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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15146 |
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| _version_ | 1866915252274200576 |
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| author | Zhou, Wei Borjigin, Ailiya He, Cong |
| author_facet | Zhou, Wei Borjigin, Ailiya He, Cong |
| contents | Modern digital ecosystems feature complex, dynamic interactions among autonomous entities across diverse domains. Traditional models often separate agents and objects, lacking a unified foundation to capture their interactive behaviors. This paper introduces the Behavioral Universe Network (BUN), a theoretical framework grounded in the Agent-Interaction-Behavior (AIB) formalism. BUN treats subjects (active agents), objects (resources), and behaviors (operations) as first-class entities, all governed by a shared Behavioral Information Base (BIB). We detail the AIB core concepts and demonstrate how BUN leverages information-driven triggers, semantic enrichment, and adaptive rules to coordinate multi-agent systems. We highlight key benefits: enhanced behavior analysis, strong adaptability, and cross-domain interoperability. We conclude by positioning BUN as a promising foundation for next-generation digital governance and intelligent applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15146 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Behavioral Universe Network (BUN): A Behavioral Information-Based Framework for Complex Systems Zhou, Wei Borjigin, Ailiya He, Cong Artificial Intelligence Modern digital ecosystems feature complex, dynamic interactions among autonomous entities across diverse domains. Traditional models often separate agents and objects, lacking a unified foundation to capture their interactive behaviors. This paper introduces the Behavioral Universe Network (BUN), a theoretical framework grounded in the Agent-Interaction-Behavior (AIB) formalism. BUN treats subjects (active agents), objects (resources), and behaviors (operations) as first-class entities, all governed by a shared Behavioral Information Base (BIB). We detail the AIB core concepts and demonstrate how BUN leverages information-driven triggers, semantic enrichment, and adaptive rules to coordinate multi-agent systems. We highlight key benefits: enhanced behavior analysis, strong adaptability, and cross-domain interoperability. We conclude by positioning BUN as a promising foundation for next-generation digital governance and intelligent applications. |
| title | Behavioral Universe Network (BUN): A Behavioral Information-Based Framework for Complex Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.15146 |