22 research outputs found
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi
Hanabi is a cooperative game that brings the problem of modeling other
players to the forefront. In this game, coordinated groups of players can
leverage pre-established conventions to great effect, but playing in an ad-hoc
setting requires agents to adapt to its partner's strategies with no previous
coordination. Evaluating an agent in this setting requires a diverse population
of potential partners, but so far, the behavioral diversity of agents has not
been considered in a systematic way. This paper proposes Quality Diversity
algorithms as a promising class of algorithms to generate diverse populations
for this purpose, and generates a population of diverse Hanabi agents using
MAP-Elites. We also postulate that agents can benefit from a diverse population
during training and implement a simple "meta-strategy" for adapting to an
agent's perceived behavioral niche. We show this meta-strategy can work better
than generalist strategies even outside the population it was trained with if
its partner's behavioral niche can be correctly inferred, but in practice a
partner's behavior depends and interferes with the meta-agent's own behavior,
suggesting an avenue for future research in characterizing another agent's
behavior during gameplay.Comment: arXiv admin note: text overlap with arXiv:1907.0384
The 2018 Hanabi competition
This paper outlines the Hanabi competition, first run at CIG 2018, and returning for COG 2019. Hanabi presents a useful domain for game agents which must function in a cooperative environment. The paper presents the results of the two tracks which formed the 2018 competition and introduces the learning track, a new track for 2019 which allows the agents to collect statistics across multiple games
Artificial intelligence in co-operative games with partial observability
This thesis investigates Artificial Intelligence in co-operative games that feature Partial Observability. Most video games feature a combination of both co-operation, as well as Partial Observability. Co-operative games are games that feature a team of at least two agents, that must achieve a shared goal of some kind. Partial Observability is the restriction of how much of an environment that an agent can observe. The research performed in this thesis examines the challenge of creating Artificial Intelligence for co-operative games that feature Partial Observability. The main contributions are that Monte-Carlo Tree Search outperforms Genetic Algorithm based agents in solving co-operative problems without communication, the creation of a co-operative Partial Observability competition promoting Artificial Intelligence research as well as an investigation of the effect of varying Partial Observability to Artificial Intelligence, and finally the creation of a high performing Monte-Carlo Tree Search agent for the game Hanabi that uses agent modelling to rationalise about other players
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for
studying how well machines can do sophisticated decision making. In recent
years, machine learning has made dramatic advances with artificial agents
reaching superhuman performance in challenge domains like Go, Atari, and some
variants of poker. As with their predecessors of chess, checkers, and
backgammon, these game domains have driven research by providing sophisticated
yet well-defined challenges for artificial intelligence practitioners. We
continue this tradition by proposing the game of Hanabi as a new challenge
domain with novel problems that arise from its combination of purely
cooperative gameplay with two to five players and imperfect information. In
particular, we argue that Hanabi elevates reasoning about the beliefs and
intentions of other agents to the foreground. We believe developing novel
techniques for such theory of mind reasoning will not only be crucial for
success in Hanabi, but also in broader collaborative efforts, especially those
with human partners. To facilitate future research, we introduce the
open-source Hanabi Learning Environment, propose an experimental framework for
the research community to evaluate algorithmic advances, and assess the
performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence
Evaluating the Effects on Monte Carlo Tree Search of Predicting Co-operative Agent Behaviour
This thesis explores the effects of including an agent-modelling strategy into Monte-Carlo Tree Search. This is to explore how the effects of such modelling might be used to increase the performance of agents in co-operative environments such as games.
The research is conducted using two applications. The first is a co-operative 2-player puzzle game in which a perfect model outperforms an agent that makes the assumption the other agent plays randomly. The second application is the partially observable co-operative card game Hanabi, in which the predictor variant is able to outperform both a standard variant of MCTS and a version that assumes a fixed-strategy for the paired agents. This thesis also investigates a technique for learning player strategies off-line based on saved game logs for use in modelling
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI
This paper reviews the field of Game AI, which not only deals with creating
agents that can play a certain game, but also with areas as diverse as creating
game content automatically, game analytics, or player modelling. While Game AI
was for a long time not very well recognized by the larger scientific
community, it has established itself as a research area for developing and
testing the most advanced forms of AI algorithms and articles covering advances
in mastering video games such as StarCraft 2 and Quake III appear in the most
prestigious journals. Because of the growth of the field, a single review
cannot cover it completely. Therefore, we put a focus on important recent
developments, including that advances in Game AI are starting to be extended to
areas outside of games, such as robotics or the synthesis of chemicals. In this
article, we review the algorithms and methods that have paved the way for these
breakthroughs, report on the other important areas of Game AI research, and
also point out exciting directions for the future of Game AI