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Soar Agents & Emergence. Lessons Learned from Dealing with Large-Scale Agent Systems S. Brueckner & H.V.D. Parunak. Emergence is Inevitable. A Fact of Life:
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Soar Agents & Emergence Lessons Learned from Dealing with Large-Scale Agent Systems S. Brueckner & H.V.D. Parunak
Emergence is Inevitable • A Fact of Life: • If a system comprises many interacting agents with non-linear behavior, then emergence of system-level features (e.g., persistent, large-scale patterns) is inevitable. • Engineering Challenge: • At least: Ensure that emergence does not hurt our desired functionality. • Optionally: Draw on engineered emergence to achieve design goals. ArchitectureFeatures : Emergence of System-LevelFeatures not Explicitly“Coded” into Agent Behavior ManyAgents NonlinearBehavior ContinuousInteractions
Use-Case 1: Prevent Negative Emergence Many SoarAgents • If we had MANY Soar Agents, we need to ensure that no undesired system dynamics emerge! • Drawing on analytic frameworks such as SM and concepts such as Universality, we can formally describe and analyze emergent effects such as system-level phase dynamics ArchitectureFeatures Concepts& Tools Relationto Soar UseCases : : : : SystemAnalysis Emergence of System-LevelFeatures not Explicitly“Coded” into Agent Behavior StatisticalMechanics Universalityin Physics ManyAgents NonlinearBehavior ContinuousInteractions
Use-Case 2: Create Cheap Realism Fewer SoarAgents • Assume we deploy Soar agents in a simulation model for cognitively realistic behavior and the ability to interrogate their decision logic • We can enhance the realism of the environment of these agents (and thus the realism of their behavior) by adding “clutter” agents with simple heuristics (e.g., crowd models) • Successfully demonstrated for TRAC Monterey ArchitectureFeatures Concepts& Tools Relationto Soar UseCases : : : : RealisticEnvironment Emergence of System-LevelFeatures not Explicitly“Coded” into Agent Behavior Universalityin Physics ManyAgents NonlinearBehavior ContinuousInteractions
Use-Case 2: Create Cheap Realism Cognitively RealisticAgent Model Simple & OpaqueAgent Model(e.g., crowd, traffic, POL) ScenarioBackground
H. V. D. Parunak, P. E. Nielsen, S. Brueckner, and R. Alonso. Hybrid Multi-Agent Systems. In Proceedings of the FourthInternationalWorkshop on Engineering Self-Organizing Systems (ESOA'06), Hakodate, Japan, Springer, 2006.http://abcresearch.org/papers/ESOA06Hybrid.pdf. Use-Case 3: Pre-Process Massive Data Fewer SoarAgents • Self-organizing agent systems with emergent properties can digest massive data (large, noisy, heterogeneous, streaming) • Emergent data structures can serve as enriched input to cognitive reasoning • Successfully demonstrated in DARPA RAID ArchitectureFeatures Concepts& Tools Relationto Soar UseCases : : : : Data Pre-Processing Emergence of System-LevelFeatures not Explicitly“Coded” into Agent Behavior ManyAgents NonlinearBehavior ContinuousInteractions
Use-Case 3: Pre-Process Massive Data High-LevelData Analysis Knowledge EmergentData Models Information Low-LevelData Fusion Data MassiveRaw Data
Conclusion: Analyzing and Engineeringfor Emergence Helps Soar Many SoarAgents Fewer SoarAgents ArchitectureFeatures Concepts& Tools Relationto Soar UseCases : : : : SystemAnalysis Data Pre-Processing RealisticEnvironment Emergence of System-LevelFeatures not Explicitly“Coded” into Agent Behavior StatisticalMechanics Universalityin Physics ManyAgents NonlinearBehavior ContinuousInteractions
