Abstract
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.
Highlights
The evaluation of the performance of computing systems includes diverse research topics, including the performance evaluation of cognitive packet networks [1], cognitive packet networks where network worms operate [2], peering-agreements among systems for peer-to-peer traffic that has some kind of autonomy [3], the Internet [4], large-scale SCI multiprocessors [5], algorithms [6], and supply chain management based on Internet of Things technology [7]
The recent symmetric MetrIntSimil metric presented in the literature is capable of measuring and comparing the intelligence of large numbers of intelligent cooperative multiagent systems (ICMASs) and based on their respective problem-solving intelligences in order to classify them into intelligence classes
ICMASs are important with respect to the fact that even in CMASs composed of less complex agents, increased intelligence could emerge at a system level [15] as result of flexible, robust and efficient cooperative problem-solving
Summary
The evaluation of the performance of computing systems includes diverse research topics, including the performance evaluation of cognitive packet networks [1], cognitive packet networks where network worms operate [2], peering-agreements among systems for peer-to-peer traffic that has some kind of autonomy [3], the Internet [4], large-scale SCI multiprocessors [5], algorithms [6], and supply chain management based on Internet of Things technology [7]. Neisser et al performed a study [28] that showed that the measurement of intelligence based on IQ test scores without any other considerations ignores many important aspects that are specific to human cognition. A universal method is effectively being proposed, called the Robust Extreme Intelligence Detection Method (ExtrIntDetect), for the detection of ICMASs with statistically extremely low and high MIQ in solving problems from a set of considered ICMASs. The novelty of the proposal lies in designing a specific series of computing processes and analyses. The symmetric MetrIntSimil metric [31] is able to compare ICMASs based on the intelligence with which they solve difficult problems and classify them into intelligence classes.
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