1. Xiaohui Cui †, Laura L. Pullum ‡, Jim Treadwell †, Robert M. Patton †, and Thomas E. Potok † Particle Swarm Social Model for Group Social Learning in an Adaptive Environment ‡ 3333 Pilot Knob Road Eagan, MN 55121 651-456-3247 Laura.L.Pullum@lmco.com † Computational Sciences and Engineering Division Oak Ridge, TN 37831 865-576-9654 cuix@ornl.gov

2. Research Overview Integrate particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the social learning of self-organized groups and their collective searching behavior in an adaptive environment.Integrate particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the social learning of self-organized groups and their collective searching behavior in an adaptive environment. Apply the particle swarm metaphor as a model of social learning for a dynamic environment. Provides an agent- based simulation platform for understanding knowledge discovery and strategic search in human self-organized social groups.Apply the particle swarm metaphor as a model of social learning for a dynamic environment. Provides an agent- based simulation platform for understanding knowledge discovery and strategic search in human self-organized social groups. Investigate the factors that affect the global performance of the whole social community through social learning.Investigate the factors that affect the global performance of the whole social community through social learning.

3. Particle Swarm Social Learning Model in Adaptive Environment Social LearningSocial Learning –Learning by observing “models” and noting the reward contingencies Adaptive EnvironmentAdaptive Environment –“Models” in the environment change in each time step. The change can be linear or random. –Highly rewarded “models” dynamically change in every time-step. The observed highly rewarded “models” by learner in time t1 may not be rewarded “models” in time t2. –Change patterns of the environment are influenced by the collective behavior of the learner groups when this collective behavior is effective enough to alter the environment Particle Swarm algorithmParticle Swarm algorithm –Developed in 1995 by James Kennedy and Russ Eberhart –Inspired by social behavior of bird flocking –Applies the concept of social interaction to problem solving –Been applied to a wide variety of search and optimization problems Particle swarm social learning modelParticle swarm social learning model –Every particle is considered as a human group –Particles interact with each other –The group can learn skills and behaviors by observations –Particles are more likely to imitate models whose behavior is rewarded –Particle also has a memory of its behavior history (e.g., people can learn from their own experiences) Personal Cognition Social Adaptive Learning

4. Experiment & Results Adaptive Environment:Adaptive Environment: A two dimensional DF1 equation (1) is used to produce the dynamic environment. The environment change rate is controlled through the logic equation (2). The adaptive mechanism of the environment is represented by equation (3). The fitness value of the solution gradually decreases when an increasing number of the group members search for problem solution in the neighbor area. Figure 2: The step size value map generated by equation (2) with different A value Figure 1: The sample landscape environment Experiment Setup: With following experiment setup, Figure 3 illustrates the initial simulation environment with 20 agent groups. Figure 4: The collective searching results for scenario (a) one group with 400 agents and scenario (b) twenty groups, 20 agents per group (a) (b) Figure 3: The initial Environment & Agent Group Figure 5: The comparison of the average fitness values of (a) each simulation iteration for group scenario a and b (b) whole simulation for different agent group scenarios (a) (b) Experiment Results:Experiment Results: Results from the simulation have shown that effective communication is not a necessary requirement for self organized groups to attain higher profit in an adaptive environment (1)(2) (3)

5. Verification and Validation Verification requires rigorous & standardized test problems, benchmarks – –Benchmark problems: moving parabola problem, moving peaks benchmark function, & DF1; DEFEAT Test Environment – –Formal Methods used for NASA ANTS verification HistoricalReal-Time Parallel DomainDomain of Interest GeneratedGathered OptionPreferred Validation – –Compare agent-based simulation/system (ABS/S) output with real phenomenon – –Compare ABS/S results with math model results – –Dock with other simulations of same phenomenon Data Validation Sources & Types

6. Conclusions & Future Direction The dynamics of the real world are influenced by the collective actions of social groups The changes of the real world impact the social groups’ actions and structure The Particle swarm social learning model is developed to simulate the complex interactions and the collective searching of the self-organized groups in an adaptive environment Next steps: – –More sophisticated models Reaction, perception of environment – –Access to more real data Increased validation of models – –Extend application to related domains