Ludii -- The Ludemic General Game System

2018

Rule-based declarative formalisms enjoy several advantages when compared with imperative solutions, especially when dealing with AI-based application development: solid theoretical bases, no need for algorithm design or coding, explicit and easily modifiable knowledge bases, executable declarative specifications, fast prototyping, quick error detection, modularity. For these reasons, ways for combining declarative paradigms, such as Answer Set Programming (ASP), with traditional ones have been significantly studied in the recent years; there are however relevant contexts, in which this road is unexplored, such as development of real-time games. In such a setting, the strict requirements on reaction times, the presence of computer-human interactivity and a generally increased impedance between the two development paradigms make the task nontrivial. In this work we illustrate how to embed rule-based reasoning modules into the well-known Unity game development engine. To this end, we p...

IEEE Computational Intelligence Magazine, 2000

We propose to return to the roots of Artificial/Computational Intelligence applicability to board games domain by attempting to mimic human way of playing (or human intelligence in a broader perspective). The paper provides the argumentation for potential virtues of developing cognitively-plausible pattern-based human-like playing systems. Such systems, besides playing games, may in principle, also be applied to other domains related to general problem-solving.

ArXiv, 2021

We developed a system able to automatically solve logical puzzles in natural language. Our solution is composed by a parser and an inference module. The parser translates the text into first order logic (FOL), while the MACE4 model finder is used to compute the models of the given FOL theory. We also empower our software agent with the capability to provide Yes/No answers to natural language questions related to each puzzle. Moreover, in line with Explainalbe Artificial Intelligence (XAI), the agent can back its answer, providing a graphical representation of the proof. The advantage of using reasoning for Natural Language Understanding (NLU) instead of Machine learning is that the user can obtain an explanation of the reasoning chain. We illustrate how the system performs on various types of natural language puzzles, including 382 knights and knaves puzzles. These features together with the overall performance rate of 80.89% makes the proposed solution an improvement upon similar s...

Electronic Notes in Theoretical Computer Science, 2005

Artificial intelligence games are a very interesting tool for teaching Artificial Intelligence techniques. Competitors write programs for agents, which are supposed to complete a given task or fight against other agents. In order to achieve the best performance, programs may have to use advanced Artificial Intelligence methods. In this paper, we present a framework to build artificial intelligence games, using Abstract State Machines (ASM) for the specification of the rules of the games. Choosing ASM, we expect that the competitors will be able to understand clearly the semantics of the rules. The framework includes a compiler for an ASM-based language, allows complete control of the order of execution of agents and easy integration with graphical libraries.

Designing and implementing AI in games is an interesting, yet complex task. This paper briefly presents some applications that make use of Answer Set Programming for such a task, and show some advantages of declarative programming frameworks against imperative (algorithmic) approaches while dealing with knowledge representation and reasoning: solid theoretical bases, no need for algorithm design or coding, explicit (and thus easily modifiable/upgradeable) knowledge representation, declarative specifications which are already executable, very fast prototyping, quick error detection, modularity.

2010

This paper presents a first attempt to bridge the gap between logical and cognitive treatments of strategic reasoning in games. The focus of the paper is backward induction, a principle which is purported to follow from common knowledge of rationality by Zermelo's theorem. There have been extensive formal debates about the merits of principle of backward induction among game theorists and logicians. Experimental economists and psychologists have shown that human subjects, perhaps due to their bounded resources, do not always follow the backward induction strategy, leading to unexpected outcomes. Recently, based on an eye-tracker study, it has turned out that even human subjects who produce the outwardly correct 'backward induction answer' use a different internal reasoning strategy to achieve it. This paper presents a formal language to represent different strategies on a finer-grained level than was possible before. The language and its semantics may lead to precisely distinguishing different cognitive reasoning strategies, that can then be tested on the basis of computational cognitive models and experiments with human subjects.

This chapter is a direct follow-up to the chapter on General Video Game Playing (GVGP). As that group recognised the need to create a Video Game Description Language (VGDL), we formed a group to address that challenge and the results of that group is the current chapter. Unlike the VGDL envisioned in the previous chapter, the language envisioned here is not meant to be supplied to the game-playing agent for automatic reasoning; instead we argue that the agent should learn this from interaction with the system. The main purpose of the language proposed here is to be able to specify complete video games, so that they could be compiled with a special VGDL compiler. Implementing such a compiler could provide numerous opportunities; users could modify existing games very quickly, or have a library of existing implementations defined within the language (e.g. an Asteroids ship or a Mario avatar) that have pre-existing, parameterised behaviours that can be customised for the users specific...

This report documents the program and the outcomes of Dagstuhl Seminar 12191 "Artificial and Computational Intelligence in Games". The aim for the seminar was to bring together creative experts in an intensive meeting with the common goals of gaining a deeper understanding of various aspects of artificial and computational intelligence in games, to help identify the main challenges in game AI research and the most promising venues to deal with them. This was accomplished mainly by means of workgroups on 14 different topics (ranging from search, learning, and modeling to architectures, narratives, and evaluation), and plenary discussions on the results of the workgroups. This report presents the conclusions that each of the workgroups reached. We also added short descriptions of the few talks that were unrelated to any of the workgroups.

Künstliche Intelligenz, 2011

Although we humans cannot compete with computers at simple brute-force search, this is often more than compensated for by our ability to discover structures in new games and to quickly learn how to perform highly selective, informed search. To attain the same level of intelligence, general game playing systems must be able to figure out, without human assistance, what a new game is really about. This makes General Game Playing in ideal testbed for human-level AI, because ultimate success can only be achieved if computers match our ability to master new games by acquiring and exploiting new knowledge. This article introduces five knowledge-based methods for General Game Playing. Each of these techniques contributes to the ongoing success of our FLUXPLAYER (Schiffel and Thielscher in Proceedings of the National Conference on Artificial Intelligence, pp. 1191–1196, 2007), which was among the top four players at each of the past AAAI competitions and in particular was crowned World Champion in 2006.

With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games.