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Programmed Graph Rewriting with Time for Simulation-Based Design

Programmed Graph Rewriting with Time for Simulation-Based Design. Eugene Syriani Hans Vangheluwe. Programmed Graph Rewriting with Time for Simulation-Based Design. Eugene Syriani Hans Vangheluwe. Overview. Background In the context The Modular Timed Graph Transformation (MoTiF) language

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Programmed Graph Rewriting with Time for Simulation-Based Design

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  1. Programmed Graph Rewriting with Time for Simulation-Based Design Eugene Syriani Hans Vangheluwe

  2. Programmed Graph Rewriting with Time for Simulation-Based Design Eugene Syriani Hans Vangheluwe

  3. Overview • Background • In the context • The Modular Timed Graph Transformation (MoTiF) language • Case Study: Reactive PacMan Game • Modelling, Simulation and Synthesis • Modelling of transformation and its environment • Simulation for optimization • Synthesis of application • Conclusion and Future Work

  4. In the context • Model Transformation  Graph Transformation • Controlled Graph Rewriting (rewriting rules) • Control Primitives : Sequencing, Branching, Looping, Parallelism, Hierarchy • Time is inherent in our transformation models Modelling, Simulation and Synthesis

  5. In the context Model of System Model of Environment Simulation Experiments Synthesis of Application

  6. Motif: Semantic Domain • The Discrete Event System Specification [1] (DEVS) formalism • Highly compositional simulation framework • DEVS: • Blocks • Ports • Events • Semantics: Parallel composition of blocks/models [1] Zeigler B.P., Multifacetted Modelling and Discrete Event Simulation/ Academic Press, London (1984)

  7. Motif: Semantic Domain • Atomic DEVS: • Time Advance • Output Function • Internal Transition • External Transition ATOMIC

  8. Motif: Semantic Domain Coupled DEVS C1 A1 C2 A2

  9. Motif [2] • Blocks • Atomic: encapsulate a graph rewriting rule • Coupled: encapsulate a set of rules (graph grammar) • Events • Inport: receive the host graph • Outport(s): send the transformed graph [2] Syriani E. and Vangheluwe H.: Programmed Graph Rewriting with DEVS. AGTIVE 2007, LNCS (2008)

  10. MotifModelling Environment in AToM3 [3] [3] de Lara J., Vangheluwe H., AToM3: A tool for multi-formalism and meta-modelling. FASE, LNCS 2306 (2002), 174-188

  11. Motif Execution class Pacmaneat: def match(): … def execute(): … class Pacmanmoveri: def match(): … def execute(): … class Pacmandie: def match(): … def execute(): … COMPILE IMPORT class Kill(ARule): def __init__(self): ARule.__init__(self, name=‘Kill') self.state = ARuleState(Pacdie()) def weightFunction(self): return 1.0 SIMULATE GENERATE

  12. Case Study: Reactive PacMan Game Simplified PacMan formalism [4] [4] Heckel R., Graph Transformation in a nutshell. FoVMT, ENTCS 148 (2006), 187-198

  13. 1 1 Case Study: Reactive PacMan Game 4 4 2 2 Build the Graph Rewriting Rules 5 pacLink pacLink 3 3 6 foodLink 1: Match[1].score + 1 5 pacLink 1 1 4 2 2 3 3 ghostLink ghostLink

  14. Case Study: Reactive PacMan Game Build the Graph Rewriting Rules ghostLink ghostLink 6 1 2 1 2 5 3 3 gridLeft gridLeft 4 4 pacLink pacLink 5 1 2 1 2 6 3 3 gridRight 4 gridRight 4

  15. Model of System Model of Environment Simulation Experiments Synthesis of Application

  16. Modelling of The transformation: System • User – Controller – Autonomous loop • Feed-back to User • On User interrupt: User Controlled • Feed-back to User User Controlled Autonomous

  17. Modelling of The transformation: Autonomous CRule Priorities

  18. Modelling of The transformation: GhostMoveCRule Decider finds the next move for the ghost Decider consumes time

  19. Modelling of transformation: System User Controlled Autonomous

  20. Modelling of The transformation: UserControlledCRule Conditional rule execution

  21. Modelling of The transformation: System User Controlled Autonomous

  22. Modelling of environment: User CoupledBlock • Customization • Modularity User Behavior User Interaction

  23. Model of the player: STRATEGY class Key: up = 'u' down = 'd' left = 'l' right = 'r' keys = [up, down, left, right] def nextRandom(self): import random return random.Random().choice(Key.keys) Smart Dummy Random

  24. Modelling of environment: UserBehaviorCoupledBlock Back-tracking Collector consumes time

  25. Modelling of environment: TryMoveCRule pacLink ghostLink pacLink 6 1 2 1 2 1 5 8 3 3 7 gridRight 4 gridRight 4 • Non-determinism

  26. Model of the player: SPEED Player reaction time [5]: • Thinking time • Motorics time • Slow • Normal • Fast • Very Fast [5] Zaitev A.V., Skorik Y.A., Mathematical description of sensorimotor reaction time distribution, Human Psychology 28(4) (2002), 494-497

  27. Model of System Model of Environment Simulation Experiments Synthesis of Application

  28. Why do we need simulation? We want to build a “playable” game • A.I. Strategy Random – Smart – Very Smart • Speed Too Slow – … – Too Fast Fix Strategy: Very Smart Find optimal speed

  29. SIMULATION results Finding the optimal game speed

  30. Model of System Model of Environment Simulation Experiments Synthesis of Application

  31. Synthesis of application • Synthesis of a reactive game • Discard the UserBehavior block • Runs on a web browser using AJAX and SVG • Real-time model transformation User Interaction User Behavior User Interaction

  32. Summary MoTiF allows for • Modelling and Simulation-Based Design • Graph transformation rules • Timed transformation system • User • Synthesis of applications • Reactive • Real-time Model Everything User Controlled Autonomous

  33. future work • Model A.I. behavior: backtracking  • Re-use and parametrisation of transformation models • MORE COMPLEX EXAMPLES

  34. ?

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