In recent decades, qualitative approaches to probabilistic uncertainty have been receiving wider and wider attention. We propose a new characterization of some of the most adopted partial preference orders by providin...
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In recent decades, qualitative approaches to probabilistic uncertainty have been receiving wider and wider attention. We propose a new characterization of some of the most adopted partial preference orders by providing an uniform axiomatic treatment of a variety of qualitative uncertainty notions. We prove a representation result that connects qualitative notions of partial uncertainty to their numerical counterparts. We also describe an executable specification, in the declarative framework of answer set programming, that constitutes the core engine for the qualitative management of uncertainty. Some basic reasoning tasks are also identified.
Unit testing frameworks are nowadays considered a best practice, included in almost all modern software development processes, to achieve rapid development of correct specifications. Knowledge representation and reaso...
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Unit testing frameworks are nowadays considered a best practice, included in almost all modern software development processes, to achieve rapid development of correct specifications. Knowledge representation and reasoning paradigms such as answer set programming (ASP), that have been used in industry-level applications, are not an exception. Indeed, the first unit testing specification language for ASP was proposed in 2011 as a feature of the ASPIDE development environment. Later, a more portable unit testing language was included in the LANA annotation language. In this paper we revisit both languages and tools for unit testing in ASP. We propose a new unit test specification language that allows one to inline tests within ASP programs, and we identify the computational complexity of the tasks associated with checking the various program-correctness assertions. Test-case specifications are transparent to the traditional evaluation, but can be interpreted by a specific testing tool. Thus, we present a novel environment supporting test-driven development of ASP programs.
Conceptual blending is a mental process that serves a variety of cognitive purposes, including human creativity. In this line of thinking, human creativity is modeled as a process that takes different mental spaces as...
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Conceptual blending is a mental process that serves a variety of cognitive purposes, including human creativity. In this line of thinking, human creativity is modeled as a process that takes different mental spaces as input and combines them into a new mental space, called a blend. According to this form of combinational creativity, a blend is constructed by taking the commonalities among the input mental spaces into account, to form a so-called generic space, and by projecting the non-common structure of the input spaces in a selective way to the novel blended space. Since input spaces for interesting blends are often initially incompatible, a generalisation step is needed before they can be blended. In this paper, we apply this idea to blend input spaces specified in the description logic epsilon L++ and propose an upward refinement operator for generalising epsilon L-+(+) concepts. We show how the generalisation operator is translated to answer set programming (ASP) in order to implement a search process that finds possible generalisations of input concepts. The generalisations obtained by the ASP process are used in a conceptual blending algorithm that generates and evaluates possible combinations of blends. We exemplify our approach in the domain of computer icons.
The need for integration of ontologies with nonmonotonic rules has been gaining importance in a number of areas, such as the Semantic Web. A number of researchers addressed this problem by proposing a unified semantic...
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The need for integration of ontologies with nonmonotonic rules has been gaining importance in a number of areas, such as the Semantic Web. A number of researchers addressed this problem by proposing a unified semantics for hybrid knowledge bases composed of both an ontology (expressed in a fragment of first-order logic) and nonmonotonic rules. These semantics have matured over the years, but only provide solutions for the static case when knowledge does not need to evolve. In this paper we take a first step towards addressing the dynamics of hybrid knowledge bases. We focus on knowledge updates and, considering the state of the art of belief update, ontology update and rule update, we show that current solutions are only partial and difficult to combine. Then we extend the existing work on ABox updates with rules, provide a semantics for such evolving hybrid knowledge bases and study its basic properties. To the best of our knowledge, this is the first time that an update operator is proposed for hybrid knowledge bases.
In this work we propose a multi-valued extension of logic programs under the stable models semantics where each true atom in a model is associated with a set of justifications. These justifications are expressed in te...
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In this work we propose a multi-valued extension of logic programs under the stable models semantics where each true atom in a model is associated with a set of justifications. These justifications are expressed in terms of causal graphs formed by rule labels and edges that represent their application ordering. For positive programs, we show that the causal justifications obtained for a given atom have a direct correspondence to (relevant) syntactic proofs of that atom using the program rules involved in the graphs. The most interesting contribution is that this causal information is obtained in a purely semantic way, by algebraic operations (product, sum and application) on a lattice of causal values whose ordering relation expresses when a justification is stronger than another. Finally, for programs with negation, we define the concept of causal stable model by introducing an analogous transformation to Gelfond and Lifschitz's program reduct. As a result, default negation behaves as "absence of proof" and no justification is derived from negative literals, something that turns out convenient for elaboration tolerance, as we explain with a running example.
