Tree Based Decision Strategies and Auctions in Computational Multi-Agent Systems

0477352 - ÚI 2018 RIV CU eng J - Journal Article
Šlapák, M. - Neruda, Roman
Tree Based Decision Strategies and Auctions in Computational Multi-Agent Systems.
Investigacion Operacional. Roč. 38, č. 4 (2017), s. 335-342. ISSN 0257-4306
Institutional support: RVO:67985807
Keywords : auction systems * decision making * genetic programming * multi-agent system * task distribution
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://rev-inv-ope.univ-paris1.fr/fileadmin/rev-inv-ope/files/38417/38417-04.pdf

This paper deals with an agent-based implementation of data mining system where a set of tasks is being processed in a distributed manner. The key role within such a system is the decision strategy of a computational agent which should consider accepting or rejecting a particular task based on various decision strategies. We present several adaptive decision strategies and compare them to traditional auction-based task distribution. Results show that optimal decision making strategy depends on the task set characteristic properties { e.g. how distinct are the best and the worst average results of each task type in dataset.
Permanent Link: http://hdl.handle.net/11104/0273719