Accelerating board games through Hardware/Software Codesign

Board games applications usually offer a great user experience when running on desktop computers. Powerful high-performance processors working without energy restrictions successfully deal with the exploration of large game trees, delivering strong play to satisfy demanding users. However, nowadays, more and more game players are running these games on smartphones and tablets, where the lower computational power and limited power budget yield a much weaker play. Recent systems-on-a-chip include programmable logic tightly coupled with general-purpose processors enabling the inclusion of custom accelerators for any application to improve both performance and energy efficiency. In this paper, we analyze the benefits of partitioning the artificial intelligence of board games into software and hardware. We have chosen as case studies three popular and complex board games, Reversi, Blokus, and Connect6. The designs analyzed include hardware accelerators for board processing, which improve performance and energy efficiency by an order of magnitude leading to much stronger and battery-aware applications. The results demonstrate that the use of hardware/software codesign to develop board games allows sustaining or even improving the user experience across platforms while keeping power and energy low

A new challenge: approaching Tetris Link with AI

Decades of research have been invested in making computer programs for playing games such as Chess and Go. This paper introduces a board game, Tetris Link, that is yet unexplored and appears to be highly challenging. Tetris Link has a large branching factor and lines of play that can be very deceptive, that search has a hard time uncovering. Finding good moves is very difficult for a computer player, our experiments show. We explore heuristic planning and two other approaches: Reinforcement Learning and Monte Carlo tree search. Curiously, a naive heuristic approach that is fueled by expert knowledge is still stronger than the planning and learning approaches. We, therefore, presume that Tetris Link is more difficult than expected. We offer our findings to the community as a challenge to improve upon.LIACS-Managemen