DR. ROBERT G. REYNOLDS (Wayne State University)
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 individual agents, This tutorial is unique in that it demonstrates the ease with which social intelligence can be integrated into a system and the resultant advantages of doing so in terms of virtual world performance.
B. History: Previous Venues and audience size for previous tutorials
“Tutorial: Cultural Algorithms: Harnessing the Power of Social Intelligence”, IEEE World Congress on Computational Intelligence, Barcelona, Spain, July 16, 2010. (Audience size 75-100)
“Cultural Algorithms: A Tutorial”, IEEE World Congress on Computational Intelligence, June 1-6. 2008. (Audience size 75)
“Cultural Algorithms: A Tutorial”, IEEE Spring Symposium, Honolulu, Hawaii, April 1-5, 2007. (Audience size 75)
“Cultural Algorithms: A Tutorial”, IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, May 15, 2006. (25)
C. 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 3.0.
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. Platform Games: Super Mario
b. 3D Racing Games
c. Real Time Strategy Games: Starcraft
d. Serious Games
1. Emergence of Urban Centers
2. The Ancient Land Bridge (Prehistoric Hunter-Gatherers)
D. 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.
The length of the tutorial is two hours.
