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

Learning on a Budget via Teacher Imitation

Deep Reinforcement Learning (RL) techniques can benefit greatly from leveraging prior experience, which can be either self-generated or acquired from other entities. Action advising is a framework that provides a flexible way to transfer such knowledge in the form of actions between teacher-student peers. However, due to the realistic concerns, the number of these interactions is limited with a budget; therefore, it is crucial to perform these in the most appropriate moments. There have been several promising studies recently that address this problem setting especially from the student's perspective. Despite their success, they have some shortcomings when it comes to the practical applicability and integrity as an overall solution to the learning from advice challenge. In this paper, we extend the idea of advice reusing via teacher imitation to construct a unified approach that addresses both advice collection and advice utilisation problems. We also propose a method to automatically tune the relevant hyperparameters of these components on-the-fly to make it able to adapt to any task with minimal human intervention. The experiments we performed in 5 different Atari games verify that our algorithm either surpasses or performs on-par with its top competitors while being far simpler to be employed. Furthermore, its individual components are also found to be providing significant advantages alone

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

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

A case study in AI-assisted board game design

We use AI agents to play successive design iterations of an analogue board game to understand the sorts of question a designer asks of a game, and how AI play-testing approaches can help answer these questions and reduce the need for time-consuming human play-testing. Our case study supports the view that AI play-testing can complement human testing, but can certainly not replace it. A core issue to be addressed is the extent to which the designer trusts the results of AI play-testing as sufficiently human-like. The majority of design changes are inspired from human play-testing, but AI play-testing helpfully complements these and often gave the designer the confidence to make changes faster where AI and humans 'agreed'

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

Potential early adopters of hybrid and electric vehicles in Spain – towards a customer profile

The adoption of electric vehicles (EVs) by consumers is regarded as a key strategic goal for the reduction in transport-related air pollution levels. Although sales of EVs continue to rise year on year, generally, the attainment of the strategic goals set by various governments for the adoption of EVs remain a distant target. The purpose of this study is to identify the customer profile of early adopters of EVs in Spain: one of Europe’s largest economies and yet the country with the lowest take up rate of EVs at present. The analysis presented here is based on an online survey of 404 potential consumers of EVs in Spain. A cluster analysis of the data was performed based on a set of three socio-demographic characteristics (gender, age, and income), one psychographic (green moral obligation - GMO) and two EV attributes (price and driving range). The results of this analysis showed that there exist two segments with respect to higher or lower customer intentions related to the adoption of EVs. These findings represent a theoretical contribution to current understanding of the customer profile of adopters of EVs and will contribute to the development of communication and retail strategies aimed at customers fitting the profile of early adopters of new technologies

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

Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing

When implementing intelligent agents for strategy games, we observe that search-based methods struggle with the complexity of such games. To tackle this problem, we propose a new variant of Monte Carlo Tree Search which can incorporate action and game state abstractions. Focusing on the latter, we developed a game state encoding for turn-based strategy games that allows for a flexible abstraction. Using an optimization procedure, we optimize the agent's action and game state abstraction to maximize its performance against a rule-based agent. Furthermore, we compare different combinations of abstractions and their impact on the agent's performance based on the Kill the King game of the Stratega framework. Our results show that action abstractions have improved the performance of our agent considerably. Contrary, game state abstractions have not shown much impact. While these results may be limited to the tested game, they are in line with previous research on abstractions of simple Markov Decision Processes. The higher complexity of strategy games may require more intricate methods, such as hierarchical or time-based abstractions, to further improve the agent's performance

Potential early adopters of hybrid and electric vehicles in Spain – towards a customer profile

The adoption of electric vehicles (EVs) by consumers is regarded as a key strategic goal for the reduction in transport-related air pollution levels. Although sales of EVs continue to rise year on year, generally, the attainment of the strategic goals set by various governments for the adoption of EVs remain a distant target. The purpose of this study is to identify the customer profile of early adopters of EVs in Spain: one of Europe’s largest economies and yet the country with the lowest take up rate of EVs at present. The analysis presented here is based on an online survey of 404 potential consumers of EVs in Spain. A cluster analysis of the data was performed based on a set of three socio-demographic characteristics (gender, age, and income), one psychographic (green moral obligation - GMO) and two EV attributes (price and driving range). The results of this analysis showed that there exist two segments with respect to higher or lower customer intentions related to the adoption of EVs. These findings represent a theoretical contribution to current understanding of the customer profile of adopters of EVs and will contribute to the development of communication and retail strategies aimed at customers fitting the profile of early adopters of new technologies