Temporal logics are well suited for reasoning about actions, as they allow for the specification of domain descriptions including temporal constraints as well as for the verification of temporal properties. The articl...
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Temporal logics are well suited for reasoning about actions, as they allow for the specification of domain descriptions including temporal constraints as well as for the verification of temporal properties. The article deals with verification of action theories defined in a temporal extension of answer set programming which combines ASP with a dynamic linear time temporal logic (DLTL). The article proposes an approach to bounded model checking that exploits the Buchi automaton construction while searching for a counterexample, with the aim of achieving completeness. The article provides an encoding in ASP of the temporal action domains and of Bounded Model Checking of DLTL formulas. The article also deals with reasoning about epistemic knowledge and incomplete states.
We present an integration of answer set programming and constraint processing as an interesting approach to constraint logic programming. Although our research is in a very early stage, we motivate constraint answer s...
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ISBN:
(纸本)9783939897170
We present an integration of answer set programming and constraint processing as an interesting approach to constraint logic programming. Although our research is in a very early stage, we motivate constraint answer set programming and report on related work, our research objectives, preliminary results we achieved, and future work.
In this paper we explore the use of answer set programming (ASP) to formalize, and reason about, psychological knowledge. In the field of psychology, a considerable amount of knowledge is still expressed using only na...
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In this paper we explore the use of answer set programming (ASP) to formalize, and reason about, psychological knowledge. In the field of psychology, a considerable amount of knowledge is still expressed using only natural language. This lack of a formalization complicates accurate studies, comparisons, and verification of theories. We believe that ASP, a knowledge representation formalism allowing for concise and simple representation of defaults, uncertainty, and evolving domains, can be used successfully for the formalization of psychological knowledge. To demonstrate the viability of ASP for this task, in this paper we develop an ASP-based formalization of the mechanics of Short-Term Memory. We also show that our approach can have rather immediate practical uses by demonstrating an application of our formalization to the task of predicting a user's interaction with a graphical interface.
In recent work, we provided a formulation of ASP programs in terms of linear logic theories. answersets were characterized in terms of maximal tensor conjunctions provable from such theories. In this paper, we propos...
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In recent work, we provided a formulation of ASP programs in terms of linear logic theories. answersets were characterized in terms of maximal tensor conjunctions provable from such theories. In this paper, we propose a full comparison between answerset Semantics and its variation obtained by interpreting literals (including negative literals) as resources, which leads to a different interpretation of negation. We argue that this novel view can be of both theoretical and practical interest, and we propose a modified answerset Semantics that we call Resource-based answerset Semantics. An advantage is that of avoiding inconsistencies, as every program has a (possibly empty) resource-based answerset. This implies however the introduction of a different way of representing constraints. We provide a characterization of the new semantics as a variation of the answerset semantics, and also in terms of Autoepistemic Logic. The latter characterization leads to a way of computing resource-based answerset via answerset solvers.
The recent application of Machine Learning techniques to the answer set programming (ASP) field proved to be effective. In particular, the multi-engine ASP solver ME-ASP is efficient: it is able to solve more instance...
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The recent application of Machine Learning techniques to the answer set programming (ASP) field proved to be effective. In particular, the multi-engine ASP solver ME-ASP is efficient: it is able to solve more instances than any other ASP system that participated to the 3rd ASP Competition on the 'System Track' benchmarks. In the ME-ASP approach, classification methods inductively learn offline algorithm selection policies starting from both a set of features of instances in a training set, and the solvers performance on such instances. In this article we present an improvement to the multi-engine framework of ME-ASP, in which we add the capability of updating the learned policies when the original approach fails to give good predictions. An experimental analysis, conducted on training and test sets of ground instances obtained from the ones submitted to the 'System Track' of the 3rd ASP Competition, shows that the policy adaptation improves the performance of ME-ASP when applied to test sets containing domains of instances that were not considered for training.
answer set programming (ASP) is a declarative programming paradigm oriented towards difficult combinatorial search problems. Syntactically, ASP programs look like Prolog programs, but solutions are represented in ASP ...
