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

Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

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.

ExtrIntDetect—A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence

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.

An Iterated Hybrid Local Search Algorithm for Pick-and-Place Sequence Optimization

This paper shows the results of our study on the pick-and-place optimization problem. To solve this problem efficiently, an iterated hybrid local search algorithm (IHLS) which combines local search with integer programming is proposed. In the section of local search, the greedy algorithm with distance weight strategy and the convex-hull strategy is developed to determine the pick-and-place sequence; in the section of integer programming, an integer programming model is built to complete the feeder assignment problem. The experimental results show that the IHLS algorithm we proposed has high computational efficiency. Furthermore, compared with the genetic algorithm and the memetic algorithm, the IHLS is less time-consuming and more suitable in solving a large-scale problem.