Agent-Based and Complex Systems (ABC’s) for the Knowledge Plane

1. Agent-Based and Complex Systems (ABC’s) for the Knowledge Plane Knowledge plane Van ParunakSven BruecknerJason Ruiter{van.parunak, sven.brueckner, jason.ruiter}@altarum.org Transparent datatransport plane E E E E

2. Overview • The Facts of (Emergent) Life • ABC Toolbox • Dynamics and Cognition

3. Why Finger-Pointing Isn’t Enough New Intuitions from Complex Adaptive Systems: • Every part can work, • yet the whole may not. Reasons: • Interactions • Discrete vs. Mean Field • Topology • Co-Evolution

4. Interactions Examples from Highway Dynamics • Cars and Traffic Jams • Braess’s Paradox Purpose of these examples: recalibrate intuitions.

5. Interactions: Traffic Jams and Cars A traffic jam is NOT a Big Car… (they move in different directions)

6. A Len =10Cap = 1k Len = 50Cap = 6k B C Len = 10Cap = 6k Len =10Cap = 1k Len = 50Cap = 6k D Interactions: Braess’s Paradox A Len = 50Cap = 6k Len =10Cap = 1k Adding capacity to a road network can increase traversal time if drivers optimize individually. B C Len =10Cap = 1k Len = 50Cap = 6k D Equilibrium = Optimum A-D time = 83 Equilibrium  Optimum A-D time = 92 D. Braess. Über ein Paradoxon aus der Verkehrsplanung. Unternehmensforschung, 12:258 - 268, 1969. URL http://homepage.ruhr-uni-bochum.de/Dietrich.Braess/paradox.pdf.

7. Enabling a Closer Look • Feedback • Co-evolution • Thresholds • Phase shifts • Phase transitions • Universality • Power laws • Emergence • Long-Range Dependencies Congestion Instability

8. Overview • The Facts of (Emergent) Life • ABC Toolbox • Dynamical Analysis Detect, Diagnose • Stigmergic Agents Control, Configure • Training vs. Design Configure • Dynamics and Cognition

9. Embedded Systems Science • Formal • Predictive Dealing with Dynamics System Behavior Macroscopic Matter • Multi-level models (abstract to realistic) • Domain-level variables • Experimental platform • Statistical Mechanics: • Formal • Mature • Predictive AORIST Element Behaviors Molecules

10. Example: Specialty Body Shop(Power & Free Conveyor) Lots of Loops & Reentrancy 11 Measure transit times between 12 & 17 Paint Washer 12 Bake Oven E-Coat Tank 25 17

11. 10 1 Transit Time (Days) 0.1 0.01 0.001 1 201 401 601 801 1001 1201 1401 1601 Time Series Index What does the “12-17 Segment” Know? Transit Times between Readers 12 and 17

12. 0.07 0.06 0.05 0.04 t(i) 0.03 0.02 0.01 0 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 t(i-1) Using Real Observations • Takens Theorem (1981): Time-delay plots capture the complete topology of the system trajectory in the underlying (unknown) state space, given • scalar observable (t) whose mapping from the underlying state variables is • continuous through second derivative and • generic (couples all degrees of freedom) (1609 Points from a Real Factory) (100 Random Points)

13. Forms of Deviation Diagonal Core: Congestion Squares: Workstation Stoppage Lower Left: Normal Operation

14. t(1206)-t(1226) t(411)-t(436) Remote Information from Local Data Includes Overlaps Overlapped During (None) a b c d Full Not FullSegment when Downstream Stops

15. N - N  2C N + N  C Load/Capacity Lessons from an Abstract Model N = C + 1 Nonlearning Random Choice Computational Effort  Learning Load   Capacity Nonlearning Random Choice

16. Model Under Study (e.g., Mini-RAG) Analysis: Experimental Platform Analysis tools (e.g., Mathematica) Parameter Sweep, APSE Plots Setup Files Data Re-ports .xml .nb .html Ob-serva-tions Docu-menta-tion

17. Examples for Control • Ants: Path planning • Ants: Brood sorting • Army Ants: Transport teaming • Termites: Nest building • Wasps: Task differentiation • Spiders: Web construction • Birds and Fish: Flocking • Wolves: Surrounding prey • People: Market economy • Taxicabs: Vehicle distribution • No central planning • Indirect interaction thru environment • Simple individual rules • Complex adaptive behavior

18. Ants: Nest Sorting (concen-tration) Stigmergy in Nature Food a. Initial Field b. Final Path Ants: Path Formation (pheromones) Nest Stigmergy: • stigma= “sign”, ergon= “action” • Actions  Signs in the environment  Actions  Signs  … • Analogs to learning, data fusion, communication Wasps: Nest Construction (shape fitting) Parunak, H. V. D. (1997). "’Go to the Ant’: Engineering Principles from Natural Agent Systems." Annals of Operations Research 75: 69-101.

19. watches “Friends” attends TFA Flight School Atta meets Hussein Structure Atta meets Clustering Red Targets Red Air Defense attends TFA Hussein UAV Coordination for Collective Imaging Blue Bomber following emerging potential field 39 Week Forecast Windows Blue Base 20 Week Forecast Windows Dynamics of Supply Networks Path Planning for UAV’s Example Applications Decentralized Pattern Recognition Learning from Natural Swarms… Information Discovery in Mammoth InfoBases

20. A Stigmergic View of the KP AgentState AgentDynamics The Environment • Captures structural information (physical, semantic, connectivity) • Enables locality of agent action • Environment = KP • Agents = • Applications • Packets • Network elements Environment’sDynamics Environment’sState

21. × + g × + b q RTarget GTarget ( ) ( ) ( ( ) ) d + a + 1 ) RThreat r × + b + j + b GNest Dist Evolving Behaviors Ghost equation: Evolved Parameters Hand-Tuned Parameters

22. Overview • The Facts of (Emergent) Life • ABC Toolbox • Dynamics and Cognition

23. Where is Cognition? Classical AI

24. Where is Cognition? Committee Model(Intelligent Agent) Systems

25. Where is Cognition? Swarming Model Intelligent (Agent Systems) Fact: At some level, cognition emerges from the interaction of non-cognitive processes. Hypothesis: Network-level cognition can emerge from appropriately structured interactions of applications, traffic, and network elements. Proposal: One task of the KP is to facilitate, and give us access to, that emergent cognition.