1. WASP-LIKE AGENTS FOR DISTRIBUTED FACTORY COORDINATION Vincent A. Cicirello, Stephen F. Smith December 2001 http://www.ri.cmu.edu/people/cicirello_vincent.html

2. PROBLEMATIC – TRUCK PAINTING (GM) System : n painting booth (paint or change) Pattern : 17 Booth, 14 Colors, 50% to 50% Problems : temperature, pressure, failure Scheduler : central assigns arriving trucks Challenge : all states of the complex system

3. BIDDING – by D.Morley (1986-1991) [Morley1996] “Embracing Complexity” in children – don’t program future states but behavior 1.Truck Arrives – Broadcast Task 2. Selfish Booth’s make bids based on suitability A) Similar Task B) Priority Task C) Any Task 3. Simple scheduler takes highest offer “Chicken Brain”, fewer booth’s, one-in-nine 10% Speed-Up (not always optimal)

4. GRAPPING WASP – AGENTS (Cicirello) 1. Stimuli S created by unprocessed Tasks classified by type and based on time 2. Threshold @ in Wasp’s for each task type (value, the lower the more reactive) 3. Update all thresholds per time unit (plus or minus) based on last queue or current element 4. Competition resolution based on queue length

5. BASIS - E. Bonebau & G. Theraulaz (1) “Division of Labor & Task Allocation” in “Swarm Intelligence from natural to artificial Systems” Biology Ants: Warrior = Worker, depending on Stimuli (Threshold by Age, Cast, Individuality) Fixed Threshold – Model (Graph on page 114) P (@,S) = S n / ( S n + @ n ), n usually = 2

6. BASIS - E. Bonebau & G. Theraulaz (2) Varying Threshold – Model (Specialization) P (@,S) = S n / ( S n + @ n ), n usually = 2 @ = @ - d : for assigned tasks @ = @ + d : for unassigned tasks Mailmen Problem Each mailman with varying thresholds for nine clustered mailing centers (local, neighbor, remote)

7. Cicirello’s Approach – Wasp Model Update Function P (@,S) = S 2 / ( S 2 + @ 2 ) @ = @ - d : for assigned tasks @ = @ + d : for unassigned tasks @ = @ - d k : for idle machines Competition F = 1.0 + T p + T s P (F 1, F 2 ) = F 1 2 / ( F 1 2 + F 2 2 ) pick shortest queue (idle & specialized maybe same)

8. RESULTS – WASP’s Settings : varying request patterns, two items, two and four machines, simulation SHORT QUEUE vs. RANDOM short queue is slightly better than random selection, due to better machine usage (details on page 9) WASP vs. BIDDING Wasp outperforms the bidding system by throughput based on reduced number of changes (specialization)

9. Contributions NOVELTY Shortest Queue better than Random (okay) Wasp selection better then bidding (previous work) LEARNED More consistent results than Morley Specialization reduces the number of unnecessary “color” changes (Morley assigns always the task) Trade – Off : Specialization vs. Adaptation Future Work Improve Adaptation (learn pattern) Parameter Tuning

10. Reference [Morley1993] “Painting Trucks at general Motors”, in Embracing Complexity, pp. 53-58 [Cicirello2001] “Wasp-like Agents for Distributed Factory Coordination”, Technical Paper, Carnegie Mellon Univ. [Theraulaz1998] “Division of Labor and Task Allocation”, in Swarm Intelligence from natural to artificial systems, pp. 107-147