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Mastergoal Machine Learning Environment
Phase 1 Completion AssessmentMSE Project
Kansas State University
Alejandro Alliana
Deliverables
• Vision Document v 0.1.0
• Project Plan 0.1.0
• Software Quality Assurance Plan 0.1.0
• Prototype
Mastergoal
• Board game with discrete states.
• Played at different levels.
• High branching factor.
• New in AI research.
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
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)
Risks
• Inexperience with some algorithms and programming language
• Exploration vs. exploitation
• Computational Cost of Evaluation Functions
• Quality
Prototypes demonstration
Constraints
• Export strategies to be used in the Mastergoal plugin environment.
• CPP programming language
Requirements
• Experiment Management
• Training strategy
• Export Strategy
• Explore game
System Components
Experiment Management
Other Use Cases
Documentation standards
• UML Diagrams
• Scenario description
• Coding Standards following the C++ standards
• Commentary standards following Code Conventions for the Java Programming Language.
Testing Standards
• Unit testing– CppTest
• Component testing
• Integration Testing
• Performance Testing
• Testing plan
Version Control
• SVN Repository
• Maven directory Structure standard
• Tortoise SVN Client
Tools
• IDE– Microsoft Visual Studio
• Modeling– Rational Rose– Gliffy.com
• Documentation– Microsoft Word
• Code control– Tortoise SVN
• Managing– Process Dashboard
– Microsoft Project
Cost Estimate
• COCOMO
• COCOMO II
• Use case points
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.
Cocomo – Effort Adjustment factors
Id Effort Adjustment Factor Parameter Range
Potential Impact Value Selected Reasoning
RELY Required reliability 0.75 - 1.40 1.87 1.00 Nominal - The application is reliability is not critical
DATA Database size 0.94 - 1.16 1.23 1.00 Nominal -Database access to store games
CPLX Product complexity 0.70 – 1.65 2.36 1.15 High – Product contains reinforcement learning algorithms.
TIME Execution time constraint 1.00 – 1.66 1.66 1.11 High – Experiments must not take too long, since the application is already computationally intensive.
STOR Main Storage Constraint 1.00 – 1.56 1.56 1.00 Nominal
VIRT Virtual machine volatility 0.87 – 1.30 1.49 1.00 Nominal
TURN Computer turnaround time 0.87 – 1.15 1.32 1.00 Nominal
ACAP Analyst capability 1.46 – 0.71 2.06 0.86 High. Developer has adequate experience.
AEXP Applications experience 1.29 – 0.82 1.57 1.10 Low. Some of the components are new to the developer
PCAP Programmer capability 1.42 – 0.70 2.03 1.00 Nominal
VEXP Virtual machine experience 1.21 – 0.90 1.34 1.00 Nominal. Developer has adequate experience with OS systems and tools.
LEXP Language experience 1.14 – 0.95 1.20 1.07 Low. Developer is new to the C++ language.
MODP Use of modern practices 1.24 – 0.82 1.51 0.91 High. The process will follow modern practices.
TOOL Use of software tools 1.24 – 0.83 1.49 0.91 High.
SCED Required development schedule 1.23 – 1.10 1.23 1.10 Low. Project is on a constrained schedule
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
COCOMO II
• COCOMO II defines three models for cost estimation:– Applications composition model– Early design model– Post-Architecture model.
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
Application Composition Model
• PM = 39/7 = 5.57 Person months – (2.25 ~ 11.07 months)
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.
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)
Project Schedule
Phase Two Deliverables
• Vision document
• Project Plan
• Test Plan
• Architecture Design
• Formal Requirements Specification
• Formal Technical Inspection
• Executable Architecture Prototype.
• Questions
End of presentation
Application Composition Model
Object Classification Object Points
Main screen Medium 2
Game exploration screen Medium 2
Export strategy screen Simple 1
Experiments status report Simple 2
Explore game screen Medium 2
Game component Complex 10
Search component Complex 10
Learning component Complex 10
TOTAL 39
Scaling factors
Scale Factor Abbreviation Value.
Precedentedness PREC Nominal – 3
Development Flexibility FLEX Low – 4
Architecture risk resolution RESL Nominal – 3
Team cohesion TEAM Extra high – 0
Frameworks Studied
• Knight Cap
• Neuro Draugths
• RL Glue