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Nitin Kumar Yadav RMIT University, Melbourne nitin.yadav@student.rmit.au

Nitin Kumar Yadav RMIT University, Melbourne nitin.yadav@student.rmit.au

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Nitin Kumar Yadav RMIT University, Melbourne nitin.yadav@student.rmit.au

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  1. Implementation and analysis of simulation based techniques for behavior composition Nitin Kumar Yadav RMIT University, Melbourne nitin.yadav@student.rmit.edu.au Minor thesis for semester 2, 2009, under the supervision of Dr. Sebastian Sardina, RMIT university

  2. Implementation and analysis of simulation based techniques for behavior composition Behavior Composition Simulation Techniques Implementation Analysis Contents

  3. Behavior Composition What is a behavior ? • Behavior • Logic of a machine • Web service • Stand alone component • Abstracted as finite transition systems • Available behaviors can be non-deterministic B1 B2

  4. Behavior Composition Combining available behaviors to realize a target behavior Available behaviors B1 B2 Can we realize T1 by composing B1 and B2 ? Target behavior (virtual) T1

  5. Behavior Composition Combined finite transition system of available behaviors B1 B2 Asynchronous product of B1 and B2 ‘composed’ transition system of available behaviors

  6. Behavior Composition Combined finite transition system of available behaviors B1 B2 Asynchronous product of B1 and B2 ‘composed’ transition system of available behaviors

  7. Behavior Composition Combined finite transition system of available behaviors B1 B2 Asynchronous product of B1 and B2 Can this behave like the target system ?

  8. Simulation • A transition system T1 simulates another transition system T2 iff T1 can ‘mimic’ all the states of T2 • A state in the available system mimics another state in the target system if: • It can do all the actions that the target state can do • The successor state in the available system as a result of such an action simulates the resulting state in the target system • Simulation is a relation of states of the composed system and the states of the target behavior which can be ‘mimicked’.

  9. Simulation Example t Simulation Relation {<S1,S1>, <T1>} {<S2,S1>, <T2>} … simulation relation is a solution to the behavior Composition problem ! How to calculate it ? Available behaviors Target System

  10. Techniques Two approaches for behavior composition • Regression based approach [Sardina,Patrizi & De Giacomo, KR 2008] • Progression based approach [Stroeder & Pagnucco, 2009, IJCAI 2009] Proceedings of Principles of Knowledge Representation and Reasoning (KR), pages 640-650, Sydney, Australia, September 2008. AAAI Press. Accepted for the IJCAI 2009

  11. Techniques Regression based approach [Sardina, Patrizi, De Giacomo] • Assume each state in the available system simulates each state in the target system • Iteratively remove non-conformant links which don’t’ follow the simulation definition i.e., • Can not perform the actions which can be requested in the matching target state • The successor state of the action does not follow the above rule • Stop when no more links can be removed

  12. Regression based approach Example t Available behaviors Assume each state from available behaviors simulates each state In the target system {<S1,S1>, <T1>} {<S1,S1>, <T2>} {<S1,S1>, <T3>} {<S2,S1>, <T1>} {<S2,S1>, <T2>} {<S2,S1>, <T3>} {<S2,S2>, <T1>} {<S2,S2>, <T2>} {<S2,S2>, <T3>} {<S1,S2>, <T1>} {<S1,S2>, <T2>} {<S1,S2>, <T3>} Target System

  13. Regression based approach Example Each Cycle : step 1 – remove the States which can not perform the Actions of the linked target state t Available behaviors {<S1,S1>, <T1>} {<S1,S1>, <T2>} {<S1,S1>, <T3>} {<S2,S1>, <T1>} {<S2,S1>, <T2>} {<S2,S1>, <T3>} {<S2,S2>, <T1>} {<S2,S2>, <T2>} {<S2,S2>, <T3>} {<S1,S2>, <T1>} {<S1,S2>, <T2>} {<S1,S2>, <T3>} Target System

  14. Regression based approach Example Each Cycle : step 2 – remove the States whose successor states are not in the simulation relation t Available behaviors {<S1,S1>, <T1>} {<S1,S1>, <T2>} {<S2,S1>, <T1>} {<S2,S1>, <T2>} {<S2,S2>, <T1>} {<S2,S2>, <T2>} {<S2,S2>, <T3>} {<S1,S2>, <T1>} {<S1,S2>, <T3>} X Continue till no more links can be removed Target System

  15. Techniques Progression based approach [Stroder & Pagnucco] • Start from the initial state • Iteratively addconformant links between the states of the composed system and the target system • Stop when no more links can be added

  16. Progression based approach Example t Available behaviors Start from states those ‘can Mimic the initial state {<S1,S1>, <T1>} {<S2,S1>, <T1>} {<S2,S2>, <T1>} {<S1,S2>, <T1>} Target System

  17. Progression based approach Example t Available behaviors Iteratively add links {<S1,S1>, <T1>} {<S2,S1>, <T1>} {<S2,S2>, <T1>} {<S1,S2>, <T1>} {<S2,S1>, <T2>} {<S2,S2>, <T2>} Target System

  18. Progression based approach Example t Available behaviors Iteratively add links {<S1,S1>, <T1>} {<S2,S1>, <T1>} {<S2,S2>, <T1>} {<S1,S2>, <T1>} {<S2,S1>, <T2>} {<S2,S2>, <T2>} {<S2,S2>, <T3>} X Target System

  19. Implementation Implementation of both the techniques on a common platform • Implement both approaches on a common platform – Java • Prototype implementation available. • TLV implementation for deterministic available behaviors is available, but not for non-deterministic behaviors. Symfony is another system, but Lacks some of the components.

  20. Analysis Comparing the speed of the techniques • Measure the speed of both the algorithms for the problems • Design benchmark problems • Hand crafted • Problems for which a known solution exists • Problems for which a solution does not exist • Randomly generated problems • Variation in size and number of available behaviors • If time left in minor thesis • Study algorithm’s behavior with respect to • Varying degrees of non determinism in available behaviors

  21. Comparing the speed of the techniques Questions ?