A Cognitive Framework for Autonomous Agents: Toward Human-Inspired Design
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917219500294144 |
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| author | Guidi, Francesco Shan, Jingfeng Saeidi, Mehrdad Testi, Enrico Favarelli, Elia Giorgetti, Andrea Dardari, Davide Zanella, Alberto Pira, Giorgio Li Starita, Francesca Guerra, Anna |
| author_facet | Guidi, Francesco Shan, Jingfeng Saeidi, Mehrdad Testi, Enrico Favarelli, Elia Giorgetti, Andrea Dardari, Davide Zanella, Alberto Pira, Giorgio Li Starita, Francesca Guerra, Anna |
| contents | This work introduces a human-inspired reinforcement learning (RL) architecture that integrates Pavlovian and instrumental processes to enhance decision-making in autonomous systems. While existing engineering solutions rely almost exclusively on instrumental learning, neuroscience shows that humans use Pavlovian associations to leverage predictive cues to bias behavior before outcomes occur. We translate this dual-system mechanism into a cue-guided RL framework in which radio-frequency (RF) stimuli act as conditioned (Pavlovian) cues that modulate action selection. The proposed architecture combines Pavlovian values with instrumental policy optimization, improving navigation efficiency and cooperative behavior in unknown, partially observable environments. Simulation results demonstrate that cue-driven agents adapt faster, achieving superior performance compared to traditional instrumental-solo agents. This work highlights the potential of human learning principles to reshape digital agents intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16648 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Cognitive Framework for Autonomous Agents: Toward Human-Inspired Design Guidi, Francesco Shan, Jingfeng Saeidi, Mehrdad Testi, Enrico Favarelli, Elia Giorgetti, Andrea Dardari, Davide Zanella, Alberto Pira, Giorgio Li Starita, Francesca Guerra, Anna Systems and Control This work introduces a human-inspired reinforcement learning (RL) architecture that integrates Pavlovian and instrumental processes to enhance decision-making in autonomous systems. While existing engineering solutions rely almost exclusively on instrumental learning, neuroscience shows that humans use Pavlovian associations to leverage predictive cues to bias behavior before outcomes occur. We translate this dual-system mechanism into a cue-guided RL framework in which radio-frequency (RF) stimuli act as conditioned (Pavlovian) cues that modulate action selection. The proposed architecture combines Pavlovian values with instrumental policy optimization, improving navigation efficiency and cooperative behavior in unknown, partially observable environments. Simulation results demonstrate that cue-driven agents adapt faster, achieving superior performance compared to traditional instrumental-solo agents. This work highlights the potential of human learning principles to reshape digital agents intelligence. |
| title | A Cognitive Framework for Autonomous Agents: Toward Human-Inspired Design |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.16648 |