A deductive argument is a pair where the first item is a set of premises, the second item is a claim, and the premises entail the claim. This can be formalised by assuming a logical language for the premises and the c...
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A deductive argument is a pair where the first item is a set of premises, the second item is a claim, and the premises entail the claim. This can be formalised by assuming a logical language for the premises and the claim, and logical entailment (or consequence relation) for showing that the claim follows from the premises. Examples of logics that can be used include classical logic, modal logic, description logic, temporal logic, and conditional logic. A counterargument for an argument A is an argument B where the claim of B contradicts the premises of A. Different choices of logic, and different choices for the precise definitions of argument and counterargument, give us a range of possibilities for formalising deductive argumentation. Further options are available to us for choosing the arguments and counterarguments we put into an argument graph. If we are to construct an argument graph based on the arguments that can be constructed from a knowledgebase, then we can be exhaustive in including all arguments and counterarguments that can be constructed from the knowledgebase. But there are other options available to us. We consider some of the possibilities in this review.
argumentation can be modelled at an abstract level using a directed graph where each node denotes an argument and each arc denotes an attack by one argument on another. Since arguments are often uncertain, it can be u...
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argumentation can be modelled at an abstract level using a directed graph where each node denotes an argument and each arc denotes an attack by one argument on another. Since arguments are often uncertain, it can be useful to quantify the uncertainty associated with each argument. Recently, there have been proposals to extend abstract argumentation to take this' uncertainty into account. This assigns a probability value for each argument that represents the degree to which the argument is believed to hold, and this is then used to generate a probability distribution over the full subgraphs of the argument graph, which in turn can be used to determine the probability that a set of arguments is admissible or an extension. In order to more fully understand uncertainty in argumentation, in this paper, we extend this idea by considering logic-based argumentation with uncertain arguments. This is based on a probability distribution over models of the language, which can then be used to give a probability distribution over arguments that are constructed using classical logic. We show how this formalization of uncertainty of logical arguments relates to uncertainty of abstract arguments, and we consider a number of interesting classes of probability assignments. (C) 2012 Elsevier Inc. All rights reserved.
A common assumption for logic-based argumentation is that an argument is a pair (Phi, alpha) where Phi is minimal subset of the knowledgebase such that Phi is consistent and Phi entails the claim alpha. Different logi...
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A common assumption for logic-based argumentation is that an argument is a pair (Phi, alpha) where Phi is minimal subset of the knowledgebase such that Phi is consistent and Phi entails the claim alpha. Different logics provide different definitions for consistency and entailment and hence give us different options for formalising arguments and counterarguments. The expressivity of classical propositional logic allows for complicated knowledge to be represented but its computational cost is an issue. In previous work we have proposed addressing this problem using connection graphs and resolution in order to generate arguments for claims that are literals. Here we propose a development of this work to generate arguments for claims that are disjunctive clauses of more than one disjunct, and also to generate counteraguments in the form of canonical undercuts (i.e. arguments that with a claim that is the negation of the conjunction of the support of the argument being undercut). (C) 2011 Elsevier Inc. All rights reserved.
There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logic...
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ISBN:
(纸本)9781607506195
There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logic-based argumentation is that an argument is a pair where Phi is a minimal subset of the knowledgebase such that Phi is consistent and Phi entails the claim alpha. We call the logic used for consistency and entailment, the base logic. Different base logics provide different definitions for consistency and entailment and hence give us different options for argumentation. This paper discusses some of the commonly used base logics in logic-based argumentation, and considers various criteria that can be used to identify commonalities and differences between them.
logic-based argumentation (LBA) exhibits unique properties and advantages over other kinds of argumentation proceedings, namely: the adequacy to logic-based pre-argument reasoning, similarity to the human reasoning pr...
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ISBN:
(纸本)354000131X
logic-based argumentation (LBA) exhibits unique properties and advantages over other kinds of argumentation proceedings, namely: the adequacy to logic-based pre-argument reasoning, similarity to the human reasoning process, reasoning with incomplete information and argument composition and extension. logic enables a formal specification to be built and a quick prototype to be developed. In order for LBA to achieve feasibility in Electronic Commerce scenarios, a set of properties must be present: self-support, correctness, conjugation, temporal containment and acyclicity. At the same time, LBA is shown to achieve stability in argument exchange (guaranteed success problem) and, depending on the definition of success, computational efficiency at each round (success problem).
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