2 research outputs found
Giraffe: Using Deep Reinforcement Learning to Play Chess
This report presents Giraffe, a chess engine that uses self-play to discover
all its domain-specific knowledge, with minimal hand-crafted knowledge given by
the programmer. Unlike previous attempts using machine learning only to perform
parameter-tuning on hand-crafted evaluation functions, Giraffe's learning
system also performs automatic feature extraction and pattern recognition. The
trained evaluation function performs comparably to the evaluation functions of
state-of-the-art chess engines - all of which containing thousands of lines of
carefully hand-crafted pattern recognizers, tuned over many years by both
computer chess experts and human chess masters. Giraffe is the most successful
attempt thus far at using end-to-end machine learning to play chess.Comment: MSc Dissertatio
Dynamic Move Chains -- a Forward Pruning Approach to Tree Search in Computer Chess
This paper proposes a new mechanism for pruning a search game-tree in
computer chess. The algorithm stores and then reuses chains or sequences of
moves, built up from previous searches. These move sequences have a built-in
forward-pruning mechanism that can radically reduce the search space. A typical
search process might retrieve a move from a Transposition Table, where the
decision of what move to retrieve would be based on the position itself. This
algorithm stores move sequences based on what previous sequences were better,
or caused cutoffs. This is therefore position independent and so it could also
be useful in games with imperfect information or uncertainty, where the whole
situation is not known at any one time. Over a small set of tests, the
algorithm was shown to clearly out-perform Transposition Tables, both in terms
of search reduction and game-play results.Comment: Publishe