In this paper we present a detailed scheme for annotating expressions of opinions, beliefs, emotions, sentiment and speculation (private states) in the news and other discourse. We explore inter-annotator agreement fo...
Approximate linear programming (ALP) has emerged recently as one of the most promising methods for solving complex factored MDPs with finite state spaces. In this work we show that ALP solutions are not limited only t...
Approximate linear programming (ALP) has emerged recently as one of the most promising methods for solving complex factored MDPs with finite state spaces. In this work we show that ALP solutions are not limited only to MDPs with finite state spaces, but that they can also be applied successfully to factored continuous-state MDPs (CMDPs). We show how one can build an ALP-based approximation for such a model and contrast it to existing solution methods. We argue that this approach offers a robust alternative for solving high dimensional continuous-state space problems. The point is supported by experiments on three CMDP problems with 24-25 continuous state factors.
We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective senten...
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Machine Learning has traditionally been a topic of research and instruction in computerscience and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expand...
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Machine Learning has traditionally been a topic of research and instruction in computerscience and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as electrical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. The project involves the development of a model for the integration of Machine Learning into the undergraduate curriculum of those engineering and science disciplines mentioned above. The goal is increased exposure to Machine Learning technology for a wider range of students in science and engineering than is currently available. Our approach of integrating Machine Learning research into the curriculum involves two components. The first component is the incorporation of Machine Learning modules into the first two years of the curriculum with the goal of sparking student interest in the field. The second is the development of new upper level Machine Learning courses for advanced undergraduate students. The paper will describe the first phase of the project, that of the integration of Machine Learning concepts into introductory engineering and scienceprogramming courses through appropriately designed programming projects.
In this short note, we discuss several aspects of "dimensions" and the related construct of "factors". We concentrate on those aspects that are relevant to articles in this special issue, especiall...
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In this short note, we discuss several aspects of "dimensions" and the related construct of "factors". We concentrate on those aspects that are relevant to articles in this special issue, especially those dealing with the analysis of the wild animal cases discussed in Berman and Hafner's 1993 ICAIL article. We review the basic ideas about dimensions, as used in HYPO, and point out differences with factors, as used in subsequent systems like CATO. Our goal is to correct certain misconceptions that have arisen over the years.
The goal of this work is recognizing opinionated and evaluative (subjective) language in text. The ability to recognize such language would be beneficial for many NLP applications such as question answering, informati...
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An important aspect of business to business e-commerce is the agile virtual enterprise (VE). VEs are established when existing enterprises dynamically form temporary alliances, joining their business in order to share...
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ISBN:
(纸本)0769514804
An important aspect of business to business e-commerce is the agile virtual enterprise (VE). VEs are established when existing enterprises dynamically form temporary alliances, joining their business in order to share their costs, skills and resources in supporting certain activities. Currently, existing enterprises use workflows to automate their operation and integrate their information systems and human resources. Thus, the establishment of a VE has been viewed as a problem of dynamically expanding and integrating workflows. We present an approach to combining workflows from different enterprises, using techniques developed in the artificial intelligence literature on planning. Our method takes two workflow views, one representing a service request and the other a service provision (advertisement), with a mix of vital and nonvital steps and a rich set of constraints, and returns a list of possible legal combinations, if any exist. It then uses plan-merging techniques to find potential conflicts between the two workflows, and to suggest additional constraints that can resolve the conflicts. The returned solutions represent terms for the establishment of a new VE, and can be evaluated by each side to determine which is most desirable.
We develop an algorithm for merging plans that are represented in a richly expressive language. Specifically, we are concerned with plans that have (i) quantitative temporal constraints, (ii) actions that are not inst...
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