Graduation Project Second Seminar Presentation. This seminar mainly aims justifies why we've used specific techniques and also, introduce our testing strategy

2. Agenda Refreshment : Problems and Goals Answering the why Why we’ve used Case-Based Reasoning. Why we’ve used Reinforcement Learning. System Architecture. Project Testing Strategy Turing Test. NPC (Static AI).

3. Problems and Goals

4. Problems and Goals Adaptive

5. Problems and Goals Adaptive Intelligent

6. Problems and Goals Machines rely on static scripting techniques. Adaptive Agent Intelligent

7. Problems and Goals

8. Problems and Goals Mobile

9. Problems and Goals The Absence of sharing experience costs a lot. Mobile Experience

10. Case Based Reasoning- a Brief

11. Why Case-Based Reasoning

12. Why Case-Based Reasoning Plan Learning

13. Why Case-Based Reasoning Plan Learning Failure Learning

14. Why Case-Based Reasoning Plan Learning Failure Learning Critic Learning

15. Why Case-Based Reasoning Plan Learning Failure Learning Critic Learning Prediction

16. Reinforcement Learning – A Brief

17. Why Reinforcement Learning Requires No Model Balance Exploration- Exploitation Applies Bootstrapping Used in the Revising Phase Sub-optimal policies

18. Why Reinforcement Learning Used in the Revising Phase

19. Why Reinforcement Learning Requires No Model

20. Why Reinforcement Learning Applies Bootstrapping

21. Why Reinforcement Learning Learn Sub-Optimal Policies

22. Why Reinforcement Learning Balance Exploration-Exploitation

23. System Architecture I-Strategizer AI Engine : Online Case Based Planner I-StrategizerToWargus Case Based Reasoner EE Module Game State Goal Game State Expansion Module Plan Retriever Perception Module Retrieved Plan Plan to be adapted Adapted Plan Game State Plan Adaptor Case (Plan) Base Plan to be adapted Wargus (Game) Plan Game State Actions Executor Game Specific Actions Actions Executed Plan Execution Module Plan Reviser (RL Techniques) Plan Retainer Revised Plan Retained Plan Feedback Game Specific Feedback

24. Case Representation : An Example

25. Interleaved Expansion and Execution

26. Testing Strategy – Turing Test

27. Testing Strategy –Playing Static AI

28. References Santiago Ontanon, Ashwin Ram - On-Line Case based Planning– 2010 KristianJ.Hammond - Case-Based Planning - A Framework for planning from Experience - 1994 Book: Reinforcement Learning An Introduction – 1998 Matthew Molineaux, David W. Aha, & Philip Moore - Learning continuous action models in a real-time strategy environment - 2008 Book: AI Game Engine Programming - 2009

29. Thanks