Personality-Driven Decision-Making in LLM-Based Autonomous Agents

• URL: http://arxiv.org/abs/2504.00727v1
• Date: Tue, 01 Apr 2025 12:36:28 GMT
• Title: Personality-Driven Decision-Making in LLM-Based Autonomous Agents
• Authors: Lewis Newsham, Daniel Prince,
• Abstract summary: This study presents a novel method for measuring and evaluating how induced personality traits affect task selection processes.<n>Our results reveal distinct task-selection patterns aligned with induced OCEAN attributes, underscoring the feasibility of designing highly plausible Deceptive Agents for proactive cyber defense strategies.
• Score: 0.276240219662896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The embedding of Large Language Models (LLMs) into autonomous agents is a rapidly developing field which enables dynamic, configurable behaviours without the need for extensive domain-specific training. In our previous work, we introduced SANDMAN, a Deceptive Agent architecture leveraging the Five-Factor OCEAN personality model, demonstrating that personality induction significantly influences agent task planning. Building on these findings, this study presents a novel method for measuring and evaluating how induced personality traits affect task selection processes - specifically planning, scheduling, and decision-making - in LLM-based agents. Our results reveal distinct task-selection patterns aligned with induced OCEAN attributes, underscoring the feasibility of designing highly plausible Deceptive Agents for proactive cyber defense strategies.

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