DOI: 10.1007/978-3-540-75256-1_23 Conference: Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 9th European Conference, ECSQARU 2007, Hammamet, Tunisia, October 31 - November 2, 2007, Proceedings
Previous logic-based handling of arguments has mainly focused on explanation or justification in presence of inconsistency.
As a consequence, only one type of argument has been considered, namely the explanatory type; several argumentation frameworks
have been proposed for generating and evaluating explanatory arguments. However, recent investigations of argument-based negotiation
have emphasized other types of arguments, such as threats, rewards, tips, and warnings. In parallel, cognitive psychologists recently started studying the characteristics of these different types of arguments,
and the conditions under which they have their desired effect. Bringing together these two lines of research, we present in
this article some logical definitions as well as some criteria for evaluating each type of argument. Empirical findings from
cognitive psychology validate these formal results.
"In turn, according to the model, proportionality should impact only the credibility of threats. In the second experiment, we compared the persuasiveness of equivalent unilateral and bilateral advices and warnings by adapting Amgoud et al's (2007) example of a typical advice, that is, 'If you revise the paper, the editor will accept it' , and by applying their model's contrapositive principle, namely, that an advice can be turned into a warning by negating its antecedent and consequent, and vice versa. In particular, 'If you do not revise the paper, the editor will not accept it' is the contrapositive warning of the advice above, and pragmatically they both have the same intention, that is, to promote the revision. "
[Show abstract][Hide abstract] ABSTRACT: Promising and warning are speech acts that have to be credible to be persuasive. The question is: When does a promise become incredible and a warning unpersuasive? Whereas credibility has been researched from a social persuasion perspective, this article answers that question empirically, from an adaptive heuristics perspective. First, we present a satisficing algorithm that discriminates conditional promises, threats, advices, and warnings by pragmatic cues. Then, we discuss an alternative model of this algorithm that further accounts for the credibility of these conditionals by formal principles, and also adds two hypotheses: (1) Threats but not promises are more credible with proportionate than disproportionate consequences, and (2) Both advices and warnings are more persuasive with bilateral than unilateral consequences. Finally, we present two experiments and their follow-ups that, consistent with the pragmatic algorithm, provide evidence against both hypotheses.
[Show abstract][Hide abstract] ABSTRACT: In multi-agent systems (MAS), negotiation provides a powerful metaphor for automating the allocation and reallocation of resources.
Methods for automated negotiation in MAS include auction-based protocols and alternating offer bargaining protocols. Recently,
argumentation-based negotiation has been accepted as a promising alternative to such approaches. Interest-based negotiation
(IBN) is a form of argumentation-based negotiation in which agents exchange (1) information about their underlying goals;
and (2) alternative ways to achieve these goals. However, the usefulness of IBN has been mostly established in the literature
by appeal to intuition or by use of specific examples. In this paper, we propose a new formal model for reasoning about interest-based
negotiation protocols. We demonstrate the usefulness of this framework by defining and analysing two different IBN protocols.
In particular, we characterise conditions that guarantee their advantage (in the sense of expanding the set of individual
rational deals) over the more classic proposal-based approaches to negotiation.
Annals of Mathematics and Artificial Intelligence 04/2009; 55(3):253-276. DOI:10.1007/s10472-009-9145-6 · 0.69 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.