Ideas for Othello Game Player (Deliverable 2)
• Improved Evaluation functions
• variations to weighted difference function, though still static
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• evaluation functions that are conditioned on board configurations mobility – incorporate a factor that reflects the number of move options available to a player (current mobility is the number of legal moves available to a player; potential mobility is number of blank squares next to opponent piece) edge stability – measure of likelihood that edge squares can be flipped

Ideas for Othello Game Player (Deliverable 2)
• More efficient searching (Alpha Beta will tend to prune more moves, when moves are ordered “best” to “worst” (which can only be “guessed” at ahead of time). Thus, rather than relying on default ordering, do
• static reordering (e.g., order boards based on weights used by the weighted-diff function, or some other static weighting)
• dynamic reordering (e.g., order boards by evaluation function score)
• Killer Moves – a (opponent) move in one line of play can be effective (detrimental) in another line of play (e.g., if an opponent can capture a corner on one line of play, consider whether the opponent can capture the corner in another line of play)

Ideas for Othello Game Player (Deliverable 2)
• Improved Memory Management
• Rather than dynamically allocating boards, then “throwing away”, reuse dynamically allocated boards and mitigate garbage collection
• Anytime search and searching with time limits
• Iterative Deepening (Alpha Beta) pruning (for Othello, with a average branching factor about 10, searching to level
N+1 will be about 10 times more expensive than N; collect stats at N, and dynamically determine whether N+1 is doable
• Forward pruning – don’t search down paths that seem nonpromising

Ideas for Othello Game Player (Deliverable 2)
• Aspiration search or iterative “broadening”
• Instead of starting the search with alpha and beta values of losing(-1) and winning(+1) values initially, carefully choose a window (e.g., based on the current board’s value), such as 0-100, and expand the window only as needed
• Think ahead – keep searching while opponent is moving, based on one or more guesses of what the opponent will do
• Hash table of book moves, particularly of opening game
(where “the book” can be initialized by playing the program against itself

Ideas for Othello Game Player (Deliverable 2)
• Different strategy over course of game (e.g., shallower search in midgame, all out search later)
• Metareasoning – at each point (each search step, is it better to stop now or keep searching) – manage the clock
“dynamically” rather than “statically (e.g., assume same time limit per move) – always ask, is search further cost effective. Another way to think about it – consider ceasing the search as a “possible move”
• Learning – about opponent and about “one’s self” (e.g., which evaluation function is best)