Personality-Driven Decision-Making in LLM-Based Autonomous Agents
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913770155016192 |
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| author | Newsham, Lewis Prince, Daniel |
| author_facet | Newsham, Lewis Prince, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00727 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personality-Driven Decision-Making in LLM-Based Autonomous Agents Newsham, Lewis Prince, Daniel Artificial Intelligence Multiagent Systems I.2.11; I.2.0 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. |
| title | Personality-Driven Decision-Making in LLM-Based Autonomous Agents |
| topic | Artificial Intelligence Multiagent Systems I.2.11; I.2.0 |
| url | https://arxiv.org/abs/2504.00727 |