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Autonomous Agents and Multi-Agent Systems

Erschienen in:

01.01.2016

Evaluation of a trust-modulated argumentation-based interactive decision-making tool

verfasst von: Elizabeth I. Sklar, Simon Parsons, Zimi Li, Jordan Salvit, Senni Perumal, Holly Wall, Jennifer Mangels

Erschienen in: Autonomous Agents and Multi-Agent Systems | Ausgabe 1/2016

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Abstract

The interactive ArgTrust application is a decision-making tool that is based on an underlying formal system of argumentation in which the evidence that influences a recommendation, or conclusion, is modulated according to values of trust that the user places in that evidence. This paper presents the design and analysis of a user study which was intended to evaluate the effectiveness of ArgTrust in a collaborative human–agent decision-making task. The results show that users’ interactions with ArgTrust helped them consider their decisions more carefully than without using the software tool.

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Called “negotiation” in [53], but much closer to what [76] calls “deliberation”.

For example, military intelligence traditionally separates information into that which comes from human sources, that which comes from signals intercepts, and that which comes from imagery. All of these sources can be rated with some measure of trustworthiness.

While stressing that this is purely illustrative—a real model of this example would be considerably more detailed.

In this scenario, because informants are paid for useful information, they are widely considered to simply make up plausible information with the result that it is considered to be untrustworthy, and certainly less trustworthy than information derived from the high-resolution imaging from a uav.

The latest version of ArgTrust at the time of writing implements all the common semantics.

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See, for example, Fig. 1 in [75].

Even the very straightforward mechanism of counting arguments for and against.

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Titel: Evaluation of a trust-modulated argumentation-based interactive decision-making tool
verfasst von: Elizabeth I. Sklar
Simon Parsons
Zimi Li
Jordan Salvit
Senni Perumal
Holly Wall
Jennifer Mangels
Publikationsdatum: 01.01.2016
Verlag: Springer US
Erschienen in: Autonomous Agents and Multi-Agent Systems / Ausgabe 1/2016
Print ISSN: 1387-2532
Elektronische ISSN: 1573-7454
DOI: https://doi.org/10.1007/s10458-015-9289-1

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