By Andrew W. Wicker, Jon Doyle (auth.), Francien Dechesne, Hiromitsu Hattori, Adriaan ter Mors, Jose Miguel Such, Danny Weyns, Frank Dignum (eds.)
This publication constitutes the completely refereed post-workshop lawsuits of five workshops, held on the tenth overseas convention on independent brokers and Multiagent platforms, AAMAS 2011, in Taipei, Taiwan, might 2-6, 2011. The 37 revised complete papers provided including 1 invited paper have been conscientiously reviewed and chosen from a number of submissions. The papers are geared up in sections at the workshops Agent-Based Modeling for coverage Engineering (AMPLE), Agent-Oriented software program Engineering (AOSE), independent Robots and Multirobot structures (ARMS), facts orientated positive Mining and Multi-Agent Simulation, hugely Multi-Agent structures: types, tools and instruments (DOCM³AS), and Infrastructures and instruments for Multiagent platforms (ITMAS).
Read Online or Download Advanced Agent Technology: AAMAS 2011 Workshops, AMPLE, AOSE, ARMS, DOCM3AS, ITMAS, Taipei, Taiwan, May 2-6, 2011. Revised Selected Papers PDF
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Additional resources for Advanced Agent Technology: AAMAS 2011 Workshops, AMPLE, AOSE, ARMS, DOCM3AS, ITMAS, Taipei, Taiwan, May 2-6, 2011. Revised Selected Papers
Example text
Below of each experiment, we presented the conclusion regarding models prediction success: Towards Qualitative Reasoning for Policy Decision Support in Demonstrations 27 – yes, denoting success – one level error, corresponding to one ordinal level mistake such as instead of classify to high violence the model classified to low – two level error, corresponding to two ordinal levels mistake such as instead of classify to high violence the model classified to zero. Results. The results demonstrate that BIU model made much better predictions than Base and Police models.
The second column corresponds to the event outcome as it occurred in real life. Then we present the models predictions, its numeric distribution, for each of the three possible outcome: no violence, low violence and high violence. For each such outcome we present the probability. Below of each experiment, we presented the conclusion regarding models prediction success: Towards Qualitative Reasoning for Policy Decision Support in Demonstrations 27 – yes, denoting success – one level error, corresponding to one ordinal level mistake such as instead of classify to high violence the model classified to low – two level error, corresponding to two ordinal levels mistake such as instead of classify to high violence the model classified to zero.
Thanks to K. Ushi. F. Dechesne et al. ): AAMAS 2011 Workshops, LNAI 7068, pp. 19–34, 2012. c Springer-Verlag Berlin Heidelberg 2012 20 N. A. Kaminka, and A. Zilka that impact violence during demonstrations, but it mostly reports on partial, macro-level qualitative descriptions of the influencing factors. However, there are also technologies that can be used to generate predictions which are not requires building a model such as machine learning techniques, for example decision tree. Decision tree takes as an input set of properties and build a model, a set of rules, that allows most accurate classification to the given data.