Andrew J. Parkes

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Automating the design of heuristic search methods is an active research field within computer science, artificial intelligence and operational research. In order to make these methods more generally applicable, it is important to eliminate or reduce the role of the human expert in the process of designing an effective methodology to solve a given(More)
B. McCollum, P. McMullan School of Electronics, Electrical Engineering and Computer Science, Queen’s University, Belfast, N. Ireland, BT7 1NN, b.mccollum@qub.ac.uk B. Paechter Centre for Emergent Computing, Napier University, Edinburgh, EH10 5DT, Scotland, b.paechter@napier.ac.uk R. Lewis Cardiff Business School, Prifysgol Caerdydd / Cardiff University,(More)
When search techniques are used to solve a practical problem, the solution produced is often brittle in the sense that small execution difficulties can have an arbitrarily large effect on the viability of the solution. The AI community has responded to this difficulty by investigating the development of “robust problem solvers” that are intended to be proof(More)
Many problem ensembles exhibit a phase transition that is associated with a large peak in the average cost of solving the problem instances. However, this peak is not necessarily due to a lack of solutions: indeed the average number of solutions is typically exponentially large. Here, we study this situation within the context of the satissability(More)
This paper describes a branch-and-cut procedure for an extension of the bounded colouring problem, generally known as curriculum-based university course timetabling. In particular, we focus on Udine Course Timetabling [di Gaspero and Schaerf, J. Math. Model. Algorithms 5:1], which has been used in Track 3 of the 2007 International Timetabling Competition.(More)
The first Cross-domain Heuristic Search Challenge (CHeSC 2011) seeks to bring together practitioners from operational research, computer science and artificial intelligence who are interested in developing more generally applicable search methodologies. The challenge is to design a search algorithm that works well, not only across different instances of the(More)
There are several powerful solvers for satisfiability (SAT), such as wsat, Davis-Putnam, and relsat. However, in practice, the SAT encodings often have so many clauses that we exceed physical memory resources on attempting to solve them. This excessive size often arises because conversion to SAT, from a more natural encoding using quantifications over(More)