Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

László Barna Iantovics

doi:10.3390/math9060681

Abstract

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

Highlights

Computer systems encounter various issues during problem-solving, including high computational complexity, especially in the case of NP-hard problems, and the presence of different types of uncertainties, e.g., due to missing or erroneous data
To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several cooperative multiagent systems (CMASs) composed of agents specialized in solving an NP-hard problem
To prove the effectiveness of the MetrIntPairII metric, we investigate a case study, using different CMASs that operate by mimicking diverse biological swarms

Summary

Introduction

Computer systems encounter various issues during problem-solving, including high computational complexity, especially in the case of NP-hard problems, and the presence of different types of uncertainties, e.g., due to missing or erroneous data. Applications of agent-based systems (ABSs) include the following: diverse problems solving in Industry 4.0 [5]; adaptive clustering [6]; modeling strategic interactions in diverse democratic systems [7]; investigation on supply chain of product recycling [8]; detecting the proportion of traders in the stock market [9]; control design in the presence of actuator saturation [10]; agent-based simulator for environmental land change [11]; distributed intrusion detection [12]; investigations of complex information systems [13]; discovering Semantic Web services through process similarity matching [14]; power system control and protection [15]; studies on task type and critic information in credit assignments [16]; planning with joint actions [17]; patient scheduling [18]; study compliance with safety regulations [19]; multi-objective optimization [20]. Problem-solving based on computational intelligence techniques include adaptive reflection detection and location in iris biometric images [22], arithmetic codes for concurrent error detection in artificial neural networks [23], and support in medical decision-making [24,25]

Results

Discussion

Conclusion