This paper describes the use of mobile agent technologies in building a framework for supporting distributed logic programming. The distinctive idea is to replace the distributed unification mechanism in most distribu...
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ISBN:
(纸本)0769517609
This paper describes the use of mobile agent technologies in building a framework for supporting distributed logic programming. The distinctive idea is to replace the distributed unification mechanism in most distributed logic programming languages with the mobility and execution locality of mobile agents. Mobile agents, migrating among logic server hosts, accomplish distributed deductions by asserting program clauses and queries into the server triggering inferences, and retrieving results. The mobile agent framework is designed to integrate a mobile agent system and necessary logic servers. One of the distinguishing features of this framework is that each logic server retains its own autonomy. Another notable characteristic is the clauses exchange ability among distributed logic servers that may make many operations required by distributed knowledge processing easier. In a prototypical implementation, a Prolog system on a host will serve as a logic server and, in the mean time, as a standalone logic programming system in the host.
Neural-Symbolic integration has become a very active research area in the last decade. In this paper, we present a new massively parallel model for modal logic. We do so by extending the language of Modal Prolog to al...
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Neural-Symbolic integration has become a very active research area in the last decade. In this paper, we present a new massively parallel model for modal logic. We do so by extending the language of Modal Prolog to allow modal operators in the head of the clauses. We then use an ensemble of C-IL/sup 2/p neural networks to encode the extended modal theory (and its relations), and show that the ensemble computes a fixpoint semantics of the extended theory. An immediate result of our approach is the ability to perform learning from examples efficiently using each network of the ensemble. Therefore, one can adapt the extended C-IL/sup 2/P system by training possible world representations.
Computationally very expensive, dynamic programming matching of data sequences has been directly implemented as a fully-parallel-architecture VLSI chip. The chip is organized as a 2D array of delay-encoding logic unit...
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Computationally very expensive, dynamic programming matching of data sequences has been directly implemented as a fully-parallel-architecture VLSI chip. The chip is organized as a 2D array of delay-encoding logic units, which works as an automatic best-match-sequence search network. The circuit operates as digital logic in the signal domain, while analog processing is carried out in the time domain. As a result, high-speed low-power operation has been established with a small chip area. A prototype chip was designed and fabricated in a 0.18-/spl mu/m CMOS technology, and a typical matching time of 80 ns with a power dissipation of 2 mW under a 1.3 V power supply has been demonstrated.
We present a new method of logic-based genetic programming (LBGP) using Prolog programming framework. Using the intrinsic mechanism of backtracking in Prolog, we are able to (i) design flexible genetic operations effe...
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We present a new method of logic-based genetic programming (LBGP) using Prolog programming framework. Using the intrinsic mechanism of backtracking in Prolog, we are able to (i) design flexible genetic operations effective to the smaller sizes of populations, and (ii) to maintain the population diversity of individual programs with both active structures (or exons) and inactive structures (or introns) generated by the genetic operations. We apply the method to obtaining multiple solutions for multi-modal problems. We validate the effectiveness of LBGP to get multiple solutions on tree constructional and tile world problems.
Automatic Test Pattern Generation (ATPG) based on Boolean Satisfiability (SAT) has been proposed as an alternative to classical search algorithms. SAT-based ATPG turned out to be more robust and more effective by form...
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ISBN:
(纸本)076952365X
Automatic Test Pattern Generation (ATPG) based on Boolean Satisfiability (SAT) has been proposed as an alternative to classical search algorithms. SAT-based ATPG turned out to be more robust and more effective by formulating the problem as a set of equations. In this paper we present an efficient ATPG algorithm that makes use of powerful SAT-solving techniques. Problem specific heuristics are applied to guide the search. In contrast to previous SAT-based algorithms, the new approach can also cope with tri-states. The algorithm has been implemented as the tool PASSAT. Experimental results on large industrial circuits are given to demonstrate the quality and efficiency of the algorithm.
Drawing on a wide range of computing technologies and methodologies, the authors present a new auditory alert system for high tides in Venice designed to replace the existing network of electromechanical sirens.
