1. 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 Implementation and analysis of simulation based techniques for behavior composition

2. Behavior Composition Simulation Techniques Implementation Analysis Contents

3. Behavior Composition 3 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 B1B2

4. Behavior Composition 4 Combining available behaviors to realize a target behavior Available behaviors Target behavior (virtual) T1 Can we realize T1 by composing B1 and B2 ? B1B2

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

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

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

8. Simulation 8 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. t Simulation 9 Example Available behaviors Target System {, } … Simulation Relation simulation relation is a solution to the behavior Composition problem ! How to calculate it ?

10. Techniques 10 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 11 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. t Regression based approach 12 Example Available behaviors Target System {, } Assume each state from available behaviors simulates each state In the target system

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

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

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

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

17. t Progression based approach 17 Example Available behaviors Target System {, } Iteratively add links

18. t Progression based approach 18 Example Available behaviors Target System {, } Iteratively add links X

19. Implementation 19 Implementation of both the techniques on a common platform Implement both approaches on a common platform – Java Prototype implementation available. 1.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 20 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. Questions ? Comparing the speed of the techniques