Same-day delivery problems are a class of stochastic decision making problems concerned with delivering orders placed dynamically by stochastic customers on the same day given a fleet of vehicles. We consider a varian...
the proceedings contain 15 papers. the topics discussed include: extending DNA-sticker arithmetic to arbitrary size using staples;parallel computation using active self-assembly;DNA walker circuits: computational pote...
ISBN:
(纸本)9783319019277
the proceedings contain 15 papers. the topics discussed include: extending DNA-sticker arithmetic to arbitrary size using staples;parallel computation using active self-assembly;DNA walker circuits: computational potential, design, and verification;leaderless deterministic chemical reaction networks;DNA sticky end design and assignment for robust algorithmic self-assembly;DNA reservoir computing: a novel molecular computing approach;signal transmission across tile assemblies: 3D static tiles simulate active self-assembly by 2D signal-passing tiles;3-color bounded patterned self-assembly;exponential replication of patterns in the signal tile assembly model;modular verification of DNA strand displacement networks via serializability analysis;iterative self-assembly with dynamic strength transformation and temperature control;probabilistic reasoning with an enzyme-driven DNA device;staged self-assembly and polyomino context-free grammars;and functional analysis of large-scale DNA strand displacement circuits.
In this paper we developed an Inductive logicprogramming (ILP) based framework ExOpaque that is able to extract a set of Horn clauses from an arbitrary opaque machine learning model, to describe the behavior of the o...
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ISBN:
(纸本)076953015X
In this paper we developed an Inductive logicprogramming (ILP) based framework ExOpaque that is able to extract a set of Horn clauses from an arbitrary opaque machine learning model, to describe the behavior of the opaque model with high fidelity while maintaining the simplicity of the Horn clauses for human interpretations.
the handling of exceptions in multiclass problems is a tricky issue in inductive logicprogramming (ILP). In this paper we propose a new formalization of the ILP problem which accounts for default reasoning, and is en...
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the handling of exceptions in multiclass problems is a tricky issue in inductive logicprogramming (ILP). In this paper we propose a new formalization of the ILP problem which accounts for default reasoning, and is encoded with first-order possibilistic logic. We show that this formalization allows us to handle rules with exceptions, and to prevent an example to be classified in more than one class. the possibilistic logic view of ILP problem, can be easily handled at the algorithmic level as an optimization problem.
Many inductive logicprogramming systems have operators reorganizing the program so far inferred, such as the intra-construction operator of CIGOL. At the same time, there is a similar reorganizing operator, called th...
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Many inductive logicprogramming systems have operators reorganizing the program so far inferred, such as the intra-construction operator of CIGOL. At the same time, there is a similar reorganizing operator, called the "folding rule," developed in program transformation. We argue that there are advantages in using an extended folding rule as a reorganizing operator for inductive-inference systems. Such an extended folding rule allows an inductive-inference system not only to recognize already-learned concepts, but also to increase the efficiently of execution of inferred programs.
Recently, strong equivalence for Answer Set programming has been studied intensively, and was shown to be beneficial for modular programming and automated optimization. In this paper we define the novel notion of stro...
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Recently, strong equivalence for Answer Set programming has been studied intensively, and was shown to be beneficial for modular programming and automated optimization. In this paper we define the novel notion of strong equivalence for logic programs with preferences. Based on this definition we give, for several semantics for preference handling, necessary and sufficient conditions for programs to be strongly equivalent. these results provide a clear picture of the relationship of these semantics with respect to strong equivalence, which differs considerably from their relationship with respect to answer sets. Finally, based on these results, we present for the first time simplification methods for logic programs with preferences.
this papers deals with modelling of the zebra puzzle (also known as Einstein's riddle) in Prolog, the logical programming language. It shows how to construct a rudimentary representation of the puzzle in Prolog an...
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ISBN:
(纸本)9781467379397
this papers deals with modelling of the zebra puzzle (also known as Einstein's riddle) in Prolog, the logical programming language. It shows how to construct a rudimentary representation of the puzzle in Prolog and then proceeds by suggesting several modifications that significantly reduce the amount of computation time required to solve it. the results illustrate why - if a correct and reasonably efficient knowledge representation is to be constructed - it may be vital for the knowledge engineer to have a firm understanding of the way in which their knowledge representation tool reasons. Naturally, the paper also includes the answer to the eternal question: who owns the zebra?
We consider the problem of identifying equivalence of two knowledge bases which are capable of abductive reasoning. Here, a knowledge base is written in either first-order logic or nonmonotonic logicprogramming. In t...
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We consider the problem of identifying equivalence of two knowledge bases which are capable of abductive reasoning. Here, a knowledge base is written in either first-order logic or nonmonotonic logicprogramming. In this work, we will give two definitions of abductive equivalence. the first one, explainable equivalence, requires that two abductive programs have the same explainability for any observation. Another one, explanatory equivalence, guarantees that any observation has exactly the same explanations in each abductive framework. Explanatory equivalence is a stronger notion than explainable equivalence. In first-order abduction, explainable equivalence can be verified by the notion of extensional equivalence in default theories. In nonmonotonic logic programs, explanatory equivalence can be checked by means of the notion of relative strong equivalence. We also show the complexity results for abductive equivalence.
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