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In this article, the author discusses aspects of the paper by Steve Minton and colleagues on large-scale constraint satisfaction in artificial intelligence (AI) research, which won the 2008 Classic Paper Award. He discusses the min-conflicts method applied in the research to solve constraint-satisfaction problems (CSP) in scheduling tasks. He says that the paper was able to provide immediate solutions to National Aeronautics & Space Administration (NASA) problems and advance AI research theory.

Abstract: The delineation of functional economic areas, or market areas, is a problem of high practical relevance, since the delineation of functional sets such as economic areas in the US, Travel-to-Work Areas in the United Kingdom, and their counterparts in other OECD countries are the basis of many statistical operations and policy making decisions at local level. This is a combinatorial optimisation problem defined as the partition of a given set of indivisible spatial units (covering a territory) into regions characterised by being (a) self-contained and (b) cohesive, in terms of spatial interaction data (flows, relationships). Usually, each region must reach a minimum size and self-containment level, and must be continuous. Although these optimisation problems have been typically solved through greedy methods, a recent strand of the literature in this field has been concerned with the use of evolutionary algorithms with ad hoc operators. Although these algorithms have proved to be successful in improving the results of some of the more widely applied official procedures, they are so time consuming that cannot be applied directly to solve real-world problems. In this paper we propose a new set of group-based mutation operators, featuring general operations over disjoint groups, tailored to ensure that all the constraints are respected during the operation to improve efficiency. A comparative analysis of our results with those from previous approaches shows that the proposed algorithm systematically improves them in terms of both quality and processing time, something of crucial relevance since it allows dealing with most large, real-world problems in reasonable time. [Copyright &y& Elsevier]

A relatively recent innovation in research on attitude constraint is the specification of hierarchical models of mass belief systems, where general orientations are assumed to determine or constrain more specific policy attitudes. But while this research has been able to demonstrate a strong correlation between general and specific idea elements, the causal direction of the relationship has been assumed rather than tested. Using panel data collected during a period of constancy in the international environment, we attempt to untangle the causal ordering of general orientations and specific policy attitudes in the realm of international politics. In accord with hierarchical models, we find, first, that general orientations (e.g., militarism and containment postures) are more stable than many specific policy preferences (e.g., attitudes toward defense spending and U.S. involvement in Central America) and, second, much of the over-time consistency in policy attitudes is generated by these more general orientations. We conclude with a discussion of the implications of this work for studies of attitude constraint, one of which is the general applicability of the procedure for investigating top-down versus bottom-up models of constraint in domains outside the international realm. [ABSTRACT FROM AUTHOR]