caa a22 4500 605514623 CHVBK 20210128100705.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s10458-014-9276-y doi (NATIONALLICENCE)springer-10.1007/s10458-014-9276-y An influence diagram based multi-criteria decision making framework for multirobot coalition formation [Elektronische Daten] [Sayan Sen, Julie Adams] Novel systems allocating coalitions of humans and unmanned heterogeneous vehicles will act as force multipliers for future real-world missions. Conventional coalition formation architectures seek to compute efficient robot coalitions by leveraging either a single greedy, approximation, or market-based algorithm, which renders such architectures inapplicable to a variety of real-world mission scenarios. A novel, intelligent multi-criteria decision making framework is presented that reasons over a library of coalition formation algorithms for selecting the most appropriate subset of algorithm(s) to apply to a wide spectrum of complex missions. The framework is based on influence diagrams in order to handle uncertainties in dynamic real-world environments. An existing taxonomy comprised of multiple mission and domain dependent features is leveraged to classify the coalition formation algorithms. Dimensionality reduction is achieved via principal component analysis, which extracts the most significant taxonomy features crucial for decision making. A link analysis technique provides the mission specific utility values of each feature-value pair and algorithm in the library. Experimental results demonstrate that the presented framework accurately selects the most appropriate subset of coalition formation algorithm(s) based on multiple mission criteria, when applied to a number of simulated real-world mission scenarios. The Author(s), 2014 Coalition formation nationallicence Influence diagrams nationallicence Link analysis nationallicence Multi-criteria decision making nationallicence Sen Sayan Department of Electrical Engineering and Computer Science, Vanderbilt University, 37240, Nashville, TN, USA aut Adams Julie Department of Electrical Engineering and Computer Science, Vanderbilt University, 37240, Nashville, TN, USA aut Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/6(2015-11-01), 1061-1090 1387-2532 29:6<1061 2015 29 10458 https://doi.org/10.1007/s10458-014-9276-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10458-014-9276-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sen Sayan Department of Electrical Engineering and Computer Science, Vanderbilt University, 37240, Nashville, TN, USA aut NATIONALLICENCE 700 1- Adams Julie Department of Electrical Engineering and Computer Science, Vanderbilt University, 37240, Nashville, TN, USA aut NATIONALLICENCE 773 0- Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/6(2015-11-01), 1061-1090 1387-2532 29:6<1061 2015 29 10458