Mastergoal Machine Learning Environment

1. Mastergoal Machine Learning Environment Phase 1 Completion Assessment MSE Project Kansas State University Alejandro Alliana

2. Deliverables • Vision Document v 0.1.0 • Project Plan 0.1.0 • Software Quality Assurance Plan 0.1.0 • Prototype

3. Mastergoal • Board game with discrete states. • Played at different levels. • High branching factor. • New in AI research.

4. Project Goals • Provide an environment to create, repeat, save experiments for creating strategies for playing Mastergoal using ML techniques. • Try different AI techniques in the environment of the game

5. Background • Traditional approaches • Search in the state space S applying actions A(st) to the states and Evaluating the generated states st+1 using a hand crafted evaluation function • Reinforcement Learning • Unsupervised learning. • Temporal difference learning. • Successful with Backgammon. • Problems with some games such as Chess and Go. • TD-Leaf, TD(μ)

6. Risks • Inexperience with some algorithms and programming language • Exploration vs. exploitation • Computational Cost of Evaluation Functions • Quality

7. Prototypes demonstration

8. Constraints • Export strategies to be used in the Mastergoal plugin environment. • CPP programming language

9. Requirements • Experiment Management • Training strategy • Export Strategy • Explore game

10. System Components

11. Experiment Management

12. Other Use Cases

13. Documentation standards • UML Diagrams • Scenario description • Coding Standards following the C++ standards • Commentary standards following Code Conventions for the Java Programming Language.

14. Testing Standards • Unit testing • CppTest • Component testing • Integration Testing • Performance Testing • Testing plan

15. Version Control • SVN Repository • Maven directory Structure standard • Tortoise SVN Client

16. Tools • IDE • Microsoft Visual Studio • Modeling • Rational Rose • Gliffy.com • Documentation • Microsoft Word • Code control • Tortoise SVN • Managing • Process Dashboard • Microsoft Project

17. Cost Estimate • COCOMO • COCOMO II • Use case points

18. COCOMO • Effort = 3.2 EAF (Size) 1.05 • Time = 2.5 (Effort) 0.38 • Where: • Effort is the number of staff months • EAF is the product of 15 effort adjustment factors. • Size is the number of delivered source instructions in KLOC.

19. Cocomo – Effort Adjustment factors

20. COCOMO Estimate • Estimated KLOC (7.5) • Effort = 3.2 (1.18) (7.5) 1.05 • Effort= 31.32 staff months • Time = 2.5 (Effort) 0.38 • Time = 9.25 months

21. COCOMO II • COCOMO II defines three models for cost estimation: • Applications composition model • Early design model • Post-Architecture model.

22. Application Composition Model • Assess Object-Counts • Classify each object instance into simple, medium and difficult and weight them. • Determine Object-Points • Estimate percentage of reuse • Determine a productivity rate • Compute the estimated person-months

23. Application Composition Model • PM = 39/7 = 5.57 Person months • (2.25 ~ 11.07 months)

24. Early Design Model • Effort = 2.45 EArch (Size)P • Where: • Effort = number of staff-moths • EArch = is the product of seven early design effort adjustment factors • Size = number of function points or KLOC • P are the scaling factors.

25. Post Architecture model • Effort = 2.45 (Eapp) (Size)P • Effort = number of staff-moths • EArch = is the product of seventeen post architecture effort adjustment factors • Size = number of function points or KLOC • P = process exponent, same as the early design model. • Effort = 33.99 staff months • Time = 9.54 months (7.632 ~ 11.93)

26. Project Schedule

27. Phase Two Deliverables • Vision document • Project Plan • Test Plan • Architecture Design • Formal Requirements Specification • Formal Technical Inspection • Executable Architecture Prototype.

28. End of presentation • Questions

29. Application Composition Model

30. Scaling factors

31. Frameworks Studied • Knight Cap • Neuro Draugths • RL Glue