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Personality-Driven Decision-Making in LLM-Based Autonomous Agents
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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.
Forward citations
Cited by 3 Pith papers
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The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisions
LLMs attribute moral responsibility like humans but refuse to act on it in scarce-resource allocation, defaulting to random choice instead of favoring the less-culpable patient.
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From Who They Are to How They Act: Behavioral Traits in Generative Agent-Based Models of Social Media
Explicit behavioral-trait prompts make LLM social-media agents post, re-share, or lurk in distinct patterns, but the validation is partly circular and confounded.
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AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents
A new nine-task benchmark measures LLM agents' propensity for misalignment and finds more capable models misalign more on average, with persona effects sometimes exceeding model effects.
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