Article Instance
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{ "pk": 49886, "title": "The Odyssey of the Fittest: Can Agents Survive and Still Be Good?", "subtitle": null, "abstract": "As AI models grow in power and generality, understanding how agents learn and make decisions in complex environments is critical to promoting ethical behavior. This study introduces the Odyssey, a lightweight, adaptive text-based adventure game, providing a scalable framework for exploring AI ethics and safety. The Odyssey examines the ethical implications of implementing biological drives—specifically, self-preservation—into three different agents: a Bayesian agent optimized with NEAT, a Bayesian agent optimized with stochastic variational inference, and a GPT-4o agent. The agents select actions at each scenario to survive, adapting to increasingly challenging scenarios. Post-simulation analysis evaluates the ethical scores of the agent's decisions, uncovering the trade-offs it navigates to survive. Specifically, analysis finds that when danger increases, agents ethical behavior becomes unpredictable. Surprisingly, the GPT-4o agent outperformed the Bayesian models in both survival and ethical consistency, challenging assumptions about traditional probabilistic methods and raising questions about the source of LLMs' probabilistic reasoning.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "Biology; Philosophy; Emotion; Evolution; Bayesian modeling" } ], "section": "Papers with Poster Presentation", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/7s537158", "frozenauthors": [ { "first_name": "Dylan", "middle_name": "T", "last_name": "Waldner", "name_suffix": "", "institution": "The University of Texas at Austin", "department": "" }, { "first_name": "Risto", "middle_name": "", "last_name": "Miikkulainen", "name_suffix": "", "institution": "University of Texas", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2025-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/49886/galley/37848/download/" } ] }