1,721,137 research outputs found
Blending under deconstruction: The roles of logic, ontology, and cognition in computational concept invention
Full text linkThe cognitive-linguistic theory of conceptual blending was introduced by Fauconnier and Turner in the late 90s to provide a descriptive model and foundational approach for the (almost uniquely) human ability to invent new concepts. Whilst blending is often described as ‘fluid’ and ‘effortless’ when ascribed to humans, it becomes a highly complex, multi-paradigm problem in Artificial Intelligence. This paper aims at presenting a coherent computational narrative, focusing on how one may derive a formal reconstruction of conceptual blending from a deconstruction of the human ability of concept invention into some of its core components. It thus focuses on presenting the key facets that a computational framework for concept invention should possess. A central theme in our narrative is the notion of refinement, understood as ways of specialising or generalising concepts, an idea that can be seen as providing conceptual uniformity to a number of theoretical constructs as well as implementation efforts underlying computational versions of conceptual blending. Particular elements underlying our reconstruction effort include ontologies and ontology-based reasoning, image schema theory, spatio-temporal reasoning, abstract specification, social choice theory, and axiom pinpointing. We overview and analyse adopted solutions and then focus on open perspectives that address two core problems in computational approaches to conceptual blending: searching for the shared semantic structure between concepts—the so-called generic space in conceptual blending—and concept evaluation, i.e., to determine the value of newly found blends
A Technology Transfer Portal to Promote Industry-Academia Collaboration in South-Tyrol
No full textTechnology transfer is a complex and multifaceted activity whose main goal is to promote academic knowledge transfer from academia to industry. In this context, one of the most challenging parts of technology transfer activities is to inform stakeholders from the industry about the availability of academic results. Traditionally, this occurs through academic publications, and companies with a research department already use this knowledge source. Nonetheless, Small and Medium Enterprises (SMEs) do not often have the time or the resources to study and interpret results from academia. This paper describes a technology transfer Web portal that promotes technology transfer offers in a industry-friendly format. The portal aims at fostering innovation and collaboration between academia and industry
Encoding preference queries to an uncertain database in possibilistic answer set programming
No full textThe representation of preference queries to an uncertain data-base requires a framework capable of dealing with preferences and uncertainty in a separate way. Possibilistic logic has shown to be a suitable setting to support different kinds of preference queries. In this paper, we propose a counterpart of the possibilistic logic-based preference query encoding within a possibilistic logic programming framework. Our approach is capable of dealing with the same interplay of preferences and uncertainty as in possibilistic logic. © 2012 Springer-Verlag Berlin Heidelberg
Using possibilistic logic for modeling qualitative decision: Answer Set Programming algorithms
No full textA qualitative approach to decision making under uncertainty has been proposed in the setting of possibility theory, which is based on the assumption that levels of certainty and levels of priority (for expressing preferences) are commensurate. In this setting, pessimistic and optimistic decision criteria have been formally justified. This approach has been transposed into possibilistic logic in which the available knowledge is described by formulas which are more or less certainly true and the goals are described in a separate prioritized base. This paper adapts the possibilistic logic handling of qualitative decision making under uncertainty in the Answer Set Programming (ASP) setting. We show how weighted beliefs and prioritized preferences belonging to two separate knowledge bases can be handled in ASP by modeling qualitative decision making in terms of abductive logic programming where (uncertain) knowledge about the world and prioritized preferences are encoded as possibilistic definite logic programs and possibilistic literals respectively. We provide ASP-based and possibilistic ASP-based algorithms for calculating optimal decisions and utility values according to the possibilistic decision criteria. We describe a prototype implementing the algorithms proposed on top of different ASP solvers and we discuss the complexity of the different implementations. © 2013 Elsevier Inc. All rights reserved
Answer set programming for computing decisions under uncertainty
No full textPossibility theory offers a qualitative framework for modeling decision under uncertainty. In this setting, pessimistic and optimistic decision criteria have been formally justified. The computation by means of possibilistic logic inference of optimal decisions according to such criteria has been proposed. This paper presents an Answer Set Programming (ASP)-based methodology for modeling decision problems and computing optimal decisions in the sense of the possibilistic criteria. This is achieved by applying both a classic and a possibilistic ASP-based methodology in order to handle both a knowledge base pervaded with uncertainty and a prioritized preference base. © 2011 Springer-Verlag Berlin Heidelberg
Nested preferences in answer set programming
No full textIn this paper, we define a class of nested logic programs, called Nested Logic Programs with Ordered Disjunction (LPODs +), which makes it possible to specify conditional (qualitative) preferences by means of nested preference statements. To this end, we augment the syntax of Logic Programs with Ordered Disjunction (LPODs) to capture more general expressions. We define the LPODs + semantics in a simple way and we extend most of the results of LPODs showing how our approach generalizes the LPODs framework in a proper way. We also show how the LPODs + semantics can be computed in terms of a translation procedure that maps a nested ordered disjunction program (OD +-program) into a disjunctive logic program
Nested logic programs with ordered disjunction
No full textIn this paper we define a class of nested logic programs, nested logic programs with ordered disjunction (LPODs+), which allows to specify qualitative preferences by means of nested preference expressions. For doing this we extend the syntax of logic programs with ordered disjunction (LPODs) to capture more general expressions. We define the LPODs+ semantics in a simple way and we extend most of the results of logic programs with ordered disjunction showing how our approach effectively is a proper generalisation of LPODs
A possibilistic argumentation decision making framework with default reasoning
No full textIn this paper, we introduce a possibilistic argumentation-based decision making framework which is able to capture uncertain information and exceptions/defaults. In particular, we define the concept of a possibilistic decision making framework which is based on a possibilistic default theory, a set of decisions and a set of prioritized goals. This set of goals captures user preferences related to the achievement of a particular state in a decision making problem. By considering the inference of the possibilistic well-founded semantics, the concept of argument with respect to a decision is defined. This argument captures the feasibility of reaching a goal by applying a decision in a given context. The inference in the argumentation decision making framework is based on basic argumentation semantics. Since some basic argumentation semantics can infer more than one possible scenario of a possibilistic decision making problem, we define some criteria for selecting potential solutions of the problem
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