Ludii as a Competition Platform

2020

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently prop...

2019

Ludii is a new general game system, currently under development, which aims to support a wider range of games than existing systems and approaches. It is being developed primarily for the task of game design, but o↵ers a number of other potential benefits for game and AI researchers, professionals and hobbyists. This paper is based on an interactive demonstration of Ludii at thuis year’s Advances in Computer Games conference (ACG 2019). It describes the approach behind Ludii, how it works, how it is used, and what it can potentially do.

AI magazine, 2005

A General Game Playing System is one that can accept a formal description of a game and play the game effectively without human intervention. Unlike specialized game players, such as Deep Blue, general game players do not rely on algorithms designed in advance for specific games; and, unlike Deep Blue, they are able to play different kinds of games. In order to promote work in this area, the AAAI is sponsoring an open competition at this summer's National Conference. This article is an overview of the technical issues and logistics associated with this summer's competition, as well as the relevance of General Game Playing to the long range goals of artificial intelligence.

2006

Real-time strategy (RTS) games are complex decision domains which require quick reactions as well as strategic planning. In this paper we describe the first RTS game AI tournament, which was held in June 2006, and the programs that participated.

Games have played a prominent role as a test bed for advancements in the field of artificial intelligence ever since its foundation over half a century ago, resulting in highly specialized world-class game-playing systems being developed for various games. The establishment of the International General Game Playing Competition in 2005, however, resulted in a renewed interest in more general problem-solving approaches to game playing. In general game playing (GGP) the goal is to create game-playing systems that autonomously learn how to play a wide variety of games skillfully, given only the descriptions of the game rules. In this paper we review the history of the competition, discuss progress made so far, and list outstanding research challenges.

2020

This paper introduces Tribes, a new turn-based strategy game framework. Tribes is a multi-player, multi-agent, stochastic and partially observable game that involves strategic and tactical combat decisions. A good playing strategy requires the management of a technology tree, build orders and economy. The framework provides a Forward Model, which can be used by Statistical Forward Planning methods. This paper describes the framework and the opportunities for Game AI research it brings. We further provide an analysis on the action space of this game, as well as benchmarking a series of agents (rule based, one step look-ahead, Monte Carlo, Monte Carlo Tree Search, and Rolling Horizon Evolution) to study their relative playing strength. Results show that although some of these agents can play at a decent level, they are still far from human playing strength.

2019 IEEE Conference on Games (CoG), 2019

Although General Game Playing (GGP) systems can facilitate useful research in Artificial Intelligence (AI) for gameplaying, they are often computationally inefficient and somewhat specialised to a specific class of games. However, since the start of this year, two General Game Systems have emerged that provide efficient alternatives to the academic state of the artthe Game Description Language (GDL). In order of publication, these are the Regular Boardgames language (RBG), and the Ludii system. This paper offers an experimental evaluation of Ludii. Here, we focus mainly on a comparison between the two new systems in terms of two key properties for any GGP system: simplicity/clarity (e.g. human-readability), and efficiency. Index Terms-General Game Playing, knowledge representation, General Game AI.

Computer strategy games 1 — games such as those in the Civilization, StarCraft, Age of Empires and Total War series, and board game adaptations such as Risk and Axis and Allies — have been popular since soon after computer games were invented, and are a popular genre among a wide range of players. Strategy games are closely related to classic board games such as Chess and Go, but though there has been no shortage of work on AI for playing classic board games, there has been remarkably little work on strategy games. This chapter addresses the understudied question of how to create AI that plays strategy game, through building and comparing AI for general strategy game playing.

Proceedings of the AAAI Conference on Artificial Intelligence

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.

IEEE Transactions on Games

Game AI competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tools to facilitate the research on their core topics and provide means for comparing and sharing results. A competition is also a way to motivate new researchers to study these challenges. In this document, we present the Geometry Friends Game AI Competition. Geometry Friends is a two-player cooperative physics-based puzzle platformer computer game. The concept of the game is simple, though its solving has proven to be difficult. While the main and apparent focus of the game is cooperation, it also relies on other AI related problems such as planning, plan execution, and motion control, all connected to situational awareness. All of these must be solved in real-time. In this paper we discuss the competition and the challenges it brings, and present an overview of the current solutions. Index Terms-game AI, collaboration, cooperation, team AI, AI competition, task and motion planning, physics-based game.