General Video Game AI: Learning from screen capture

General Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain. In this paper, we propose for the first time a screen capture learning agent for General Video Game AI framework. A Deep Q-Network algorithm was applied and improved to develop an agent capable of learning to play different games in the framework. After testing this algorithm using various games of different categories and difficulty levels, the results suggest that our proposed screen capture learning agent has the potential to learn many different games using only a single learning algorithm

Population seeding techniques for Rolling Horizon Evolution in General Video Game Playing

While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length). An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution. In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm. The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search

Diversity maintenance using a population of repelling random-mutation hill climbers

A novel evolutionary algorithm, which can be viewed as an extension to the simple, yet effective, approach of the Random-Mutation Hill Climber (RMHC), is presented. The algorithm addresses the shortcomings of RMHC and its multi-individual parallel version through the introduction of a penalty term into the fitness function, which penalizes individuals in the population for being too similar, hence maintaining population diversity. The performance of the algorithm is evaluated on the deceptive trap and a set of SAT problems, comparing them to the Crowding EA. The results show that at a small cost of solution speed on simpler problems, the algorithm gains better capabilities of dealing with the issues of local maxima

Bandit-based Random Mutation Hill-Climbing

The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far solution and accepts the neighbour if it is better than or equal to it. In this work, we propose to use a novel method to select the neighbour solution using a set of independent multi-armed bandit-style selection units which results in a bandit-based Random Mutation Hill-Climbing algorithm. The new algorithm significantly outperforms Random Mutation Hill-Climbing in both OneMax (in noise-free and noisy cases) and Royal Road problems (in the noise-free case). The algorithm shows particular promise for discrete optimisation problems where each fitness evaluation is expensive

General Video Game for 2 players: Framework and competition

This paper presents a new track of the General Video Game AI competition for generic Artificial Intelligence agents, which features both competitive and cooperative real time stochastic two player games. The aim of the competition is to directly test agents against each other in more complex and dynamic environments, where there is an extra uncertainty in a game, consisting of the behaviour of the other player. The framework, server functionality and general competition setup are analysed and the results of the experiments with several sample controllers are presented. The results indicate that currently Open Loop Monte Carlo Tree Search is the overall leading algorithm on this set of games

Evolving Game Skill-Depth using General Video Game AI agents

Most games have, or can be generalised to have, a number of parameters that may be varied in order to provide instances of games that lead to very different player experiences. The space of possible parameter settings can be seen as a search space, and we can therefore use a Random Mutation Hill Climbing algorithm or other search methods to find the parameter settings that induce the best games. One of the hardest parts of this approach is defining a suitable fitness function. In this paper we explore the possibility of using one of a growing set of General Video Game AI agents to perform automatic play-testing. This enables a very general approach to game evaluation based on estimating the skill-depth of a game. Agent-based play-testing is computationally expensive, so we compare two simple but efficient optimisation algorithms: the Random Mutation Hill-Climber and the Multi-Armed Bandit Random Mutation Hill-Climber. For the test game we use a space-battle game in order to provide a suitable balance between simulation speed and potential skill-depth. Results show that both algorithms are able to rapidly evolve game versions with significant skill-depth, but that choosing a suitable resampling number is essential in order to combat the effects of noise

General video game AI: Competition, challenges, and opportunities

The General Video Game AI framework and competition pose the problem of creating artificial intelligence that can play a wide, and in principle unlimited, range of games. Concretely, it tackles the problem of devising an algorithm that is able to play any game it is given, even if the game is not known a priori. This area of study can be seen as an approximation of General Artificial Intelligence, with very little room for game-dependent heuristics. This short paper summarizes the motivation, infrastructure, results and future plans of General Video Game AI, stressing the findings and first conclusions drawn after two editions of our competition, and outlining our future plans

Evaluating and modelling Hanabi-playing agents

Agent modelling involves considering how other agents will behave, in order to influence your own actions. In this paper, we explore the use of agent modelling in the hidden-information, collaborative card game Hanabi. We implement a number of rule-based agents, both from the literature and of our own devising, in addition to an Information Set-Monte Carlo Tree Search (IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new, predictor version that uses a model of the agents with which it is paired. We observe a significant improvement in game-playing strength from this agent in comparison to IS-MCTS, resulting from its consideration of what the other agents in a game would do. In addition, we create a flawed rule-based agent to highlight the predictor's capabilities with such an agent

Predictive Models and Monte Carlo Tree Search: A Pipeline for Believable Agents

Developing and assessing believable agents remains a sought out challenge. Recently, research has approached this problem by treating and assessing believability as a time-continuous phenomenon, learning from collected data to predict believability of games and game states. Our study will build on this work: by integrating this believability model with a game agent to affect its behaviour. In this short paper, we first describe our methodology and then the results obtained from our user study, which suggests that this methodology can help creating more believable agents, opening the possibility of integrating this type of models into game development. We also discuss the limitations of this approach, possible variants to tackle these, and ideas for future work to extend this preliminary work

Fingerprinting Tabletop Games

We present some initial work on characterizing games using a visual 'fingerprint' generated from several independent optimisation runs over the parameters used in Monte Carlo Tree Search (MCTS). This 'fingerprint' provides a useful tool to compare games, as well as highlighting the relative sensitivity of a specific game to algorithmic variants of MCTS. The exploratory work presented here shows that in some games there is a major change in the optimal MCTS parameters when we move from 2-players to 3 or 4-players