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Understanding Human and Machine Interaction from Decision Perspective: An Empirical Study Based on the Game of Go.

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Název: Understanding Human and Machine Interaction from Decision Perspective: An Empirical Study Based on the Game of Go.
Autoři: Zhao, Ping, Li, Xuerong, Wang, Shouyang
Zdroj: Journal of Systems Science & Complexity; Apr2024, Vol. 37 Issue 2, p647-667, 21p
Abstrakt: The authors aim to interpret human and AI interactions from the decision perspective. The authors decompose the interaction analysis into the following main components in the context of interactions: Individual behavior patterns, interaction relationships, and comprehensive analysis. The authors interpret intertemporal decisions from a physical perspective and employ cross-discipline concepts and methodologies to extract the behavior characteristics of players in the empirical case study. About the individual behavior patterns, the authors find that human players prefer short-term periods to AI in decision-making. The interaction relationship analysis reveals a dynamic relationship between possible short-term co-movement and nearly counter-movement in the long run. The authors apply principal component analysis to descriptive indicators and discover a regular decision hierarchy. The main behavior pattern of players in the game of Go is switching between careful and daring behaviors. The differences in the decision hierarchies imply a discrepancy of patience between humans and AI. [ABSTRACT FROM AUTHOR]
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Abstrakt:The authors aim to interpret human and AI interactions from the decision perspective. The authors decompose the interaction analysis into the following main components in the context of interactions: Individual behavior patterns, interaction relationships, and comprehensive analysis. The authors interpret intertemporal decisions from a physical perspective and employ cross-discipline concepts and methodologies to extract the behavior characteristics of players in the empirical case study. About the individual behavior patterns, the authors find that human players prefer short-term periods to AI in decision-making. The interaction relationship analysis reveals a dynamic relationship between possible short-term co-movement and nearly counter-movement in the long run. The authors apply principal component analysis to descriptive indicators and discover a regular decision hierarchy. The main behavior pattern of players in the game of Go is switching between careful and daring behaviors. The differences in the decision hierarchies imply a discrepancy of patience between humans and AI. [ABSTRACT FROM AUTHOR]
ISSN:10096124
DOI:10.1007/s11424-024-1450-y