We present a new general board game (GBG) playing and learning framework. GBG defines the common interfaces for board games, game states and their AI agents. It allows one to run competitions of different agents on different games. It standardizes those parts of board game playing and learning that otherwise would be tedious and repetitive parts in coding. GBG is suitable for arbitrary 1-, 2-, ..., N-player board games. It makes a generic TD($\lambda$)-n-tuple agent for the first time available to arbitrary games. On various games, TD($\lambda$)-n-tuple is found to be superior to other generic agents like MCTS. GBG aims at the educational perspective, where it helps students to start faster in the area of game learning. GBG aims as well at the research perspective by collecting a growing set of games and AI agents to assess their strengths and generalization capabilities in meaningful competitions. Initial successful educational and research results are reported.
This paper presents BTT-Go: an agent for Go whose ar- chitecture is based on the well-known agent Fuego, that is, its search process for the best move is based on sim- ulations of games performed by means of Monte- Carlo Tree Search (MCTS). In Fuego, these simulations are guided by supervised heuristics called prior knowledge and play-out policy. In this context, the goal behind the BTT-Go proposal is to reduce the supervised character of Fuego, granting it more autonomy. To cope with this task, the BTT-Go counts on a Transposition Table (TT) whose role is not to waste the history of the nodes that have already been explored throughout the game. By this way, the agent proposed here reduces the super- vised character of Fuego by replacing, whenever pos- sible, the prior knowledge and the play-out policy with the information retrieved from the TT. Several evalua- tive tournaments involving BTT-Go and Fuego confirm that the former obtains satisfactory results in its purpose of attenuating the supervision in Fuego without losing its competitiveness, even in 19x19 game-boards.
Our platform supports a large, variable number of agents within a persistent and open-ended task. The inclusion of many agents and species leads to better exploration, divergent niche formation, and greater overall competence. In recent years, multiagent settings have become an effective platform for deep reinforcement learning research. Despite this progress, there are still two main challenges for multiagent reinforcement learning. We need to create open-ended tasks with a high complexity ceiling: current environments are either complex but too narrow or open-ended but too simple.
Game playing programming assignments can provide useful hands-on learning experiences for teaching search tree programming techniques, space efficient data representation, and heuristic evaluation functions. However, a number of issues arise with game playing assignments that are not relevant to the focus of learning the AI material. Among these are interface development and agent communication. This paper describes the implementation of a network game playing system that allows students to center their attention on the relevant AI topics. Student evaluations and test scores over the last three years indicate that the use of this game playing system has enhanced the learning of AI concepts.
Facebook's new augmented reality tools will let you place virtual objects into the real world when you view your surroundings through your phone. Leave messages on the fridge for your spouse, or tag businesses with floating notes and tips written on walls. We'll get AR games that incorporate real-world objects thanks to a technology called "SLAM" (simultaneous location and mapping) that lays a 3-D grid over the table in front of you, turning it into a gameboard. Also, we'll get AR art, pieces only viewable through your phone. As Zuckerberg said, "This is going to be a thing in the future--people standing around looking at blank walls."