General Board Game Concepts

Many games often share common ideas or aspects between them, such as their rules, controls, or playing area. However, in the context of General Game Playing (GGP) for board games, this area remains under-explored. We propose to formalise the notion of "game concept", inspired by terms generally used by game players and designers. Through the Ludii General Game System, we describe concepts for several levels of abstraction, such as the game itself, the moves played, or the states reached. This new GGP feature associated with the ludeme representation of games opens many new lines of research. The creation of a hyper-agent selector, the transfer of AI learning between games, or explaining AI techniques using game terms, can all be facilitated by the use of game concepts. Other applications which can benefit from game concepts are also discussed, such as the generation of plausible reconstructed rules for incomplete ancient games, or the implementation of a board game recommender system

Recent Advances in General Game Playing

The goal of General Game Playing (GGP) has been to develop computer programs that can perform well across various game types. It is natural for human game players to transfer knowledge from games they already know how to play to other similar games. GGP research attempts to design systems that work well across different game types, including unknown new games. In this review, we present a survey of recent advances (2011 to 2014) in GGP for both traditional games and video games. It is notable that research on GGP has been expanding into modern video games. Monte-Carlo Tree Search and its enhancements have been the most influential techniques in GGP for both research domains. Additionally, international competitions have become important events that promote and increase GGP research. Recently, a video GGP competition was launched. In this survey, we review recent progress in the most challenging research areas of Artificial Intelligence (AI) related to universal game playing

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Complexity, Heuristic, and Search Analysis for the Games of Crossings and Epaminondas

Games provide fertile research domains for algorithmic research. Often, game research helps solve real-world problems through the testing and refinement of search algorithms in game domains. Other times, game research finds limits for certain algorithms. For example, the game of Go proved intractable for the Min-Max with Alpha-Beta pruning algorithm leading to the popularity of Monte-Carlo based search algorithms. Although effective in Go, and game domains once ruled by Alpha-Beta such as Lines of Action, Monte-Carlo methods appear to have limits too as they fall short in tactical domains such as Hex and Chess. In a continuation of this type of research, two new games, Crossings and Epaminondas, are presented, analyzed and used to test two Monte-Carlo based algorithms: Upper Confidence Bounds applied to Trees (UCT) and Heuristic Guided UCT (HUCT). Results indicate that heuristic knowledge can positively affect UCT\u27s performance in the lower complexity domain of Crossings. However, both agents perform worse in the higher complexity domain of Epaminondas. This identifies Epaminondas as another domain that poses difficulties for Monte Carlo agents

Ludii as a Competition Platform

Ludii is a general game system being developed as part of the ERC-funded Digital Ludeme Project (DLP). While its primary aim is to model, play, and analyse the full range of traditional strategy games, Ludii also has the potential to support a wide range of AI research topics and competitions. This paper describes some of the future competitions and challenges that we intend to run using the Ludii system, highlighting some of its most important aspects that can potentially lead to many algorithm improvements and new avenues of research. We compare and contrast our proposed competition motivations, goals and frameworks against those of existing general game playing competitions, addressing the strengths and weaknesses of each platform

Inductive logic programming at 30: a new introduction

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.Comment: Paper under revie

Making sense of sensory input

This paper attempts to answer a central question in unsupervised learning: what does it mean to "make sense" of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory -- objects, properties, and laws -- must be integrated into a coherent whole. On our account, making sense of sensory input is a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence

Gerador automático de cenários para jogos genéricos

Jogos Genéricos (do inglês General Game Playing) é uma área da Inteligência Artificial que consiste no desenvolvimento de agentes capazes de jogar qualquer jogo, sem conhecimento prévio das regras. Estes agentes são capazes de jogar jogos bastante distintos, como o Xadrez ou o Quatro em Linha, tendo acesso às regras do jogo somente quando o jogo se inicia. À medida que o jogo decorre os agentes adaptam a sua estratégia de forma a obter o melhor resultado possível. Atualmente, as interfaces gráficas destes jogos são bastante pobres, tipicamente em 2D, com uma palete de cor reduzida e elementos de jogo simplistas, o que contribui para uma fraca experiência de utilizador. Além disso, estas interfaces não são genéricas, ou seja, cada jogo possui uma interface própria, construída especificamente para o jogo, e como tal não pode ser reutilizada para outro jogo sem as devidas modificações necessárias. Esta dissertação tem o objetivo de enriquecer a área dos jogos genéricos ao criar um gerador automático de cenários para jogos genéricos capaz de oferecer uma interface de jogo elaborada e funcionalidades úteis para o utilizador, em que os cenários são os elementos gráficos que compõem o jogo, como as peças e tabuleiro. O gerador de cenários possui uma estrutura genérica, que interpreta a informação relativa ao jogo disponibilizada pelo motor de jogos genéricos usado, e cria uma interface de jogos genéricos. Com JavaScript, a tecnologia utilizada para criar a interface gráfica dos jogos genéricos, foi possível a criação de ambientes 3D com interações intuitivas, podendo ser usada num computador ou dispositivo móvel. As funcionalidades presentes dão um maior controlo quanto à interface gráfica do jogo, em relação a alternativas existentes, como escolher um estilo de jogo ou funcionalidades que têm em conta necessidades do utilizador (e.g., sistema de cores adaptado à condição de daltonismo). Para analisar a experiência de utilização do gerador de cenários, identificar possíveis melhorias e recolher a opinião quanto ao interesse em contribuir na área do jogos genéricos, realizaram-se testes com 30 utilizadores. Os resultados obtidos indicam que o gerador de cenários oferece uma experiência de utilização e interação intuitivas, e que pode ser expandido e personalizado, adaptando-se a um grande número de jogos, devido às suas caracterísiticas genéricas. Através do gerador de cenários a experiência dos jogos genéricos é melhorada, potenciando assim a atração de mais utilizadores para a comunidade.General Game Playing is an Artificial Intelligence field of study that consists in the development of agents capable of playing any game without prior knowledge of the rules. These agents are able to play quite distinct games, such as Chess or Four in Line, by having access to the rules of the game only when the game starts. As the game progresses the agents adapt their strategy in order to achieve the best possible outcome. Currently, the graphical interface of these games is rather poor, typically 2D, with a reduced color palette and simplistic game elements, which contributes to a poor user experience. Another limiting aspect of current graphical interfaces for general game playing is that the interface is not generic, that is, each game has its own interface, having been built specifically for that game, and it cannot be reused for another game without the proper modifications. The purpose of this dissertation is to enrich the area of generic games by creating an automatic generator of game scenarios for generic games capable of offering an elaborate game interface and useful user functionalities, in which the scenarios are the graphical elements that make the game, such as the game pieces and the game board. The scenarios generator has a generic structure, that interprets the game information available by a used generic game engine, and creates a generic game interface. With JavaScript, the technology used to create the graphical interface of generic games, it is possible to create 3D environments with intuitive interactions, which can be used on a computer or mobile device. The present features give greater control over the graphical interface of the game, comparing with other existing alternatives, such as choosing a game style or functionalities that respect specific user needs (e.g., color mode suitable to the color blindness condition). In order to analyze the experience of using the scenarios generator, identify possible improvement points and gather the opinion about the interest in contributing to the generic games field, tests were carried out with 30 users. The results indicate that the scenarios generator offers an intuitive user experience and interaction, and it can be increased and customized, adapting to a great number of games, thanks to its generic characteristics. With the scenarios generator, the generic games user experience is improved, suggesting the ability to attract more users to the community