Efficient Reasoning in Regular Boardgames
This work addresses the need for efficient and general reasoning tools in General Game Playing, offering incremental improvements in compiler optimizations and benchmarking for researchers and developers in AI game playing.
The paper tackles the problem of efficient reasoning in Regular Boardgames (RBG), a universal formalism for finite deterministic games with perfect information, by describing compiler optimizations that achieve efficiency improvements of 1.7 to 33-fold in game playouts per second and showing that RBG is the fastest among abstract general game playing languages, competitive with handcrafted game-specific implementations.
We present the technical side of reasoning in Regular Boardgames (RBG) language -- a universal General Game Playing (GGP) formalism for the class of finite deterministic games with perfect information, encoding rules in the form of regular expressions. RBG serves as a research tool that aims to aid in the development of generalized algorithms for knowledge inference, analysis, generation, learning, and playing games. In all these tasks, both generality and efficiency are important. In the first part, this paper describes optimizations used by the RBG compiler. The impact of these optimizations ranges from 1.7 to even 33-fold efficiency improvement when measuring the number of possible game playouts per second. Then, we perform an in-depth efficiency comparison with three other modern GGP systems (GDL, Ludii, Ai Ai). We also include our own highly optimized game-specific reasoners to provide a point of reference of the maximum speed. Our experiments show that RBG is currently the fastest among the abstract general game playing languages, and its efficiency can be competitive to common interface-based systems that rely on handcrafted game-specific implementations. Finally, we discuss some issues and methodology of computing benchmarks like this.