This is an implementation of a computer adversary for minichess, a simplified chess game used in Bart Massey's CS 542 used in Bart Massey's CS 542
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Some parts of this software are adapted from PyChess. In particular is based on in PyChess. Also some PyChess tests were adapted.

See LICENSE for licensing information.


minichess should be on the python path, while tests can be anywhere.

I wrote a small suite of tests which helped to keep the bugs out. To run the test suite without installing (tested on Python 2.5): cd tests sh run # sets PYTHONPATH


4 strategies are included:

  • Random: chooses a legal move at random
  • Flat: chooses a move with a high static value
  • NegaMax: standard adversary search
    • iterative deepening until a time limit is hit
    • overshoots the time limit so has unpredictable timing
  • AlphaBeta: NegaMax optimized to trim search branches which aren't relevant


  • The engine will play games with itself or humans via
  • Also plays on the IMCS server:
  • I haven't implemented any dynamic time management: Both strategies take the next run that overshoots 3.0s, however long it takes. This can lead to draws in competition, especially against a tough opponent.

Minichess implementation

  • Move generation is simple and slow, using a generator to "feel around" the board.
  • The board is represented by an "augmented" 7x10=70 element array, which allows easy detection of "falling off the board".
  • Moves are reversible, so there is only one state, which is modified incrementally. Moves are stored bitwise in a single int32.


Currently AlphaBeta only reaches 6 plies in reasonable time. Further optimizations such as TT, opening book, and endgame DB should help.

Python isn't ideal for highly recursive algorithms. Potential speedups can also come from:

  • Applying Psycho (Python JIT compiler)
  • Profiling to detect hotspots for optimization
  • Optimizing data structures and precomputing lots of stuff in lookup tables(bitboards etc)