Swarm intelligence is an innovative computational and behavioral metaphor for solving distributed problems that originally took its inspiration from the biological examples provided by social insects such as ants, termites, bees, and wasps and by swarming, flocking, herding, and shoaling phenomena in vertebrates. The abilities of such natural systems appear to transcend the abilities of the constituent individual agents.

The problems social insects and swarms of vertebrates solve - for instance, discovering new food sources, dividing labor among nestmates, building sophisticated nests, reliably migrating over thousands of miles, coordinated maneuvering within narrow spaces, and, in general, robustly facing changes in the team composition and external challenges - have important counterparts in engineering and computer science.

Our goals in this course will be to understand the underlying principles of collective behavior in natural systems by examining key biological concepts (e.g., self-organization, stigmergy, collective movements, foraging, trail-laying and following, and task allocation and division). We will also consider extensions and applications of these concepts to problems in engineering and computer science.