Instrumenting MultiAgent Organisations with Artifacts to Support Reputation Processes Reputation is often cited as an instrument to enforce norm compliance: agents that do not follow the norms have their reputation decreased. Conceiving reputation as a collective process, i.e. a kind of shared voices as proposed by Conte u0026 Paolucci, is not a simple task. In this paper, we propose a rst step in this direction by instrumenting multiagent organisation with an artifact that publishes some objective evaluations of the performance of the agents with respect to their behaviour within the organisation. The members of the organisation can then read these evaluations and build up their reputation of others. The artifact serves thus as an instrument that aid in the building of the reputation of the agents. We propose that the evaluation of the agents is not simply based on their obedience to norms, but also considers their proactiveness and their contribution to the success of collective tasks that are being executed in the organisation. This proposal is detailed and exempli ed in the context of the Moise organisational model supported by a set of organisational artifacts as proposed in the ora4mas approach.

Ontologybased products reputation model Different reputation models are used in the web in order to generate reputation values for products using usesu0027 review data. Most of the current reputation models use review ratings and neglect usersu0027 textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And thatu0027s why the use of usersu0027 textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for usersu0027 extracted opinions about productsu0027 features from usersu0027 text. Our model uses features ontology in order to define general features and subfeatures of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.