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.

This paper presents an analysis of three major contests for machine intelligence. We conclude that a new era for Turing’s test requires a fillip in the guise of a committed sponsor, not unlike DARPA, funders of the successful 2007 Urban Challenge.

Abstract: The purpose of this paper is to consider Turing's two tests for machine intelligence: the parallel-paired, three-participants game presented in his 1950 paper, and the “jury-service” one-to-one measure described two years later in a radio broadcast. Both versions were instantiated in practical Turing tests during the 18th Loebner Prize for artificial intelligence hosted at the University of Reading, UK, in October 2008. This involved jury-service tests in the preliminary phase and parallel-paired in the final phase

"Abstract of chapter in book (Eds: Jordi Vallverdú & David Casacuberta): 'Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence' The Turing Test, originally configured as a game for a human to distinguish between an unseen and unheard man and woman, through a text-based conversational measure of gender, is the ultimate test for deception and hence, thinking. So conceived Alan Turing when he introduced a machine into the game. His idea, that once a machine deceives a human judge into believing that they are the human, then that machine should be attributed with intelligence. What Turing missed is the presence of emotion in human dialogue, without expression of which, an entity could appear non-human. Indeed, humans have been confused as machine-like, the confederate effect, during instantiations of the Turing Test staged in Loebner Prizes for Artificial Intelligence. We present results from recent Loebner Prizes and two parallel conversations from the 2006 contest in which two human judges, both native English speakers, each concomitantly interacted with a non-native English speaking hidden-human, and jabberwacky, the 2005 and 2006 Loebner Prize bronze prize winner for most human-like machine. We find that machines in those contests appear conversationally worse than non-native hidden-humans, and, as a consequence attract a downward trend in highest scores awarded to them by human judges in the 2004, 2005 and 2006 Loebner Prizes. Analysing Loebner 2006 conversations, we see that a parallel could be drawn with autistics: the machine was able to broadcast but it did not inform; it talked but it did not emote. The hidden-humans were easily identified through their emotional intelligence, ability to discern emotional state of others and contribute with their own ‘balloons of textual emotion’." http://www.igi-global.com/book/handbook-research-synthetic-emotions-sociable/507

Chatterbox Challenge is an annual web-based contest for artificial conversational systems, ACE. The 2010 instantiation was the tenth consecutive contest held between March and June in the 60th year following the publication of Alan Turing’s influential disquisition ‘computing machinery and intelligence’. Loosely based on Turing’s viva voca interrogator-hidden witness imitation game, a thought experiment to ascertain a machine’s capacity to respond satisfactorily to unrestricted questions, the contest provides a platform for technology comparison and evaluation. This paper provides an insight into emotion content in the entries since the 2005 Chatterbox Challenge. The authors find that synthetic textual systems, none of which are backed by academic or industry funding, are, on the whole and more than half a century since Weizenbaum’s natural language understanding experiment, little further than Eliza in terms of expressing emotion in dialogue. This may be a failure on the part of the academic AI community for ignoring the Turing test as an engineering challenge.

"In journal: Minds and Machines, Vol. 20, issue 3: pp 441-454, 2010 http://www.springerlink.com/content/ul03135815l85553/ Based on insufficient evidence, and inadequate research, Floridi and his students report inaccuracies and draw false conclusions in their Minds and Machines evaluation, which this paper aims to clarify. Acting as invited judges, Floridi et al. participated in nine, of the ninety-six, Turing tests staged in the finals of the 18th Loebner Prize for Artificial Intelligence in October 2008. From the transcripts it appears that they used power over solidarity as an interrogation technique. As a result, they were fooled on several occasions into believing that a machine was a human and that a human was a machine. Worse still, they did not realise their mistake. This resulted in a combined correct identification rate of less than 56%. In their paper they assumed that they had made correct identifications when they in fact had been incorrect. [Downloaded 217 times from Minds and Machines, highest in 90 days to 15 April 2011: http://www.springer.com/computer/ai/journal/11023]"

An evaluation of the 2008 Loebner contest

One of the differences between natural conversational entities – NCE (humans) and artificial conversational entities – ACE (such as Carpenter’s Jabberwacky), is the ability the former have to constrain random output during dialogue. When humans want to participate in, and pursue conversation with each other they maintain coherent dialogue through contextual relevance, create metaphors – fusing seemingly unrelated ideas to ensure abstract points are understood, and indicate topic change at mutually acceptable junctures.

On a literal reading of `Computing Machinery and Intelligence', Alan Turing presented not one, but two, practical tests to replace the question `Can machines think?' He presented them as equivalent. I show here that the first test described in that much-discussed paper is in fact not equivalent to the second one, which has since become known as `the Turing Test'. The two tests can yield different results; it is the first, neglected test that provides the more appropriate indication of intelligence. This is because the features of intelligence upon which it relies are resourcefulness and a critical attitude to one's habitual responses; thus the test's applicablity is not restricted to any particular species, nor does it presume any particular capacities. This is more appropriate because the question under consideration is what would count as machine intelligence. The first test realizes a possibility that philosophers have overlooked: a test that uses a human's linguistic performance in setting an empirical test of intelligence, but does not make behavioral similarity to that performance the criterion of intelligence. Consequently, the first test is immune to many of the philosophical criticisms on the basis of which the (so-called) `Turing Test' has been dismissed.