CATALOG DESCRIPTION: This course focuses on the exploration, construction and analysis of multi-agent models. Sample models from a variety of content domains are explored and analyzed. Spatial and network topologies are introduced. The prominent agent-based frameworks are covered as well as methodology for replicating, verifying and validating agent-based models. We use state of the art ABM and complexity science tools. This course can help satisfy the project course and artificial intelligence area course requirement for CS and CIS majors, and satisfy the breadth requirement in artificial intelligence for Ph.D. students in CS. It also satisfies a design course requirement for Learning Sciences graduate students, counts towards the Cognitive Science specialization and as an advanced elective for the Cognitive Science major.

Course Coordinator: Uri Wilensky

Required Textbooks: None. Course reading packet.

Course Goal: Construct and analyze multi-agent models in both spatial and network topologies

Prerequisites: None for 372. 472 requires graduate school enrollment.

Detailed Course Topics:

  • What is an agent?
  • Stationary and moveable agents
  • Interactions between agents
  • Agent topologies
  • Properties of networks
  • Applications of ABM
  • Artificial Life
  • Comparison with Systems Dynamics Models
  • Integration of Machine Learning
  • Evolutionary computation
  • Systematic exploration of model parameter space
  • Verification of model specification
  • Replication of models
  • Validation of models
  • Connecting ABM with physical devices
  • Sensors and motors
  • Combining human and virtual agents
  • Participatory simulations

Grades:

  • No exams for this class
  • 20% Participation
  • 30% Homework
  • 50% Final Project

Course Objectives for students:

When a student completes this course, he/she should be able to:

  • • Identify core mechanisms of novel agent-based models
  • • Identify trade-offs in the design and use of agent topologies
  • • Construct original multi-agent models
  • • Use behavior run and analysis tools to analyze model parameter space
  • • Verify and validate agent-based models
  • • Apply agent-based modeling to both scientific and everyday phenomena