Drawing on a wide range of computing technologies and methodologies, the authors present a new auditory alert system for high tides in Venice designed to replace the existing network of electromechanical sirens.
logic programming requires that the programmer convert a problem into a set of constraints based on predicates. Choosing the predicates and introducing appropriate constraints can be intricate and error prone. If the ...
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logic programming requires that the programmer convert a problem into a set of constraints based on predicates. Choosing the predicates and introducing appropriate constraints can be intricate and error prone. If the problem domain is structured enough, we can let the programmer express the problem in terms of more abstract, higher-level constraints. A compiler can then convert the higher-level program into a logic-programming formalism. The compiler writer can experiment with alternative low-level representations of the higher-level constraints in order to achieve a high-quality translation. The programmer can then take advantage of both a reduction in complexity and an improvement in runtime speed for all problems within the domain. We apply this analysis to the domain of tabular constraint-satisfaction problems. Examples of such problems include logic puzzles solvable on a hatch grid and combinatorial problems such as graph coloring and independent sets. The proper abstractions for these problems are rows, columns, entries, and their interactions. We present a higher-level language, Constraint Lingo, dedicated to problems in this domain. We also describe how we translate programs from Constraint Lingo into lower-level logic formalisms such as the logic of propositional schemata. These translations require that we choose among competing lower-level representations in order to produce efficient results. The overall effectiveness of our approach depends on the appropriateness of Constraint Lingo, our ability to translate Constraint Lingo programs into high-quality representations in logic formalisms, and the efficiency with which logic engines can compute answer sets. We comment on our computational experience with these tools in solving both graph problems and logic puzzles. Copyright (C) 2004 John Wiley Sons, Ltd.
The paper addresses the problem of computing siphons and traps in a standard Petri net. In particular, starting from a clear formulation in terms of predicate logic, it is shown how binary programming techniques can b...
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The paper addresses the problem of computing siphons and traps in a standard Petri net. In particular, starting from a clear formulation in terms of predicate logic, it is shown how binary programming techniques can be adopted to formulate and solve the problem of finding minimal and basis siphons. An experimental campaign on a large set of random test instances proves the effectiveness of the method when compared to a constructive one.
Recently the relationship between abstract interpretation and program specialization has received a lot of scrutiny, and the need has been identified to extend program specialization techniques so as to make use of mo...
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Recently the relationship between abstract interpretation and program specialization has received a lot of scrutiny, and the need has been identified to extend program specialization techniques so as to make use of more refined abstract domains and operators. This article clarifies this relationship in the context of logic programming, by expressing program specialization in terms of abstract interpretation. Based on this, a novel specialization framework, along with generic correctness results for computed answers and finite failure under SLD-resolution, is developed. This framework can be used to extend existing logic program specialization methods, such as partial deduction and conjunctive partial deduction, to make use of more refined abstract domains. It is also shown how this opens up the way for new optimizations. Finally, as shown in the paper, the framework also enables one to prove correctness of new or existing specialization techniques in a simpler manner. The framework has already been applied in the literature to develop and prove correct specialization algorithms using regular types, which in turn have been applied to the verification of infinite state process algebras.
We propose a new translation from normal logic programs with constraints under the answer set semantics to propositional logic. Given a normal logic program, we show that by adding, for each loop in the program, a cor...
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We propose a new translation from normal logic programs with constraints under the answer set semantics to propositional logic. Given a normal logic program, we show that by adding, for each loop in the program, a corresponding loop formula to the program's completion, we obtain a one-to-one correspondence between the answer sets of the program and the models of the resulting propositional theory. In the worst case, there may be an exponential number of loops in a logic program. To address this problem, we propose an approach that adds loop formulas a few at a time, selectively. Based on these results, we implement a system called ASSAT(X), depending on the SAT solver X used, for computing one answer set of a normal logic program with constraints. We test the system on a variety of benchmarks including the graph coloring, the blocks world planning, and Hamiltonian Circuit domains. Our experimental results show that in these domains, for the task of generating one answer set of a normal logic program, our system has a clear edge over the state-of-art answer set programming systems Smodels and DLU. (C) 2004 Elsevier B.V. All rights reserved.
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