Set-based representations of conjunctive and disjunctive knowledge
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Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification
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Building Natural Language Generation Systems (Studies in Natural Language Processing)
An introduction to bipolar representations of information and preference
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Artificial Intelligence
Uncertainty modelling for vague concepts: A prototype theory approach
Artificial Intelligence
Fuzzy Sets and Systems
A bipolar model of assertability and belief
International Journal of Approximate Reasoning
Imprecise bipolar belief measures based on partial knowledge from agent dialogues
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Vagueness as probabilistic linguistic knowledge
ViC'09 Proceedings of the 2009 international conference on Vagueness in communication
Bipolar semantic cells: an interval model for linguistic labels
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Gradualness, uncertainty and bipolarity: Making sense of fuzzy sets
Fuzzy Sets and Systems
Not Exactly: In Praise of Vagueness
Not Exactly: In Praise of Vagueness
Belief functions on distributive lattices
Artificial Intelligence
Conditional beliefs in a bipolar framework
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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This paper proposes an integrated approach to indeterminacy and epistemic uncertainty in order to model an intelligent agent@?s decision making about the assertability of vague statements. Initially, valuation pairs are introduced as a model of truth-gaps for propositional logic sentences. These take the form of lower and upper truth-valuations representing absolutely true and not absolutely false respectively. In particular, we consider valuation pairs based on supervaluationist principles and also on Kleene@?s three-valued logic. The relationship between Kleene valuation pairs and supervaluation pairs is then explored in some detail with particular reference to a natural ordering on semantic precision. In the second part of the paper we extend this approach by proposing bipolar belief pairs as an integrated model combining epistemic uncertainty and indeterminacy. These comprise of lower and upper belief measures on propositional sentences, defined by a probability distribution on a finite set of possible valuation pairs. The properties of these measures are investigated together with their relationship to different types of uncertainty measure. Finally, we apply bipolar belief measures in a preliminary decision theoretic study so as to begin to understand how the use of vague expressions can help to mitigate the risk associated with making forecasts or promises. This then has potential applications to natural language generation systems.