Playing Smart - Artificial Intelligence in Computer Games

Abstract: 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

A framework for analyzing RFID distance bounding protocols

Many distance bounding protocols appropriate for the RFID technology have been proposed recently. Unfortunately, they are commonly designed without any formal approach, which leads to inaccurate analyzes and unfair comparisons. Motivated by this need, we introduce a unied framework that aims to improve analysis and design of distance bounding protocols. Our framework includes a thorough terminology about the frauds, adversary, and prover, thus disambiguating many misleading terms. It also explores the adversary's capabilities and strategies, and addresses the impact of the prover's ability to tamper with his device. It thus introduces some new concepts in the distance bounding domain as the black-box and white-box models, and the relation between the frauds with respect to these models. The relevancy and impact of the framework is nally demonstrated on a study case: Munilla-Peinado distance bounding protocol

Data assurance in opaque computations

The chess endgame is increasingly being seen through the lens of, and therefore effectively defined by, a data ‘model’ of itself. It is vital that such models are clearly faithful to the reality they purport to represent. This paper examines that issue and systems engineering responses to it, using the chess endgame as the exemplar scenario. A structured survey has been carried out of the intrinsic challenges and complexity of creating endgame data by reviewing the past pattern of errors during work in progress, surfacing in publications and occurring after the data was generated. Specific measures are proposed to counter observed classes of error-risk, including a preliminary survey of techniques for using state-of-the-art verification tools to generate EGTs that are correct by construction. The approach may be applied generically beyond the game domain

Skill Rating by Bayesian Inference

Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players’ skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of games by players across a broad FIDE Elo range, and is in principle applicable to any scenario where high-value decisions are being made under pressure