<i>MetrIntPair</i>-A Novel Accurate Metric for the Comparison of Two Cooperative Multiagent Systems Intelligence Based on Paired Intelligence Measurements

Iantovics, László Barna; Rotar, Corina; Niazi, Muaz A.

doi:10.1002/int.21903

Cited by 21 publications

(7 citation statements)

References 56 publications

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“…The achieved accuracy of the prediction was calculated by the Pearson's correlation coefficient [21] R between the target and predicted values and by their difference (see Figures 7 and 8). It could be relied on since it proved its efficiency as a statistical measure investigating complex intelligence based systems [22]. We can observe that in the hidden layer the best neurons number achieving the highest R coefficient was N h = 45 for Dice 1 and N h = 20 for Dice 2.…”

Section: Artificial Neural Network Resultsmentioning

confidence: 95%

Analysis of Usability for the Dice CAPTCHA

et al. 2019

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This paper explores the usability of the Dice CAPTCHA via analysis of the time spent to solve the CAPTCHA, and number of tries for solving the CAPTCHA. The experiment was conducted on a set of 197 subjects who use the Internet, and are discriminated by age, daily Internet usage in hours, Internet experience in years, and type of device where a solution to the CAPTCHA is found. Each user was asked to find a solution to the Dice CAPTCHA on a tablet or laptop, and the time to successfully find a solution to the CAPTCHA for a given number of attempts was registered. Analysis was performed on the collected data via association rule mining and artificial neural network. It revealed that the time to find a solution in a given number of attempts of the CAPTCHA depended on different combinations of values of user’s features, as well as the most meaningful features influencing the solution time. In addition, this dependence was explored through prediction of the CAPTCHA solution time from the user’s features via artificial neural network. The obtained results are very helpful to analyze the combination of features having an influence on the CAPTCHA solution, and consequently, to find the CAPTCHA mostly complying to the postulate of “ideal” test.

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Section: Artificial Neural Network Resultsmentioning

confidence: 95%

Analysis of Usability for the Dice CAPTCHA

et al. 2019

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“…In [65] we proposed an innovative metric called MetrIntPair (Metric for Pairwise Intelligence Comparison of Agent-Based Systems) for comparison of two cooperative multiagent systems problemsolving intelligence. MetrIntPair is able to make an accurate comparison by taking into consideration the variability in the problem-solving intelligence of systems.…”

Section: Metrics For Measuring Machine Intelligencementioning

confidence: 99%

“…In our previous studies [65,66] we highlighted the fact that a metric for intelligence measurement needs to treat the aspect of variability in intelligence. The metrics presented in [65,66] are illustrative to the situation where the treatment of the variability by a metric can result in advantages to the accuracy and robustness in intelligence level comparison and classification of the systems based on their intelligence. A disadvantage of many previously proposed intelligence metrics in the literature consists of limited universality.…”

Section: Metrics For Measuring Machine Intelligencementioning

confidence: 99%

II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

Iantovics¹,

Iakovidis²,

Nechita³

2019

IJCIS

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A novel accurate and robust metric called II-Learn for measuring the increase of intelligence of a system after a learning process is proposed. We define evolving learning systems, as systems that are able to make at least one measurable evolutionary step by learning. To prove the effectiveness of the metric we performed a case study, using a learning system. The universality of II-Learn is based on the fact that it does not depend on the architecture of the studied system.

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“…Ref. [46] proposes a novel metric called MetrIntPair for measuring the machine intelligence of cooperative multiagent systems. The MetrIntPair metric is able to exactly analyze the intelligence of two cooperative multiagent systems.…”

Section: Metrics For Measuring the Machine Intelligencementioning

confidence: 99%

MetrIntSimil—An Accurate and Robust Metric for Comparison of Similarity in Intelligence of Any Number of Cooperative Multiagent Systems

2018

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Intelligent cooperative multiagent systems are applied for solving a large range of real-life problems, including in domains like biology and healthcare. There are very few metrics able to make an effective measure of the machine intelligence quotient. The most important drawbacks of the designed metrics presented in the scientific literature consist in the limitation in universality, accuracy, and robustness. In this paper, we propose a novel universal metric called MetrIntSimil capable of making an accurate and robust symmetric comparison of the similarity in intelligence of any number of cooperative multiagent systems specialized in difficult problem solving. The universality is an important necessary property based on the large variety of designed intelligent systems. MetrIntSimil makes a comparison by taking into consideration the variability in intelligence in the problem solving of the compared cooperative multiagent systems. It allows a classification of the cooperative multiagent systems based on their similarity in intelligence. A cooperative multiagent system has variability in the problem solving intelligence, and it can manifest lower or higher intelligence in different problem solving tasks. More cooperative multiagent systems with similar intelligence can be included in the same class. For the evaluation of the proposed metric, we conducted a case study for more intelligent cooperative multiagent systems composed of simple computing agents applied for solving the Symmetric Travelling Salesman Problem (STSP) that is a class of NP-hard problems. STSP is the problem of finding the shortest Hamiltonian cycle/tour in a weighted undirected graph that does not have loops or multiple edges. The distance between two cities is the same in each opposite direction. Two classes of similar intelligence denoted IntClassA and IntClassB were identified. The experimental results show that the agent belonging to IntClassA intelligence class is less intelligent than the agents that belong to the IntClassB intelligence class.

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MetrIntPair-A Novel Accurate Metric for the Comparison of Two Cooperative Multiagent Systems Intelligence Based on Paired Intelligence Measurements

Cited by 21 publications

References 56 publications

Analysis of Usability for the Dice CAPTCHA

Analysis of Usability for the Dice CAPTCHA

II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

MetrIntSimil—An Accurate and Robust Metric for Comparison of Similarity in Intelligence of Any Number of Cooperative Multiagent Systems

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