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Cultural Algorithms: Incorporating Social Intelligence into Virtual Worlds

Dr. Robert G. Reynolds
Professor Computer Science
Wayne State University
Detroit, Michigan 48202
Associate Research Scientist
Museum of Anthropology
University of Michigan-Ann Arbor

A. Goal of the Tutorial:

The intended audience will be those students and practitioners who are interested in adding social intelligence into virtual worlds. Attendees will be provided with concepts and software tools that will illustrate how to design in cultural knowledge and social behavior into virtual worlds. Currently intelligence within virtual worlds is often at the level of an individual agent. This tutorial is unique in that it demonstrates the ease with which social intelligence can be integrated into a system, especially ones with multiple objectives, and the resultant advantages of doing so in terms of virtual world performance.

B. Content

  1. What are Cultural Algorithms:
    A basic description of the Cultural Algorithm Framework and its relationship to other socially motivated learning technologies will be described along with an introduction to the Cultural Algorithms Toolkit, CAT 3.1 that supports multi-objective problem solving.

  2. Why Cultural Algorithms work:
    Here we discuss the basic phases of the problem solving process in Cultural Algorithms and how those phases emerge from the interaction of the knowledge sources in the belief space, knowledge swarms, and the population of problem solvers in the population space. Convergence properties will be discussed.

  3. When will Cultural Algorithms work?
    Since Cultural Algorithms derive their power from the emergence of knowledge and population swarms, what problems are suitable for solution with Cultural Algorithms and what problems will be hard or deceptive?

  4. Virtual Worlds:
    a. The Basic Components 
    b. Opportunities for Socially Motivated Learning in Virtual Worlds.
           1. Avatar design.
           2. Collective movement.
           3. Group decision making.
           4. Level design.

  5. Application Examples:
    a. Game Worlds.
           1. Platform Games: Super Mario
           2. 3D Racing Games
           3. Real Time Strategy Games: Starcraft
     b. Real Worlds
           1. Emergence of Urban Centers
           2. The Ancient Land Bridge (Prehistoric Hunter-Gatherers)
           3. Vanishing Societies
           4. Health Care Applications
           5. Sustainable Cultures.

C. Tutorial Description

Cultural Algorithms were developed by Reynolds as a computational framework in which to embed social learning in an evolutionary context [1979]. Unlike traditional learning approaches Cultural Algorithms derive their power from large collections of interacting agents. Within virtual worlds such as games or other interactive digital entertainment systems it is often the case that we wish to coordinate the behavior of large groups of intelligent agents in an efficient fashion. This tutorial focuses on the ability of Cultural Algorithms to perform large-scale group learning within these virtual worlds. They have been used to generate socially intelligent controllers and group social behavior in various gaming genres, both serious and fun. This tutorial describes Cultural Algorithms and how they can be used to incorporate social intelligence into a virtual world using examples form a variety of genres. A toolkit based upon the Cultural Algorithms paradigm will be presented and used as a basis for developing example applications.



Dr. Robert G. Reynolds received his Ph.D. degree in Computer Science, specializing in Artificial Intelligence from the University of Michigan, Ann Arbor. He is currently a professor of Computer Science and director of the Artificial Intelligence Laboratory at Wayne State University. He is an Adjunct Associate Research Scientist with the Museum of Anthropology at the University of Michigan-Ann Arbor, a member of the Complex Systems Group at the University of Michigan-Ann Arbor, and is a participant in the University of Michigan –Wayne state University NSF IGERT program on Incentive-Based Design. His interests are in the development of computational models of cultural evolution for use in the simulation of complex organizations and in computer gaming applications.

Dr. Reynolds produced a framework, Cultural Algorithms, in which to express and computationally test various theories of social evolution using multi-agent simulation models. He has applied these techniques to problems concerning the location of ancient hunting sites beneath Lake Huron, the origins of the state in the Valley of Oaxaca, Mexico, the emergence of prehistoric urban centers, the origins of language and culture, and the disappearance of the Ancient Anazazi in Southwestern Colorado using game programming techniques. He has co-authored three books; Flocks of the Wamani (1989, Academic Press), with Joyce Marcus and Kent V. Flannery; The Acquisition of Software Engineering Knowledge (2003, Academic Press), with George Cowan; and Excavations at San Jose Mogote 1: The Household Archaeology with Kent Flannery and Joyce Marcus (2005, Museum of Anthropology-University of Michigan Press).

He has received funding from both government and industry to support his work. He has published over 250 papers on the evolution of social intelligence in journals, book chapters, and conference proceedings. The journals include IEEE Computer, IEEE Computational Intelligence, Complexity, Scientific American, IEEE Transactions of Evolutionary Computation, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Software, Communications of the ACM, and the Proceedings of the National Academy of Sciences. He is currently an associate editor for the IEEE Transactions on Artificial Intelligence in Games, IEEE Transactions on Evolutionary Computation, International Journal of Swarm Intelligence Research, International Journal of Artificial Intelligence Tools, International Journal of Computational and Mathematical Organization Theory, International Journal of Software Engineering and Knowledge Engineering, and the Journal of Semantic Computing. He serves on the IEEE Technical Committee on Evolutionary Computation, the IEEE Technical Committee on Computational Intelligence in Games, and the IEEE Systems, Man, and Cybernetics technical committee on Soft Computing. He was also a program co-chair for the 2008 IEEE World Congress on Computational Intelligence, program co-chair for 2008 IEEE Swarm Intelligence Symposium, and on the Advisory Board for the International Swarm Intelligence Symposium (2007).

Recently his groups from the Artificial Intelligence Laboratory have designed several award winning game controllers using Cultural Algorithms. They won the IEEE Super Mario Competition at the 2010 IEEE World Congress on Computational Intelligence and placed 2nd in the IEEE Car Racing Competition at the 2008 IEEE World Congress on Computational Intelligence among others.


The length of the tutorial:
two hours.


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