Proceedings of the National Academy of Sciences of the United States of America, vol.99, no. Suppl 3, pp.7280–7287, 2002.
. N. Gilbert, “Agent-based social simulation: dealing with complexity, ” The Complex Systems Network of Excellence, vol.9, no.25, pp.1–14, 2004.
. H. Jiang, W. Karwoski, and T. Z. Ahram, “Applications of agent based simulation for human socio-cultural behavior modeling,” Work:A Journal of Prevention, Assessment and Rehabilitation, vol.41,pp.2274–2278, 2012.
. A. Drogoul and J. Ferber, “Multi-agent simulation as a tool for modeling societies: Application to social differentiation in ant colonies,” in Artificial Social Systems, pp.2–23, Springer, 1994.
. Z. Kobti, R. G. Reynolds, and T. Kohler, “A multi-agent simulation using cultural algorithms: the effect of culture on the resilience of social systems,” in Proceedings of the IEEE Congress on Evolutionary Computation, vol.3, pp.1988–1995, 2003.
. T. A. Kohler, C. D. Johnson, M. Varien, S. Ortman, R. Reynolds,Z. Kobti, J. Cowan, K. Kolm, S. Smith, and L. Yap, “Settlement ecodynamics in the prehispanic central mesa verde region,” The model-based archaeology of socionatural systems, pp.61–104, 2007.
. S. A. Crabtree, K. Harris, B. Davies, and I. Romanowska, “Outreaching archaeology with agent-based modeling: Part 3 of 3,” Advances in Archaeological Practice, vol.7, no.2, p.194, 2019.
. S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice Hall, 2010.
. F. Mokom and Z. Kobti, “A cultural evolutionary model for artifact capabilities,” in Proceedings of the European Conference on Artificial Life, (Paris, France), pp.542–549, MIT Press, August 2011.
. F. Mokom and Z. Kobti, “Exploiting objects as artifacts in multi agent based social simulations,” in Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp.1869–1870, 2015.
. F. Mokom, “Modeling the evolution of artifact capabilities in multi agent based simulations,”, 2015.
. D. Schlierkamp-Voosen and H. Mühlenbein, “Predictive models for the breeder genetic algorithm,” Evolutionary, vol.1, no.1, pp.25–49,1993.
. C.-J. Chung and R. G. Reynolds, “CAEP: an evolution-based tool for real-valued function optimization using cultural algorithms, ” International Journal on Artificial Intelligence Tools, vol.7, no.3,pp.239–291, 1998.
. Z. Kobti, R. G. Reynolds, and T. Kohler, “The effect of kinship cooperation learning strategy and culture on the resilience of social systems in the village multi-agent simulation,” in Evolutionary Computation, 2004. CEC 2004. Congress on, vol.2, pp.1743–1750,IEEE, 2004.
. R. G. Reynolds, An Adaptive Computer Model of the Evolution of Agriculture for Hunter-gatherers in the Valley of Oaxaca. Ph.D.thesis, Dept. of Computer Science, University of Michigan, 1979.
. B. Peng, R. Reynolds, and J. Brewster, “Cultural swarms,” in Evolutionary Computation, 2003. CEC’03. The 2003 Congress on,vol.3, pp.1965–1971, IEEE, 2003.
. R. G. Reynolds and B. Peng, “Cultural algorithms: Modeling of how cultures learn to solve problems.,” in Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, pp.166–172, IEEE Computer Society, Washington, DC, 2004.
. T. A. Kohler, “The final 400 years of pre-hispanic agricultural society in the mesa verde region,” Kiva, vol.66, pp.191–264, 2000.
. T. A. Kohler, “News from the northern american southwest: Prehistory on the edge of chaos,” Journal of Archeological Research,vol.1, no.4, pp.267–321, 1993.
How to Cite
Authors who submit papers with this journal agree to the following terms.