ParCeL-5/ParSSAP: A Parallel Programming Model and Library for Easy Development and Fast Execution of Simulations of Situated Multi-Agent Systems Stéphane Vialle Eugen Dedu Claude Timsit SNPD’02 Charles Hermite Center (France) ERSIDP Supélec (France)

2 1 - Motivations 1 - Situated MAS with fine modeling: complex to program (ex: simultaneity of agent actions and conflict resolutions) 2 - Fine or large situated MAS: very time consuming (for simultaneity modeling, fair conflict resolution, …) 3 - MAS: natural parallelism different of computer parallelism (efficient adaptation is difficult)  We need: Parallel programming models allowing easy developments Parallel development tools on modern MIMD parallel computers ?? C-library ….

3 2 - ParCeL-5 Model: Basic Components 4 basic components : Environment, Resources, Agent/Cell, Arbitrator 1 - The Environment : 2D-Grid or 2D-Torus (torus = circular grid) 4-connectivity or 8-connectivity Only one agent per box An obstacle entirely fills a box 2D-Grid2D-Torus 4-connectivity8-connectivity

4 2 - ParCeL-5 Model: Basic Components 4 basic components : Environment, Resources, Agent/Cell, Arbitrator 2 - The Resources : Resources store or consume some objects (ores) Any number of resources Any number of kind of resources (any kind of ores) Only one resource per box, but an agent can still enter the box Mines Factories

5 2 - ParCeL-5 Model: Basic Components 4 basic components : Environment, Resources, Agent/Cell, Arbitrator 3 - The Agents/Cells : Local memory Perception of their local environment Private behavior (static) They are mobile agents : Declared before running Dynamically created or destroyed : Local memory (dynamically extensible) Priority and Speed Percepts of the environment Behavior function Agent skeleton later

6 2 - ParCeL-5 Model: Basic Components 4 basic components : Environment, Resources, Agent/Cell, Arbitrator 4 - The Arbitrator : A set of rules modeling the physical law of the environment Automatically applied: when agents attempt to act when environment is updated Manages all conflicts, with fairness and random choices Arbitrator Conflict ! Winner agent

7 2 - ParCeL-5 Model: Agent Behaviors Agents: Perceive their local environment Act on their local environment Actions on env.: move, take/drop objects, Actions on agents: create/destroy other agents LOCAL actions Unlimited number of percepts But any percept must be LOCAL Direct vision Potential field detection, …. Potential field detection Direct vision Agent Behavior: Agent Percepts: Agent actions:

8 2-ParCeL-5 Model: Cyclic Running Step 1: Activation of agent behaviors, declaration of action intentions Simulation beginning Simulation end Parallel initialisation step (environment, resources, percepts) Step 2: Resolution of agent conflicts (by the arbitrator) Step 4: Execution of parallel or sequential end-cycle function (user) Step 5: Update of the environment and agent percepts Simulation loop Step 3: Execution of actions of winners and non-conflicting agents parallelsequential  Mainly Parallel

9 2-ParCeL-5 Model: Easy Situated MAS Developments Behavior functions ParCeL-5 model User Agent run Conflict management Environment update Cyclic running … Parallelization … ParCeL-5 library

Parallelization: Strategy Domain partitioning strategy SPMD paradigm Multithreading implementation on shared memory Synchronization barriers Environment: domain partitioning Shared Memory PE-0PE-1PE-2 PE-3 Optimized parallel and sequential design of : percept management environment update Classical Specific and Optimized

Parallelization: Wave Potential Field Propagation Agent following an increasing potential path Agent out potential field Potential field frontier 5 Medium source 7 Strong source Obstacle Potential propagation principles : Potentials emitted by resources (function of their features) Spread, decrease, and bypass obstacles Detected by agents (to avoid obstacles and reach resources) Fast and sufficient composition of potential fields (max( ) operator)

Parallelization: Wave Potential Field Propagation Parallelization : Automatic parallel use from agent behaviors Explicit parallel update: Domain decomposition Local propagation Frontier sharing ; Re-propagation ; Until no change Several sequential algorithms  Several complex combinations in parallel algorithm Potential field Potential source Obstacle P0 slice P1 slice Frontier Complex potential propagation

Parallelization: Wave Potential Field Propagation Ex : 1024x1024 boxes, resources, 0 obstacle, SGI-Origin2000 Breadth first recursive method: most evolved, less efficiently implemented re-propagation & numerous obstacles Regular loop iterative method: less evolved, most efficiently implemented propagation re-propagation & numerous potential sources

14 4 – Applications and Global Perfs 3 - Society of competitive selfish agents : Ishida – Yokoi – Kakazu, ECAL’99 One agent : does nothing or cooperates = f(random, agent behaviors)  How many cooperate ? Same results than original simulations Correct situated MAS simulator 1 - Carrier robot simulation : 1024 x 1024 boxes, 100 cycles – agents Agents catch and carry some ores Search efficient carrier population S(32) = 15 (SGI) S(4) = 2.7 (SGI) – 3.5 (SUN) Efficient parallel runs % of cooperative agents time Stabilization phenomena 2 – Game of life (Conway) : Immobile agents … Possible with ParCeL-5 !

Conclusion and Future Works Present results of ParCeL-5/ParSSAP: Good perfs : on high cost SGI-Origin2000: S(32) = 15 on small 4-processor SUN: S(4) = 3.5 Allows quick developments of situated MAS Runs simulations with more than agents Open parallel programming model : vision, potential, … In the future …. Add direct communication between agents Add heuristic choice of wave propagation algorithms Continue to build large situated MAS for emergence search Optimize for low cost multiprocessor PC

16 ParCeL-5/ParSSAP: A Parallel Programming Model and Library for Easy Development and Fast Execution of Simulations of Situated Multi-Agent Systems Questions ?