1. Ibrahim Fathy, Mostafa Aref, Omar Enayet, and Abdelrahman Al-Ogail Faculty of Computer and Information Sciences Ain-Shams University ; Cairo ; Egypt

2. Introduction. Problem definition. Objectives. Intelligent OLCBP Agent Model. OLCBP\RL Hybridization. Experiments and Results. Conclusion & Future Work. Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

3. Online Case Based Planning is an architecture based on CBR. It addresses issues of plan acquisition, on-line plan execution, interleaved planning and execution and on-line plan adaptation. Darmok is a previous system that applies this -without revision- to RTS Games. Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

4. A Reinforcement Learning Approach. Combines temporal difference learning technique “One-Step SARSA” with eligibility traces to learn state-action pair values effectively. It’s an on-policy method. Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

5. Considered a challenging domain due to: Severe Time Constraints – Real-Time AI – Many Objects – Imperfect Information – Micro-Actions Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

6. Research in learning and planning in real-time strategy (RTS) games is very interesting. The research on online case-based planning in RTS Games does not include the capability of online learning from experience. The knowledge certainty remains constant, which leads to inefficient decisions. Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

7. Proposing an intelligent agent model based on both online case- based planning (OLCBP) and reinforcement learning (RL) techniques. Increasing the certainty of the case base by learning from experience. Increasing both efficiency and effectiveness of the plan decision making process. Evaluating the model using empirical simulation on Wargus. ( A clone of the well-known strategy game Warcraft 2) Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

8. Environment ( RTS Game “Wargus”) Case Base Offline Phase (Case Acquisition) Plan Expansion /Execution Module Online Case- Based Learner Behaviors Goals Evaluated Case Retrieved Case Traces Cases Actions Online Phase Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

10. Case Goal Strategy Situation State Shallow FeaturesDeep Features Behavior Pre-ConditionsAlive-ConditionsSuccess-Conditions Snippet Learning Parameters Certainty FactorEligibilityPrior Confidence Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

12. Since the evaluation of the case base is changed, the case retrieval algorithm must change also. The case with the best predicted performance will be retrieved to be executed. Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

15. Learning ParameterValue used Learning Rate (α)0.1 Discount Rate (γ )0.8 Decay Rate (λ )0.5 Exploration Rate0.1

16. Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

18. Agent has learnt that building a smaller heavy army in that specific situation (the existence of a towers defense) is more preferable than building a larger light army. Similarly, the agent can evaluate the entire case base and learn the right choices. Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

19. Online case-based planning was hybridized with reinforcement learning in order to introduce an intelligent agent capable of planning and learning online using temporal difference with eligibility traces: Sarsa (λ) algorithm. The empirical evaluation has shown that the proposed model –unlike Darmok System - increases the certainty of the case base by learning from experience, and hence the process of decision making for selecting more efficient, effective and successful plans. Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

20. Implementing a prototype based on the proposed model. Developing a strategy/case base visualization tool capable of visualizing agent’s preferred playing strategy according to its learning history. This will help in tracking the learning curve of the agent. Finally, designing and developing a multi-agent system where agents are able to share their experiences together. Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10

21. Thank You ! Questions ? Intelligent Online Case-Based Planning Agent Model for RTS Games – ISDA’10