{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:46:33Z","timestamp":1760240793321,"version":"build-2065373602"},"reference-count":82,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T00:00:00Z","timestamp":1567555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CHIST-ERA programme supported by the Future and Emerging Technologies (FET) programme of the European Union through the ERA-NET funding scheme","award":["UEFISCDI 101\/2019"],"award-info":[{"award-number":["UEFISCDI 101\/2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this research, we define a specific type of performance of the intelligent agent-based systems (IABSs) in terms of a difficult problem-solving intelligence measure. Many studies present the successful application of intelligent cooperative multiagent systems (ICMASs) for efficient, flexible and robust solving of difficult real-life problems. Based on a comprehensive study of the scientific literature, we conclude that there is no unanimous view in the scientific literature on machine intelligence, or on what an intelligence metric must measure. Metrics presented in the scientific literature are based on diverse paradigms. In our approach, we assume that the measurement of intelligence is based on the ability to solve difficult problems. In our opinion, the measurement of intelligence in this context is important, as it allows the differentiation between ICMASs based on the degree of intelligence in problem-solving. The recent OutIntSys method presented in the scientific literature can identify systems with outlier high and outlier low intelligence from a set of studied ICMASs. In this paper, a novel universal method called ExtrIntDetect, defined on the basis of a specific series of computing processes and analyses, is proposed for the detection of the ICMASs with statistical outlier low and high problem-solving intelligence from a given set of studied ICMASs. ExtrIntDetect eliminates the disadvantage of the OutIntSys method with respect to its limited robustness. The recent symmetric MetrIntSimil metric presented in the literature is capable of measuring and comparing the intelligence of large numbers of ICMASs and based on their respective problem-solving intelligences in order to classify them into intelligence classes. Systems whose intelligence does not statistically differ are classified as belonging to the same class of intelligent systems. Systems classified in the same intelligence class are therefore able to solve difficult problems using similar levels of intelligence. One disadvantage of the symmetric MetrIntSimil lies in the fact that it is not able to detect outlier intelligence. Based on this fact, the ExtrIntDetect method could be used as an extension of the MetrIntSimil metric. To validate and evaluate the ExtrIntDetect method, an experimental evaluation study on six ICMASs is presented and discussed.<\/jats:p>","DOI":"10.3390\/sym11091123","type":"journal-article","created":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T03:22:36Z","timestamp":1567653756000},"page":"1123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["ExtrIntDetect\u2014A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6254-9291","authenticated-orcid":false,"given":"L\u00e1szl\u00f3 Barna","family":"Iantovics","sequence":"first","affiliation":[{"name":"Department of Informatics, Faculty of Sciences and Letters, University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, 540124 T\u00e2rgu Mure\u0219, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roumen","family":"Kountchev","sequence":"additional","affiliation":[{"name":"Department of Radio Communications and Video Technologies, Faculty of Telecommunications, Technical University of Sofia, 1000 Sofia, Bulgaria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gloria Cerasela","family":"Cri\u0219an","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Informatics, Faculty of Sciences, Vasile Alecsandri University of Bac\u0103u, 600115 Bac\u0103u, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0166-5316(01)00042-6","article-title":"Design and performance of cognitive packet networks","volume":"46","author":"Gelenbe","year":"2001","journal-title":"Perform. 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