Measuring the machine intelligence quotient (MIQ) of human-machine cooperative systems

Park, Hee-Jun; Kim, Byung Kook; Lim, Kim

doi:10.1109/3468.911366

Cited by 48 publications

(5 citation statements)

References 19 publications

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“…Inspired by the g factor of intelligence and its measure, the intelligence quotient (IQ), some researchers proposed an equivalent metric for artificial intelligence: the machine intelligence quotient (MIQ). Park et al ( 2001 ) proposed a framework for MIQ measurement in human-machine cooperative systems. These authors affirm that “machine intelligence is the ability to replicate the human mental faculty and to perform human-like.” Following it, they define MIQ as “the measure of autonomy and performance for unanticipated events.” Actually, from their definitions, both uses of intelligence relate to different constructs or phenomena.…”

Section: Measuring Intelligencementioning

confidence: 99%

Section: Measuring Intelligencementioning

confidence: 99%

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Intelligence across humans and machines: a joint perspective

Da Silveira,

Lopes

2023

Front. Psychol.

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This paper aims to address the divergences and contradictions in the definition of intelligence across different areas of knowledge, particularly in computational intelligence and psychology, where the concept is of significant interest. Despite the differences in motivation and approach, both fields have contributed to the rise of cognitive science. However, the lack of a standardized definition, empirical evidence, or measurement strategy for intelligence is a hindrance to cross-fertilization between these areas, particularly for semantic-based applications. This paper seeks to equalize the definitions of intelligence from the perspectives of computational intelligence and psychology, and offer an overview of the methods used to measure intelligence. We argue that there is no consensus for intelligence, and the term is interchangeably used with similar, opposed, or even contradictory definitions in many fields. This paper concludes with a summary of its central considerations and contributions, where we state intelligence is an agent's ability to process external and internal information to find an optimum adaptation (decision-making) to the environment according to its ontology and then decode this information as an output action.

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Section: Measuring Intelligencementioning

confidence: 99%

Section: Measuring Intelligencementioning

confidence: 99%

Intelligence across humans and machines: a joint perspective

Da Silveira,

Lopes

2023

Front. Psychol.

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“…Schreiner studied the research question of how precisely ISs are defined, and analyzed how to measure and compare the intelligence capabilities of ISs. Park et al [39] introduced the notion of an intelligence task graph to study the measurement of machine intelligence of human-machine cooperative systems. Anthon and Jannett [40] analyzed the ABS intelligence considering the ability to compare alternatives with different levels of complexity.…”

Section: Metrics For Measuring Machine Intelligencementioning

confidence: 99%

Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Iantovics

2021

Mathematics

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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.

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“…MIQ is a measure of autonomy and performance for unanticipated events and links the infrastructural needs of an outsourcing institution with its throughput. MIQ differs significantly from other indices like control performance, reliability, and fault-diagnosis (Park, Kim, & Lim, 2001). The use of a rule oriented approach for workflow control and design consistency checking has been illustrated in DPSSEE (Deng et al, 2003).…”

Section: Offshore Engineering and Management (Oem)mentioning

confidence: 99%

Transformation from the Information Age to the Conceptual Age

Patki¹,

Patki

Kulkarni

2008

Journal of Information Technology Research

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The previous decades have seen the emergence of the Information Age, where the key focus was on knowledge acquisition and application. With the emergence of cross-domain disciplines like outsourcing, we are witnessing a trend towards creative knowledge, rational application, and innovation. We are now progressing from an era that was information-dependent towards the era that revolves around concept development. This age, referred to as the Conceptual Age, will be dominated by six new senses--design, story, symphony, empathy, play and meaning--creating a need to diverge from the current reliance on linear and sequential algorithmic practices in outsourcing and to adopt cognition based engineering and management approaches. This article lays the foundation for offshore engineering and management (OEM) and discusses estimation issues in OEM that have their roots in software engineering. Also, this article identifies the limitations of the current methodologies from an outsourcing point of view, and delineates how they can be deployed effectively for an outsourced environment.

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Measuring the machine intelligence quotient (MIQ) of human-machine cooperative systems

Cited by 48 publications

References 19 publications

Intelligence across humans and machines: a joint perspective

Intelligence across humans and machines: a joint perspective

Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Transformation from the Information Age to the Conceptual Age

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