Agreement Technologies: Second International Conference, AT by Munindar P. Singh (auth.), Carlos Iván Chesñevar, Eva

By Munindar P. Singh (auth.), Carlos Iván Chesñevar, Eva Onaindia, Sascha Ossowski, George Vouros (eds.)

This quantity constitutes the refereed complaints of the second one foreign convention on contract applied sciences, AT 2013, held in Beijing, China, in August 2013. The 15 revised complete papers awarded including invited talks have been rigorously reviewed and chosen from a variety of submissions and concentrate on themes akin to semantic applied sciences, normative multiagent platforms, digital corporations and digital associations, argumentation and negotiation, belief and attractiveness, functions of contract applied sciences, contract applied sciences architectures, environments and methodologies, in addition to interdisciplinary foundations of contract technologies.

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Extra resources for Agreement Technologies: Second International Conference, AT 2013, Beijing, China, August 1-2, 2013. Proceedings

Example text

First, the energy cost of transmitting information to the sink increases, as all nodes are at a considerable distance to the sink. Second, as nodes are situated near each other, groups of a high number of nodes can be formed. To completely define the experimental setup considered, Table 1 presents the values assigned to the COSA algorithm parameters. A Multi-agent Approach to Energy-Aware WSNs Organization 39 Table 1. 95 Experimental Results To show the properties of COSA, we performed a set of experiments in the scenarios described in Section 3.

C. Delgado-Roman and C. Sierra variable under limits. The computational effort implied by these strategies is negligible although certainly the number of messages exchanged and the number of sampling actions taken by leaders and coalition members grow. Despite of this, these strategies increase the robustness of the algorithm as they allow a quicker detection of nodes’ measurements deviation. These strategies do not mend nodes’ malfunction, but they do reduce the effect that this can cause on the system’s global error.

The complexity of the proposed DCF algorithm is given in the following propositions. Proposition 1. The computation complexity of the algorithm is O(pn2 m) , where we denote with n = |A|, m = maxS∈CS {|αS |}, p = max{|EventQueue|}. Proof. The number of iterations that the algorithm needs to cycle through is bounded by a) the maximum number of events in the global queue O(p) (line 4); b) the maximum number of coalition mergers that may occur O(n), which corresponds to the formation of the grand coalition (line 7); c) O(nm) the maximum number of operations required in order to construct the list CandidateL.

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