answer set programming (ASP) does not allow for incrementally constructing answersets or locally validating constructions like proofs by only looking at a part of the given program. In this article, we elaborate upon...
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answer set programming (ASP) does not allow for incrementally constructing answersets or locally validating constructions like proofs by only looking at a part of the given program. In this article, we elaborate upon an alternative approach to ASP that allows for incremental constructions. Our approach draws its basic intuitions from the area of default logics. We investigate the feasibility of the concept of semi-monotonicity known from default logics as a basis of incrementality. On the one hand, every logic program has at least one answerset in our alternative setting, which moreover can be constructed incrementally based on generating rules. On the other hand, the approach may produce answersets lacking characteristic properties of standard answersets, such as being a model of the given program. We show how integrity constraints can be used to re-establish such properties, even up to correspondence with standard answersets. Furthermore, we develop an SLD-like proof procedure for our incremental approach to ASP, which allows for query-oriented computations. Also, we provide a characterization of our definition of answersets via a modification of Clarks completion. Based on this notion of program completion, we present an algorithm for computing the answersets of a logic program in our approach.
We study finding similar or diverse solutions of a given computational problem, in answer set programming, and introduce offline methods and online methods to compute them using answerset solvers. We analyze the comp...
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ISBN:
(纸本)9783642028458
We study finding similar or diverse solutions of a given computational problem, in answer set programming, and introduce offline methods and online methods to compute them using answerset solvers. We analyze the computational complexity of some problems that are related to finding similar or diverse solutions, and show the applicability and effectiveness of our methods in phylogeny reconstruction.
We argue that it is high time that types had a beneficial impact in the field of answer set programming and in particular Disjunctive Datalog as exemplified by the DLV system. Things become immediately more challengin...
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ISBN:
(纸本)9783642024436
We argue that it is high time that types had a beneficial impact in the field of answer set programming and in particular Disjunctive Datalog as exemplified by the DLV system. Things become immediately more challenging, as we wish to present a type system for DLV-Complex, an extension of DLV with uninterpreted function symbols, external implemented predicates and types. Our type system owes to the seminal polymorphic type system for Prolog introduced by Mycroft and O'Keefe, in the formulation by Lakshman and Reddy. The most innovative part of the paper is developing a declarative grounding procedure which is at the same time appropriate for the operational semantics of ASP and able to handle the new features provided by DLV-Complex. We discuss the soundness of the procedure and evaluate informally its success in reducing, as expected, the set of ground terms. This yields an automatic reduction in size and numbers of (non isomorphic) models. Similar results Could have only been achieved in the current untyped version by careful use of generator predicates in lieu of types.
In the current approaches to fuzzy answer set programming (FASP) one can state preferences amongst rules to denote their relative importance. However, in many situations we need more complex preferences such as those ...
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ISBN:
(纸本)9789899507968
In the current approaches to fuzzy answer set programming (FASP) one can state preferences amongst rules to denote their relative importance. However, in many situations we need more complex preferences such as those in the answerset optimization framework proposed by Brewka for crisp answer set programming. Unfortunately, these complex preferences do not readily fit into the current FASP approaches. In this paper we propose a language to state such preferences and show that programs with these preferences can be translated into equivalent general fuzzy answer set programming (gFASP) programs. This not only provides an implementation method, but also shows that this extension can be added as syntactic sugar on top of general fuzzy answer set programming solvers.
In this article, we consider the issue of how first-order answerset programs can be extended for handling preference reasoning. To this end, we propose a progression-based preference semantics for first-order answer ...
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In this article, we consider the issue of how first-order answerset programs can be extended for handling preference reasoning. To this end, we propose a progression-based preference semantics for first-order answerset programs while explicit preference relations are presented. We study essential properties of the proposed preferred answerset semantics. To understand the expressiveness of preferred first-order answer set programming, we further specify a second-order logic representation which precisely characterizes the progression-based preference semantics.
Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach performs well. In this paper, we show that Logic programming, particularly answer ...
