An Entropy-based Measure of Intelligence Degree of System Structures
This work addresses the challenge of quantifying intelligence in systems for researchers in AI and complex systems, offering a potential approach to understand natural evolution and design life-like structures, though it appears incremental as it builds on existing entropy and structural concepts.
The paper tackles the problem of measuring system intelligence based on structural properties, proposing an entropy-based measure that quantifies intelligence degree through function diversity and order generation, and shows that some structures are smarter than others under this measure.
In this paper, we investigate how to measure the intelligence of systems under specific structures. Two indicators are adopted to characterize the intelligence of a given structure, namely the function diversity of the structure, and the ability to generate order under specific environments. A measure of intelligence degree is proposed, with which the intelligence degree of several basic structures is calculated. It is shown that some structures are indeed "smarter" than the others under the proposed measure. The results add a possible way of revealing the evolution mechanism of natural life and constructing life-like structures with high intelligence degree.