Collaborative computer personalities in the game of chess

Computer chess has played a crucial role in Artificial Intelligence research since the creation of the modem computer. It has gained this prominent position due to the large domain that it encompasses, including psychology, philosophy and computer science. The new and innovative techniques initially created for computer chess have often been successfully transferred to other divergent research areas such as theorem provers and economic models. The progress achieved by computers in the game of chess has been illustrated by Deep Blue’s famous victory over Garry Kasparov in 1997. However, further improvements are required if more complex problems are to be solved. In 1999 the Kasparov versus the World match took place over the Internet. The match allowed chess players from around the world to collaborate in a single game of chess against the then world champion, Garry Kasparov. The game was closely fought with Kasparov coming out on top. One of the most surprising aspects of the contest was the high quality of play achieved by the World team. The World team consisted of players with varying skill and style of play, despite this they achieved a level of play that was considered better than any of its individual members. The purpose of this research is to investigate if collaboration by different players can be successfully transferred to the domain of computer chess

Deep Learning: A Philosophical Introduction

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs, and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally-accepted explanation as to why they work so well. This article provides an introduction to these networks, as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success, and ends by proposing ten areas of research that would benefit from future engagement by philosophers of mind, epistemology, science, perception, law, and ethics

The CHREST architecture of cognition : the role of perception in general intelligence

Original paper can be found at: http://www.atlantis-press.com/publications/aisr/AGI-10/ Copyright Atlantis Press. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.This paper argues that the CHREST architecture of cognition can shed important light on developing artificial general intelligence. The key theme is that "cognition is perception." The description of the main components and mechanisms of the architecture is followed by a discussion of several domains where CHREST has already been successfully applied, such as the psychology of expert behaviour, the acquisition of language by children, and the learning of multiple representations in physics. The characteristics of CHREST that enable it to account for empirical data include: self-organisation, an emphasis on cognitive limitations, the presence of a perception-learning cycle, and the use of naturalistic data as input for learning. We argue that some of these characteristics can help shed light on the hard questions facing theorists developing artificial general intelligence, such as intuition, the acquisition and use of concepts and the role of embodiment

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

Can Deep Blue™ make us happy? Reflections on human and artificial expertise

Sadly, progress in AI has confirmed earlier conclusions, reached using formal domains, about the strict limits of human information processing and has also shown that these limits are only partly remedied by intuition. More positively, AI offers mankind a unique avenue to circumvent its cognitive limits: (1) by acting as a prosthesis extending processing capacity and size of the knowledge base; (2) by offering tools for studying our own cognition; and (3) as a consequence of the previous item, by developing tools that increase the quality and quantity of our own thinking. These ideas are illustrated with chess expertise

Expertise in chess

This chapter provides an overview of research into chess expertise. After an historical background and a brief description of the game and the rating system, it discusses the information processes enabling players to choose good moves, and in particular the trade-offs between knowledge and search. Other topics include blindfold chess, talent, and the role of deliberate practice and tournament experience

On The Foundations of Digital Games

Computers have lead to a revolution in the games we play, and, following this, an interest for computer-based games has been sparked in research communities. However, this easily leads to the perception of a one-way direction of influence between that the field of game research and computer science. This historical investigation points towards a deep and intertwined relationship between research on games and the development of computers, giving a richer picture of both fields. While doing so, an overview of early game research is presented and an argument made that the distinction between digital games and non-digital games may be counter-productive to game research as a whole

AI Researchers, Video Games Are Your Friends!

If you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question "what can video games do for AI", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question "what can AI do for video games", and lays out a vision for what video games might look like if we had significantly more advanced AI at our disposal. The chapter is based on my keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad audience.Comment: in Studies in Computational Intelligence Studies in Computational Intelligence, Volume 669 2017. Springe