Xi Breakthrough Player An extremely intelligent Breakthrough AI. You can tell by the use of X. CSE 486 B Fall 2012 Miami University

Team Mike Jacobs Jiang Nuo Reuben Smith

Components Search Threading Depth control Storage optimization Features Parametric feature weights Feature weight optimization Opponent interfacing

Search Negamax with alpha-beta – Negamax is minimax “simplified” – State evaluation is from the perspective of the player associated with the current depth – Scores are negated as search moves up the tree

Threading Threading is used to split up the search workload evenly between search workers – The number of workers created is based on the number of processor cores available less one Workers use copies of state, so no concurrency issues are possible

Depth Control We dynamically adjust depth limit after each move targetMoveTime = gameTimeLimit / averageGameMoves – targetMoveTime is calculated at the start of each game – averageGameMoves is updated after each game

Depth Control, cont. If search finishes in less than targetMoveTime, then the depth limit increases If search finishes in more than targetMoveTime, then the depth limit decreases Depth control uses coefficients to affect how easily the depth limit can be changed again This allows search depth to stabilize based on system performance and search domain size

Storage Optimization Instead of creating a move object for each possible move at every node in the search tree, move data is encoded inside a primitive short: – Bits 0-2 represent starting row – Bits 3-5 represent starting column – Bits 6-8 represent ending row – Bits 9-11 represent ending column – Bits represent move flags (e.g. move is a take)

Features 7 features implemented Notable features: – Squares owned – Penetration – Spread – Conflict – Cover

Parametric Feature Weights The player can take in a parameter object to allow user-defined weights This can be used to control the difficulty level of the player; for example: – easy – only one feature is “on” (has weight) – hard – all features are given an optimal weighting

Feature Weight Optimization Hill climbing can be used in two different places to optimize feature weights: 1.To determine optimal weight for each feature in a set of features 2.To choose the set of features that are turned on (from sets of 2 to 4 features) – Results from #1 would be used to find a maximum for scoring in #2 – Momentum would be used to improve the chances of finding the global maximum This was not included in the final submission due to time and computational requirements

Opponent Interfacing (taunting) Lastly, the player will also maintain superiority by intimidating the opponent at the start of each game: – “Hello,. Nice to beat you.“ – “Spill the blood of the innocent!”

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