1 / 6

Particle Swarm Social Model for Group Social Learning in an Adaptive Environment

Particle Swarm Social Model for Group Social Learning in an Adaptive Environment. Xiaohui Cui † , Laura L. Pullum ‡ , Jim Treadwell † , Robert M. Patton † , and Thomas E. Potok †. † Computational Sciences and Engineering Division Oak Ridge, TN 37831 865-576-9654 cuix@ornl.gov.

kadeem-cooke
Download Presentation

Particle Swarm Social Model for Group Social Learning in an Adaptive Environment

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  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. • 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.

  3. Particle Swarm Social Learning Model in Adaptive Environment • Social Learning • Learning by observing “models” and noting the reward contingencies • Adaptive 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 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 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: 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. (1) (2) (3) Figure 1: The sample landscape environment Figure 2: The step size value map generated by equation (2) with different A value • Experiment Setup: • With following experiment setup, Figure 3 illustrates the initial simulation environment with 20 agent groups. Figure 3: The initial Environment & Agent Group • 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 (b) (a) (b) (a) 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 Figure 4: The collective searching results for scenario (a) one group with 400 agents and scenario (b) twenty groups, 20 agents per group

  5. Preferred Option Gathered Generated Domain of Interest Parallel Domain Real-Time Historical 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 • 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

More Related