Autonomous agents are a challenging concept for future unmanned air operations in hostile environments. Previous aeronautic missions highlight the lack of on-board reasoning abilities to increase the decision making c...
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ISBN:
(纸本)0780367227
Autonomous agents are a challenging concept for future unmanned air operations in hostile environments. Previous aeronautic missions highlight the lack of on-board reasoning abilities to increase the decision making capability, to efficiently react to unexpected events or to adapt plans to unexpected situation changes. If many agent-based system approaches exhibit reasoning functionalities, the complexity of air missions prevents from using the underlying generic models onto realistic missions. By taking advantage of constraint programming techniques, this paper demonstrates how a dedicated planning method can manage unmanned air vehicles into a realistic mission.
Experience using constraint programming to solve real-life problems has shown that finding an efficient solution to a problem often requires experimentation with different constraint solvers or even building a problem...
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The paper describes the advantages of the use of constraint logic programming to articulate transformation rules for multimedia presentation in combination with efficient constraint solving techniques. It demonstrates...
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In this paper we study the use of parallelism to speed up execution of Answer Set Programs (ASP). ASP is an emerging programming paradigm which combines features from constraint programming, logic programming, and non...
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Instruction scheduling is one of the most important steps for improving the performance of object code produced by a compiler. The local instruction scheduling problem is to find a minimum length instruction schedule ...
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Scheduling final exams for large numbers of courses and students in universities, such as the Lebanese American University (LAU). is an intractable problem. In order to solve this problem. the approach must be efficie...
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Scheduling final exams for large numbers of courses and students in universities, such as the Lebanese American University (LAU). is an intractable problem. In order to solve this problem. the approach must be efficient. flexible and adaptable. Conflicts occur when multiple exams are scheduled for the same student at the same period (simultaneously), and unfairness to a student refers to consecutive exams (two exams directly after each other) or more than two exams on the same day (referred to as multiples). A good exam schedule would aim for minimizing conflicts and the two unfairness factors based on user-assigned weights associated to these three factors and subject them to some constraints. Likewise. since a limited number of rooms are available in each exam period. an additional constraint concerned with room violations is added to achieve the goal of minimizing room violations. All constraints may be violated if necessary. since it is almost impossible in real world situations to find a solution without violating any constraint. ln this work. we first fonnulatc the problem as a modified weighted-graph coloring problem and adapt two natural optimization algorithms: Simulated Annealing and Genetic Algorithm; in addition to a clustering based algorithm (FESP). and a hybrid of natural optimization and clustering based algorithms (FESPSA) for solving the exam scheduling problem taking into account the specific objectives and constraints of LAU. Then. we compare these algorithms with each other as well as with the manual procedure done by the registrar's office. The comparison is done using realistic data taken from LAU for six semesters. Our experimental results show that simulated annealing gives better exam schedules than genetic algorithms. FESPSA. FESP and manual scheduling. All algorithms were run on different exam days ranging from five to ten. Simulated Annealing stayed to show the best results in all semesters in all days variations. Moreover. Simulated Annea
In this paper we study the use of parallelism to speed up execution of Answer Set Programs (ASP). ASP is an emerging programming paradigm which combines features from constraint programming, logic programming, and non...
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Multisets are the fundamental data structure of P systems. In this paper we relate P systems with the language and theory for multisets presented in [9.] This allows us, on the one hand, to define and implement P syst...
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This paper is an introduction to Newton, a constraint programming language over nonlinear real constraints. Newton originates from an effort to reconcile the declarative nature of constraint logic programming (CLP) la...
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This paper is an introduction to Newton, a constraint programming language over nonlinear real constraints. Newton originates from an effort to reconcile the declarative nature of constraint logic programming (CLP) languages over intervals with advanced interval techniques developed in numerical analysis, such as the interval Newton method. Its key conceptual idea is to introduce the notion of box-consistency, which approximates arc-consistency, a notion well-known in artificial intelligence. Box-consistency achieves an effective pruning at a reasonable computation cost and generalizes some traditional interval operators. Newton has been applied to numerous applications in science and engineering, including nonlinear equation-solving, unconstrained optimization, and constrained optimization. It is competitive with continuation methods on their equation-solving benchmarks and outperforms the interval-based methods we are aware of on optimization problems.
We use a local search method we term Large Neighbourhood Search (LNS) to solve vehicle routing problems. LNS is analogous to the shuffling technique of job-shop scheduling, and so meshes well with constraint programmi...
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ISBN:
(纸本)3540652248
We use a local search method we term Large Neighbourhood Search (LNS) to solve vehicle routing problems. LNS is analogous to the shuffling technique of job-shop scheduling, and so meshes well with constraint programming technology. LNS explores a large neighbourhood of the current solution by selecting a number of "related" customer visits to remove from the set of planned routes. and re-inserting these visits using a constraint-based tree search. Unlike similar methods, we use Limited Discrepancy Search during the tree search to re-insert visits. We analyse the performance of our method on benchmark problems. We demonstrate that results produced are competitive with Operations Research meta-heuristic methods. indicating that constraint-based technology is directly applicable to vehicle routing problems.
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