The theorem on loop formulas due to Fangzhen Lin and Yuting Zhao shows how to turn a logic program into a propositional formula that describes the program's stable models. In this paper we simplify and generalize ...
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The theorem on loop formulas due to Fangzhen Lin and Yuting Zhao shows how to turn a logic program into a propositional formula that describes the program's stable models. In this paper we simplify and generalize the statement of this theorem. The simplification is achieved by modifying the definition of a loop in such a way that a program is turned into the corresponding propositional formula by adding loop formulas directly to the conjunction of its rules, without the intermediate step of forming the program's completion. The generalization makes the idea of a loop formula applicable to stable models in the sense of a very general definition that covers disjunctive programs, programs with nested expressions, and more.
Towards combining rules and ontologies for the Semantic Web, nonmonotonic Description Logic Programs (dl-programs) have been proposed as a powerful formalism to couple nonmonotonic logic programming and Description Lo...
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Towards combining rules and ontologies for the Semantic Web, nonmonotonic Description Logic Programs (dl-programs) have been proposed as a powerful formalism to couple nonmonotonic logic programming and Description Logic reasoning on a clear semantic basis. In this paper, we present cq-programs, which enhance dl-programs with conjunctive queries (CQ) and union of conjunctive queries (UCQ) over Description Logics knowledge bases, as well as with disjunctive rules. The novel formalism has two advantages. First, it offers increased expressivity because it allows for (U)CQs in the bodies of the rules. The (U)CQs allow one to access unnamed individuals in the rules and they increase the expressivity of the formalism, as evident from the increase in complexity from NEXP to 2-EXP. And second, when implemented as a combination between a logic programming system and a DL-reasoner, this integration of rules and ontologies gives rise to strategies for optimizing calls to the DL-reasoner, by exploiting specific support for (U)CQs. To this end, we present equivalence preserving transformations which can be used for program rewriting, and we present respective generic rewriting algorithms. Experimental results for a cq-program prototype show that this can lead to significant performance improvements, and suggest that cq-programs and program rewriting provide a useful basis for dl- and cq-program optimization.
We present the General Game Playing system Centurio. Centurio is a Java-based player featuring different strategies based on Monte Carlo Tree Search extended by techniques borrowed from Upper Confidence bounds applied...
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We present the General Game Playing system Centurio. Centurio is a Java-based player featuring different strategies based on Monte Carlo Tree Search extended by techniques borrowed from Upper Confidence bounds applied to Trees as well as answer set programming (for single-player games). Centurio's Monte Carlo Tree Search is accomplished in a massively parallel way by means of multi-threading as well as cluster-computing. Another major feature of Centurio is its compilation of game descriptions, states, and state manipulations into Java, yielding an edge over existing Prolog-based approaches. Centurio is open source software freely available via the web.
General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This ...
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General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce a task-based combination recommendation strategy, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.
For some problems with too many solutions, one way to obtain the more desirable solutions is to assign each solution a weight that characterizes its importance quantitatively, and then compute the solutions whose weig...
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For some problems with too many solutions, one way to obtain the more desirable solutions is to assign each solution a weight that characterizes its importance quantitatively, and then compute the solutions whose weights are over (resp. below) a given threshold. This paper studies computing weighted solutions for a given computational problem, in the context of answer set programming (ASP). In particular, we investigate two sorts of methods for computing weighted solutions: one method suggests modifying the ASP representation of the problem to compute weighted solutions using an existing ASP solver and the other suggests modifying the search algorithm of the answerset solver to compute weighted solutions incrementally. The applicability of these methods are shown on two sorts of problems: reconstructing weighted phylogenies (for Indo-European languages and for Quercus species) and finding weighted plans (for Blocks World planning problems). In the experiments with the representation-based method, the answerset solver clasp is used and weight functions are represented in ASP. For the search-based method, the algorithm of clasp is modified (the modified solver is called CLASP-W) and weight functions are implemented in C++. For phylogenies, two weight functions are introduced by incorporating domain-specific information about groupings of species;one of them cannot be represented in ASP due to some mathematical functions not supported by the ASP solvers. For plans, we define a weight function that characterizes the total cost of executing actions in a plan. In these experiments the following are observed. With weight measures that can be represented in ASP, the search-based method outperforms the representation-based method in terms of computational efficiency (both time and space). With weight functions that cannot be represented in ASP, the search-based method provides a tool for computing weighted solutions in ASP. The search-based method can be applied to differe
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