1. UNDERSTANDING THE BRAIN’S EMERGENT PROPERTIES Don Miner, Marc Pickett, and Marie desJardins Multi-Agent Planning & Learning Lab University of Maryland, Baltimore County March 6, 2008 don1@umbc.edu http://maple.cs.umbc.edu/~don/projects/SAF

2. 2 RULE ABSTRACTION Rule Abstraction: the process of learning correlations between swarm-level properties and low-level parameter values. Enables control of swarms in terms of the swarm-level properties. Enables predictions of emergent behavior from the agent-level parameters. Results: end-user control over swarms, swarm “planners”, richer applications.

3. 3 RULE ABSTRACTION Low-level parameters: Abstract property: Mapping function: Reverse mapping function: The learning problem is defining: and

4. 4 BOIDS “Boids” by Craig Reynolds in 1986 Agents follow three rules: Separation Alignment Cohesion Swarm-level parameters: Density Internal velocity http://www.red3d.com/cwr/boids/ Separation Alignment Cohesion

5. 5 RULE HIERARCHIES Natural extension to rule abstraction: Abstract properties are used as low-level parameters of higher-level abstract properties. Changes anywhere in the hierarchy are propagated throughout.

6. 6 THE MIND AS A RULE HIERARCHY? Looking at the mind as an emergent property of the brain, can we define a rule hierarchy that models intelligence? Can we learn how parameter values of atom-level programs influence emergent properties of the brain? Is there a way to break up the concept of intelligence into sub-emergent properties -- or does it all just emerge in one step? What other benefits are there of thinking of the brain as an emergent system?