reconchess: python package for Reconnaissance Chess


Reconnaissance Chess is a chess variant (more precisely, a family of chess variants) invented as an R&D project at Johns Hopkins Applied Physics Laboratory (JHU/APL). Reconnaissance Chess adds the following elements to standard (classical) chess: sensing; incomplete information; decision making under uncertainty; coupled management of ‘battle forces’ and ‘sensor resources’; and adjudication of multiple, simultaneous, and competing objectives. Reconnaissance chess is a paradigm and test bed for understanding and experimenting with autonomous decision making under uncertainty and in particular managing a network of sensors to maintain situational awareness informing tactical and strategic decision making.

The game implemented in this python package is a relatively basic version using only one kind of sensor that provides perfect information in a small region of the chess board. In the future, extended versions may include noisy sensors of different types; multiple sensing actions per turn; the need to divide attention and resources among multiple, concurrent games; and other complicating factors.

This package includes a “game arbiter” which controls the game flow, maintains the ground truth game board, and notifies players of information collected by sense and move actions. The package also contains a client API for interacting with the arbiter, which can be used by bot players or other game interfaces.


pip install reconchess


Documentation is hosted by Read the Docs.


Distributed under BSD 3-Clause License, for details see LICENSE file.

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