Autonomous Checkers Robot Using Enhanced Massive Parallel Game Tree Search

Abstract

The dream of building intelligent robotic systems to interact and communicate with people and help them in their lives is a very old and ongoing study. In this research, the massive parallel autonomous checkers agent "MPACA" can autonomously play checkers with a human upto Grandmaster level without requiring a special checkers board for detecting human movements. The main aim and contribution of this research is proposing enhanced algorithms for a game tree search using two different approaches. The first was a task-based approach on CPU with a parallel database, while the second was a threads-based approach on the GPU with no divergence and dynamic parallelism. The two approaches were compared with previous studies using various approaches, including threads on CPU for up to 6x speedup for an 8-core processor and threads on GPU using iterative dependence and fixed grid and block size of up to 40x speedup at 14 depth. Furthermore, the approaches were tested with different depths on the CPU and the GPU. The result shows speed up for parallel CPU tasks up to 7x for an 8-core processor and parallel GPU of up to 80x at 14 depth.