Author:
Thorsten Händler
Affiliation:
Ferdinand Porsche Mobile University of Applied Sciences (FERNFH), Austria
Keyword(s):
Taxonomy, Autonomous Agents, Multi-Agent Collaboration, Large Language Models (LLMs), AI System Classification, Alignment, Software Architecture, Architectural Viewpoints, Software-Design Rationale, Context Interaction, Artificial Intelligence, Domain-Ontology Diagram, Feature Diagram, Radar Chart.
Abstract:
Large language models (LLMs) have revolutionized the field of artificial intelligence, endowing it with sophisticated language understanding and generation capabilities. However, when faced with more complex and interconnected tasks that demand a profound and iterative thought process, LLMs reveal their inherent limitations. Autonomous LLM-powered multi-agent systems represent a strategic response to these challenges. While these architectures hold promising potential in amplifying AI capabilities, striking the right balance between different levels of autonomy and alignment remains the crucial challenge for their effective operation. This paper proposes a comprehensive multi-dimensional taxonomy, engineered to analyze how autonomous LLM-powered multi-agent systems balance the dynamic interplay between autonomy and alignment across various aspects inherent to architectural viewpoints such as goal-driven task management, agent composition, multi-agent collaboration, and context intera
ction. Our taxonomy aims to empower researchers, engineers, and AI practitioners to systematically analyze the architectural dynamics and balancing strategies employed by these increasingly prevalent AI systems. The exploratory taxonomic classification of selected representative LLM-powered multi-agent systems illustrates its practical utility and reveals potential for future research and development. An extended version of this paper is available on arXiv (Händler, 2023).
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