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Agenda

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 ). Problems and Goals. Problems and Goals. Adaptive. Problems and Goals.

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Agenda

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  1. 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).

  2. Problems and Goals

  3. Problems and Goals Adaptive

  4. Problems and Goals Intelligent Adaptive

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

  6. Problems and Goals

  7. Problems and Goals Mobile

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

  9. Case Based Reasoning- a Brief

  10. Why Case-Based Reasoning

  11. Why Case-Based Reasoning Plan Learning

  12. Why Case-Based Reasoning Failure Learning Plan Learning

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

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

  15. Reinforcement Learning – A Brief

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

  17. Why Reinforcement Learning Used in the Revising Phase

  18. Why Reinforcement Learning Requires No Model

  19. Why Reinforcement Learning Applies Bootstrapping

  20. Why Reinforcement Learning Learn Sub-Optimal Policies

  21. Why Reinforcement Learning Balance Exploration-Exploitation

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

  23. Case Representation : An Example

  24. Interleaved Expansion and Execution

  25. Testing Strategy – Turing Test

  26. Testing Strategy –Playing Static AI

  27. 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

  28. Thanks

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