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answer set programming (ASP) is a declarative programming paradigm oriented towards difficult combinatorial search problems. Syntactically, ASP programs look like Prolog programs, but solutions are represented in ASP by sets of atoms, and not by substitutions, as in Prolog. answerset systems, such as SMODELS, SMODELSCC, and DLV, compute answersets of a given program in the sense of the answerset (stable model) semantics. This is different from the functionality of Prolog systems, which determine when a given query is true relative to a given logic program. ASP has been applied to many areas of science and technology, from the design of a decision support system for the Space Shuttle to graph-theoretic problems arising in zoology and linguistics
In this paper, the class of possibilistic nested logic programs is introduced. These possibilistic logic programs allow us to use nested expressions in the bodies and heads of their rules. By considering a possibilist...
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In this paper, the class of possibilistic nested logic programs is introduced. These possibilistic logic programs allow us to use nested expressions in the bodies and heads of their rules. By considering a possibilistic nested logic program as a possibilistic theory, a construction of a possibilistic logic programing semantics based on answersets for nested logic programs and the proof theory of possibilistic logic is defined. In order to define a general method for computing the possibilistic answersets of a possibilistic nested program, the idea of equivalence between possibilistic nested programs is explored. By considering properties of equivalence between possibilistic programs, a process of transforming a possibilistic nested logic program into a possibilistic disjunctive logic program is defined. Given that our approach is an extension of answer set programming, we also explore the concept of strong equivalence between possibilistic nested logic programs. To this end, we introduce the concept of poss SE-models. Therefore, we show that two possibilistic nested logic programs are strong equivalents whenever they have the same poss SE-models. The expressiveness of the possibilistic nested logic programs is illustrated by a scenario from the medical domain. In particular, we exemplify how possibilistic nested logic programs are expressive enough for capturing medical guidelines which are pervaded by vagueness and qualitative information. (C) 2015 Elsevier Inc. All rights reserved.
Traditional machine learning algorithms require a dataset composed of homogeneous objects, randomly sampled from a single relation. However, real world tasks such as link prediction and entity resolution, require the ...
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ISBN:
(纸本)9781509000166
Traditional machine learning algorithms require a dataset composed of homogeneous objects, randomly sampled from a single relation. However, real world tasks such as link prediction and entity resolution, require the representation of multiple relations, heterogeneous and structured data. Inductive Logic programming (ILP) is a subarea of machine learning that induces structured hypotheses from multi-relational examples and background knowledge (BK) represented as logical clauses. With a few exceptions, most of the systems developed in ILP induce Horn-clauses and uses Prolog as their baseline inference engine. However, the recent development of efficient answer set programming solvers points out that these can be a viable option to be the reasoning component of ILP systems, especially to address nonmonotonic reasoning. In this paper, we present dASBoT, a system that is capable of inducing extended normal rules mined from answersets yielded from the examples and the BK. We show empirical evidence that dASBoT can support the task of relational identification by learning rules in three link prediction and two entity resolution tasks.
This is an expository article about the solution to the frame problem proposed in 1980 by Raymond Reiter. For years, his "frame default" remained untested and suspect. But developments in some seemingly unre...
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This is an expository article about the solution to the frame problem proposed in 1980 by Raymond Reiter. For years, his "frame default" remained untested and suspect. But developments in some seemingly unrelated areas of computer science-logic programming and satisfiability solvers-eventually exonerated the frame default and turned it into a basis for important applications.
We present a constraint based declarative approach for analyzing qualitatively genetic regulatory networks (GRNs) with the discrete formalism of R. Thomas. For this purpose, we use the logic programming technology ASP...
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ISBN:
(数字)9783319164830
ISBN:
(纸本)9783319164830;9783319164823
We present a constraint based declarative approach for analyzing qualitatively genetic regulatory networks (GRNs) with the discrete formalism of R. Thomas. For this purpose, we use the logic programming technology ASP (answer set programming) whose related logic is non monotonic. Our aim is twofold. First, we give a formal modeling of both Thomas' GRNs and biological data like experimental behaviors and gene interactions and we evaluate the declarative approach on three real biological applications. Secondly, for taking into account both gene interaction properties which are only generally true and automatic inconsistency repairing, we introduce an optimized modeling which leads us to exhibit new logical expressions for the conjunction of defaults and to show that they can be applied safely to Thomas' GRNs.
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