By Longbing Cao
This publication constitutes the completely refereed and revised chosen papers from the ninth overseas Workshop on brokers and knowledge Mining interplay, ADMI 2013, held in Saint Paul, MN, united states in may possibly 2013. the ten papers offered during this quantity have been conscientiously chosen for inclusion within the publication and are prepared in topical sections named agent mining and knowledge mining.
Read Online or Download Agents and Data Mining Interaction: 9th International Workshop, ADMI 2013, Saint Paul, MN, USA, May 6-7, 2013, Revised Selected Papers PDF
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Extra info for Agents and Data Mining Interaction: 9th International Workshop, ADMI 2013, Saint Paul, MN, USA, May 6-7, 2013, Revised Selected Papers
2. Comparing conventions across projects ODE project were on the ﬁles in the testing package (implying testers were not following conventions). However, this was not observed in the other two projects. There were substantial number of errors across all the projects on exceeding the line length (80 characters). The number of classes exceeding the method limits check (30 methods) were high in Ant project, but there were not any in the other two projects. There were more than 10 instances in all the three projects that had more than 7 methods in a class.
G. messages containing sanctions) that are added to any of the monitored repositories. For example, a message may contain information where agent A informs B that it has violated a norm. e. the sanction was for the right reason an actual violation). – Step 2: Upon sanction identiﬁcation, the monitoring agent sends a request to the reasoning agent that identiﬁes the reason for the sanction to occur. – Step 3: The reasoning agents identiﬁes the reason from the appropriate repositories. Note that the reason for the sanction to occur can be found in one or more of the repositories.
It also discusses the opportunities presented by the domain of software repositories for the study of norms. In particular, it points towards how norms can be mined by leveraging and extending prior work in the areas of Normative Multi-Agent Systems (NorMAS) and mining software repositories. 1 Introduction A good example of a large, real-world multi-agent organization involving a huge number of (human) agents is open source software development (OSSD). In fact, large-scale open source systems such as Linux and Android OS, used and tested by millions are developed by hundreds of contributors over extended period of time.