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ISBN:
(纸本)9781479929023
Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach performs well. In this paper, we show that Logic programming, particularly answer set programming (ASP), can be used to elegantly and efficiently implement the key components of syntax based aspect extraction. Specifically, the well known double propagation (DP) method is implemented using 8 ASP rules that naturally model all key ideas in the DP method. Our experiment on a widely used data set also shows that the ASP implementation is much faster than a Java-based implementation. Syntactical approach has its limitation too. To further improve the performance of syntactical approach, we identify a set of general words from WordNet that have little chance to be an aspect and prune them when extracting aspects. The concept of general words and their pruning are concisely captured by 10 new ASP rules, and a natural extension of the 8 rules for the original DP method. Experimental results show a major improvement in precision with almost no drop in recall compared with those reported in the existing work on a typical benchmark data set. Logic programming provides a convenient and effective tool to encode and thus test knowledge needed to improve the aspect extraction methods so that the researchers can focus on the identification and discovery of new knowledge to improve aspect extraction.
In this paper, a possibilistic disjunctive logic programming approach for modeling uncertain, incomplete, and inconsistent information is defined. This approach introduces the use of possibilistic disjunctive clauses,...
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In this paper, a possibilistic disjunctive logic programming approach for modeling uncertain, incomplete, and inconsistent information is defined. This approach introduces the use of possibilistic disjunctive clauses, which are able to capture incomplete information and states of a knowledge base at the same time. By considering a possibilistic logic program as a possibilistic logic theory, a construction of a possibilistic logic programming semantic based on answersets and the proof theory of possibilistic logic is defined. It shows that this possibilistic semantics for disjunctive logic programs can be characterized by a fixed-point operator. It is also shown that the suggested possibilistic semantics can be computed by a resolution algorithm and the consideration of optimal refutations from a possibilistic logic theory. In order to manage inconsistent possibilistic logic programs, a preference criterion between inconsistent possibilistic models is defined. In addition, the approach of cuts for restoring consistency of an inconsistent possibilistic knowledge base is adopted. The approach is illustrated in a medical scenario.
Fuzzy answer set programming (FASP) is a recent formalism for knowledge representation that enriches the declarativity of answer set programming by allowing propositions to be graded. To now, no implementations of FAS...
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Fuzzy answer set programming (FASP) is a recent formalism for knowledge representation that enriches the declarativity of answer set programming by allowing propositions to be graded. To now, no implementations of FASP solvers are available and all current proposals are based on compilations of logic programs into different paradigms, like mixed integer programs or bilevel programs. These approaches introduce many auxiliary variables which might affect the performance of a solver negatively. To limit this downside, operators for approximating fuzzy answersets can be introduced: Given a FASP program, these operators compute lower and upper bounds for all atoms in the program such that all answersets are between these bounds. This paper analyzes several operators of this kind which are based on linear programming, fuzzy unfounded sets and source pointers. Furthermore, the paper reports on a prototypical implementation, also describing strategies for avoiding computations of these operators when they are guaranteed to not improve current bounds. The operators and their implementation can be used to obtain more constrained mixed integer or bilevel programs, or even for providing a basis for implementing a native FASP solver. Interestingly, the semantics of relevant classes of programs with unique answersets, like positive programs and programs with stratified negation, can be already computed by the prototype without the need for an external tool.
Dealing with domains involving substantial quantitative information in answer set programming (ASP) often results in cumbersome and inefficient encodings. Hybrid "CASP" languages combining ASP and Constraint...
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Dealing with domains involving substantial quantitative information in answer set programming (ASP) often results in cumbersome and inefficient encodings. Hybrid "CASP" languages combining ASP and Constraint programming aim to overcome this limitation, but also impose inconvenient constraints - first and foremost that quantitative information must be encoded by means of total functions. This goes against central knowledge representation principles that contribute to the power of ASP, and makes the formalization of certain domains difficult. ASP{f} is being developed with the ultimate goal of providing scientists and practitioners with an alternative to CASP languages that allows for the efficient representation of qualitative and quantitative information in ASP without restricting one's ability to deal with incompleteness or uncertainty. In this paper we present the latest outcome of such research: versions of the language and of the supporting system that allow for practical, industrial-size use and scalability. The applicability of ASP{f} is demonstrated by a case study on an actual industrial application.
Several extensions of the stable model semantics are available to describe "intensional" functions-functions that can be described in terms of other functions and predicates by logic programs. Such functions...
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Several extensions of the stable model semantics are available to describe "intensional" functions-functions that can be described in terms of other functions and predicates by logic programs. Such functions are useful for expressing inertia and default behaviors of systems, and can be exploited for alleviating the grounding bottleneck involving functional fluents. However, the extensions were defined in different ways under different intuitions. In this paper we provide several reformulations of the extensions, and note that they are in fact closely related to each other and coincide on large syntactic classes of logic programs.
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