Argumentation in Multi-Agent Systems: 4th International by Leila Amgoud, Yannis Dimopoulos, Pavlos Moraitis (auth.),

By Leila Amgoud, Yannis Dimopoulos, Pavlos Moraitis (auth.), Iyad Rahwan, Simon Parsons, Chris Reed (eds.)

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By Leila Amgoud, Yannis Dimopoulos, Pavlos Moraitis (auth.), Iyad Rahwan, Simon Parsons, Chris Reed (eds.)

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Read Online or Download Argumentation in Multi-Agent Systems: 4th International Workshop, ArgMAS 2007, Honolulu, HI, USA, May 15, 2007, Revised Selected and Invited Papers PDF

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Extra info for Argumentation in Multi-Agent Systems: 4th International Workshop, ArgMAS 2007, Honolulu, HI, USA, May 15, 2007, Revised Selected and Invited Papers

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Argumentation is an important feature of human intelligence: the ability to understand and manipulate arguments is fundamental to understand a new problem, to reason about actions, and to perform scientific research. An argument is a sequence of inferences leading to a valid conclusion: a set of arguments is managed by an argumentation component that is particularly useful in the case of conflicting information. In this paper we elaborate on the idea of social support for argumentation in a MAS, by coupling the agents and artifacts (A&A) meta-model for MAS with argumentation theory.

B A→B A (Ab) For example, all humans are mortal, Parmenide is a mortal, then Parmenide is a human, in tuple form looks like argument (name, beliefs([mortal (parmenide)], [clause(mortal (X ), [human(X )])]), infer (Ab), conclusions([human(parmenide)]). The definition of contrast is not trivial because there are different type of attack well defined in [4]. Following those definitions, two possible types of attack are ‘conclusions against conclusions’ – called rebuttals – and ‘conclusions against beliefs’—called undercuts.

For example, if we consider a list of four input arguments [a,b,c,d], the resulting search trees are shown in figure 1. There, the possible partial solutions are denoted in square brackets. The algorithm can also be used in a dynamic context with inputs in succession. To find a new solution, after each update we have to insert new arguments in each existing conflict free set, and run the algorithm again. 0 [6] and shows the main predicates implementing the conflict free set division. selection(X,[X|Rest],Rest).

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