85,492 research outputs found
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An Ontological formalization of the planning task
In this paper we propose a generic task ontology, which formalizes the space of planning problems. Although planning is one of the oldest researched areas in Artificial Intelligence and attempts have been made in the past at developing task ontologies for planning, these formalizations suffer from serious limitations: they do not exhibit the required level of formalization and precision and they usually fail to include some of the key concepts required for specifying planning problems. In con-trast with earlier proposals, our task ontology formalizes the nature of the planning task independently of any planning paradigm, specific domains, or applications and provides a fine-grained, precise and comprehensive characterization of the space of planning problems. Finally, in addition to producing a formal specification we have also operationalized the ontology into a set of executable definitions, which provide a concrete reusable resource for knowledge acquisition and system development in planning applications
Preference fusion and Condorcet's Paradox under uncertainty
Facing an unknown situation, a person may not be able to firmly elicit
his/her preferences over different alternatives, so he/she tends to express
uncertain preferences. Given a community of different persons expressing their
preferences over certain alternatives under uncertainty, to get a collective
representative opinion of the whole community, a preference fusion process is
required. The aim of this work is to propose a preference fusion method that
copes with uncertainty and escape from the Condorcet paradox. To model
preferences under uncertainty, we propose to develop a model of preferences
based on belief function theory that accurately describes and captures the
uncertainty associated with individual or collective preferences. This work
improves and extends the previous results. This work improves and extends the
contribution presented in a previous work. The benefits of our contribution are
twofold. On the one hand, we propose a qualitative and expressive preference
modeling strategy based on belief-function theory which scales better with the
number of sources. On the other hand, we propose an incremental distance-based
algorithm (using Jousselme distance) for the construction of the collective
preference order to avoid the Condorcet Paradox.Comment: International Conference on Information Fusion, Jul 2017, Xi'an,
Chin
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
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Culture, institutions and democratization.
We construct a model of revolution and transition to democracy under individualistic and collectivist cultures. The main result is that, despite facing potentially more challenging collective action problems, countries with individualistic cultures are more likely to end up adopting democracy earlier than countries with collectivist cultures. Our empirical analysis suggests a strong and robust association between individualistic cultures and average polity scores and durations of democracy, even after controlling for other determinants of democracy emphasized in the literature. We provide evidence that countries with collectivist cultures also are more likely to experience autocratic breakdowns and transitions from autocracy to autocracy
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