Can we evaluate a logic program declaratively? That is, can a logic program be evaluated correctly and efficiently, independent of query modes and rule/predicate ordering, finding a complete set of answers, and termin...
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Can we evaluate a logic program declaratively? That is, can a logic program be evaluated correctly and efficiently, independent of query modes and rule/predicate ordering, finding a complete set of answers, and terminating properly? the answer could be "yes", at least for a good subclass of logic programs, based on our investigation and experimentation using a deductive database approach. In this paper, an n-queens problem, a classical logic program, is used as a running example to demonstrate the methodology. Our analysis shows that binding analysis and constraint exploration are two essential issues in the realization of declarative logic programming. The limitations of our methodology are also discussed in the paper. (C) 1998 Elsevier Science Inc. All rights reserved.
Redistricting is the process of dividing a geographic area consisting of spatial units-often represented as spatial polygons-into smaller districts that satisfy some properties. It can therefore be formulated as a set...
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Redistricting is the process of dividing a geographic area consisting of spatial units-often represented as spatial polygons-into smaller districts that satisfy some properties. It can therefore be formulated as a set partitioning problem where the objective is to cluster the set of spatial polygons into groups such that a value function is maximized [ 1]. Widely used algorithms developed for point-based data sets are not readily applicable because polygons introduce the concepts of spatial contiguity and other topological properties that cannot be captured by representing polygons as points. Furthermore, when clustering polygons, constraints such as spatial contiguity and unit distributedness should be strategically addressed. Toward this, we have developed the Constrained Polygonal Spatial Clustering (CPSC) algorithm based on the A* search algorithm that integrates cluster-level and instance-level constraints as heuristic functions. Using these heuristics, CPSC identifies the initial seeds, determines the best cluster to grow, and selects the best polygon to be added to the best cluster. We have devised two extensions of CPSC-CPSC* and CPSC*-PS-for problems where constraints can be soft or relaxed. Finally, we compare our algorithm with graph partitioning, simulated annealing, and genetic algorithm-based approaches in two applications-congressional redistricting and school districting.
The drive toward sustainable wastewater management is challenging the conventional paradigm of linear end-of-pipe solutions. A shift toward more sustainable solutions requires that information about new ideas, systems...
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The drive toward sustainable wastewater management is challenging the conventional paradigm of linear end-of-pipe solutions. A shift toward more sustainable solutions requires that information about new ideas, systems, and technologies be more readily accessible for addressing wastewater problems. It is commonly argued that decision-making needs to involve engineers and other community representatives to define values and brainstorm solutions. This paper describes a decision support system (DSS) prototype that is designed to help community planners identify solutions which balance environmental, economic, and social goals. The system is designed to be scalable, adaptable, and flexible to allow fair assessment of new ideas and technologies. It supports the exploration of consequences of various alternatives and visualizes the tradeoffs between them. Our DSS takes in modular descriptions of components and a description of a community context, automates the design of alternative wastewater systems, and facilitates evaluating how well each design satisfies the given context. It provides an adaptable platform from which new solutions can be designed without having to predefine how a single component fits within a specific system. Our DSS facilitates the exploration of alternative solutions by visualizing the effect of various tradeoffs and their consequences in relation to the community's sustainability goals.
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