AI Research Group

This is the website for the Artificial Intelligence Research Group at the University of St Andrews. Find out who our members are on the People page. Have a look at some of our Research Projects. Or take a look at all the papers published by our members on the Publications page.

The Artificial Intelligence Research Group’s work in artificial intelligence and symbolic computing is concerned with how we can make use of computers to augment human mathematical and logical capabilities and in investigating how to automate human capabilities such as face recognition. We are very active in the field of Constraint Programming, where our interests are constraint modeling and design of efficient constraint solvers – such of Minion.

News and Events

The latest AI Research Group posts from the School of Computer Science blog.

SRG Seminar: “Interactional Justice vs. The Paradox of Self-Amendment and the Iron Law of Oligarchy” by Jeremy Pitt


Event details

  • When: 15th November 2017 13:00 - 14:00
  • Where: Cole 1.33a
  • Series: Systems Seminars Series
  • Format: Seminar

Self-organisation and self-governance offer an effective approach to resolving collective action problems in multi-agent systems, such as fair and sustainable resource allocation. Nevertheless, self-governing systems which allow unrestricted and unsupervised self-modification expose themselves to several risks, including the Suber’s paradox of self-amendment (rules specify their own amendment) and Michel’s iron law of oligarchy (that the system will inevitably be taken over by a small clique and be run for its own benefit, rather than in the collective interest). This talk will present an algorithmic approach to resisting both the paradox and the iron law, based on the idea of interactional justice derived from sociology, and legal and organizational theory. The process of interactional justice operationalised in this talk uses opinion formation over a social network with respect to a shared set of congruent values, to transform a set of individual, subjective self-assessments into a collective, relative, aggregated assessment.

Using multi-agent simulation, we present some experimental results about detecting and resisting cliques. We conclude with a discussion of some implications concerning institutional reformation and stability, ownership of the means of coordination, and knowledge management processes in ‘democratic’ systems.

Biography
Photograph of Professor Jeremy Pitt
Jeremy Pitt is Professor of Intelligent and Self-Organising Systems in the Department of Electrical & Electronic Engineering at Imperial College London, where he is also Deputy Head of the Intelligent Systems & Networks Group. His research interests focus on developing formal models of social processes using computational logic, and their application in self-organising multi-agent systems, for example fair and sustainable common-pool resource management in ad hoc and sensor network. He also has strong interests in human-computer interaction, socio-technical systems, and the social impact of technology; with regard to the latter he has edited two books, This Pervasive Day (IC Press, 2012) and The Computer After Me (IC Press, 2014). He has been an investigator on more than 30 national and European research projects and has published more than 150 articles in journals and conferences. He is a Senior Member of the ACM, a Fellow of the BCS, and a Fellow of the IET; he is also an Associate Editor of ACM Transactions on Autonomous and Adaptive Systems and an Associate Editor of IEEE Technology and Society Magazine.


n-Queens Completion is NP-Complete


Peter Nightingale and Ian Gent at Falkland Palace, Wednesday, 17 August 2017.
©Stuart Nicol Photography, 2017

Ian Gent, Christopher Jefferson and Peter Nightingale have shown that a classic chess puzzle is NP-Complete. Their paper “Complexity of n-Queens Completion” was published in the Journal of Artificial Intelligence Research on August 30.

The n-Queens puzzle is a classic chess problem: given a chessboard of size n by n, can you place n queens so that no two queens attack each other?  That is, can you place the queens with no two queens are on the same row, column, or diagonal? The n-Queens puzzle has long been known to be simple to solve:  you can solve the problem for all n except 2 and 3, and solutions for all other n can be described in a few lines.  This very simplicity has led to repeated controversy in Artificial Intelligence (AI). The n-Queens puzzle is often used as a benchmark problem, but good results on the problem can always be challenged because the problem is so simple to solve without using AI methods.

The new work follows a challenge on Facebook on New Year’s Day, 2015, when a friend of Ian’s asked him how hard n-Queens is if some queens were already placed on the board.  It turns out, this version (dating from 1850) of the puzzle is only two years younger than the more famous n-Queens problem. The picture shows Peter (left) and Ian (right) with queens on the board at positions suggested by Nauck in 1850, the squares b4 and d5.  Can you put another 6 queens on the board so that the entire board is a solution of 8-Queens?  The general version with some number of queens preplaced on an n by n board is the n-Queens Completion puzzle.

 

With queens at b4 and d5, can you place 6 more queens to get a solution to the 8-queens puzzle? ©Stuart Nicol Photography, 2017

Ian, Christopher and Peter have shown that the n-Queens puzzle is in fact hard, not simple.  It belongs to the complexity classes NP-Complete and #P-Complete. Other NP-Complete problems include the “travelling salesperson problem”, finding cliques in graphs, and many other important problems, from scheduling to circuit layout. This puts n-Queens Completion at the centre of the most important theoretical problem in computer science — it has long been known that either all NP-complete problems are easy, or none of them are. Most computer scientists believe that this means there is no efficient algorithm possible for solving this problem, compared to the very simple techniques long known for n-Queens.
The importance of this work is that it provides researchers with a benchmark that can be used for evaluating AI techniques. Moreover, it helps to explain why so many AI techniques have been used on the n-Queens puzzle. Most techniques do most of their work with some queens partially placed, using some form of (hopefully intelligent) trial and error. In fact it turns out that many researchers – in order to solve a simple problem – have solved it by turning the simple problem of n-Queens into the hard problem of n-Queens Completion.
It does seem that AI researchers should not use n-Queens as a benchmark, but the very closely related n-Queens Completion puzzle is a valid benchmark. As well as the theoretical results, the paper shows how example instances can be generated which appear to be hard in practice. Some caution is still needed, though. It does seem to be quite hard to generate hard instances of n-Queens Completion.
The University has also issued an article on the same paper, under the title “Simple” chess puzzle holds key to $1m prize