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arXiv · 2504.00727

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

Lewis Newsham·Daniel Prince
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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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Lewis Newsham, Daniel Prince. 2025-04-01. Personality-Driven Decision-Making in LLM-Based Autonomous Agents. https://arxiv.org/abs/2504.00727

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