Artificial Intelligence
Assumptions, beliefs and probabilities
Artificial Intelligence
Information flow: the logic of distributed systems
Information flow: the logic of distributed systems
A logic-based theory of deductive arguments
Artificial Intelligence
Domains for Denotational Semantics
Proceedings of the 9th Colloquium on Automata, Languages and Programming
Assumption-Based Modeling Using ABEL
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
On the acceptability of arguments in preference-based argumentation
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Extending uncertainty formalisms to linear constraints and other complex formalisms
International Journal of Approximate Reasoning
A dialogue mechanism for public argumentation using conversation policies
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
A Dialogue Mechanism for Public Argumentation Using Conversation Policies
Argumentation in Multi-Agent Systems
Formal Theories of Information
Belief functions on real numbers
International Journal of Approximate Reasoning
Solving weighted argumentation frameworks with soft constraints
CSCLP'09 Proceedings of the 14th Annual ERCIM international conference on Constraint solving and constraint logic programming
Argumentation in bayesian belief networks
ArgMAS'04 Proceedings of the First international conference on Argumentation in Multi-Agent Systems
Logic of dementia guidelines in a probabilistic argumentation framework
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Unifying logical and probabilistic reasoning
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Argumentation logic to assist in security administration
Proceedings of the 2012 workshop on New security paradigms
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Probability is usually closely related to Boolean structures, i.e., Boolean algebras or propositional logic. Here we show, how probability can be combined with non-Boolean structures, and in particular non-Boolean logics. The basic idea is to describe uncertainty by (Boolean) assumptions, which may or may not be valid. The uncertain information depends then on these uncertain assumptions, scenarios or interpretations. We propose to describe information in information systems, as introduced by Scott into domain theory. This captures a wide range of systems of practical importance such as many propositional logics, first order logic, systems of linear equations, inequalities, etc. It covers thus both symbolic as well as numerical systems. Assumption-based reasoning allows then to deduce supporting arguments for hypotheses. A probability structure imposed on the assumptions permits to quantify the reliability of these supporting arguments and thus to introduce degrees of support for hypotheses. Information systems and related information algebras are formally introduced and studied in this paper as the basic structures for assumption-based reasoning. The probability structure is then formally represented by random variables with values in information algebras. Since these are in general non-Boolean structures some care must be exercised in order to introduce these random variables. It is shown that this theory leads to an extension of Dempster-Shafer theory of evidence and that information algebras provide in fact a natural frame for this theory.