Beyond Intelligence Ownership: Intelligent and Autonomous Acquisition, Provisioning, and Maintenance of Desired Resources
Abstract
Artificial intelligence research has primarily focused on developing more capable models, agents, and intelligent systems. However, users may not ultimately desire intelligence as an object in itself. Instead, they may desire the resources, capabilities, services, and conditions that intelligence can help them obtain and maintain. This paper introduces the concept of Intelligent Possession (IP), a goal-oriented paradigm in which an intelligent system autonomously identifies, acquires, provisions, coordinates, and maintains access to resources required to satisfy a user's needs. Under this paradigm, the intelligence mechanism becomes an enabling infrastructure rather than the primary object of user value. A user seeking transportation, for example, may not need to own a vehicle, operate an autonomous-driving model, or manage a transportation agent. The desired outcome is reliable mobility. Similarly, a user may require computational capability without owning a particular model or computing device, or may need a continuously available service without managing its underlying infrastructure.
This paper distinguishes Intelligent Possession from conventional AI assistance, autonomous task execution, resource provisioning, ownership, and service-based access. It develops a conceptual framework comprising need representation, resource discovery, acquisition and provisioning, orchestration, maintenance, adaptation, and termination. A formal model represents the relationship between user needs, resource configurations, access arrangements, intelligent coordination, and lifecycle costs. The framework considers physical ownership, temporary access, shared provisioning, delegated control, and hybrid arrangements as alternative means of satisfying a desired state. It further identifies research questions concerning preference inference, autonomous acquisition, cost-aware resource selection, reliability, user control, security, and the allocation of responsibility. The paper proposes an evaluation agenda comparing intelligence-centered and outcome-centered systems across satisfaction, total cost, availability, autonomy, resource utilization, and failure recovery. As a conceptual contribution, the framework provides a basis for investigating AI systems designed around what users need to have available rather than which intelligence systems they need to operate.
Related
- Beyond Individual Large Language Models: A General Framework for Intelligent and Autonomous Provisioning of AI Capabilities
- Artificial Intelligence Powered End-to-End Agricultural Machinery: Intelligent Transformation of Design and Maintenance Systems
- Data-driven joint optimization of maintenance and spare parts provisioning: A distributionally robust approach
- Intelligent P-cycle provisioning in elastic optical networks via energy-aware deep reinforcement learning
- ZAC-Specialization: Collective Capability Provisioning Through Hardware–Role Co-Design in Heterogeneous Intelligent Agent Populations
BibTeX
@misc{zhang2026beyond,
title = {{Beyond Intelligence Ownership: Intelligent and Autonomous Acquisition, Provisioning, and Maintenance of Desired Resources}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23591761},
url = {https://zenodo.org/records/23591761},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond Intelligence Ownership: Intelligent and Autonomous Acquisition, Provisioning, and Maintenance of Desired Resources
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/23
PB - Zenodo
DO - 10.5281/zenodo.23591761
UR - https://zenodo.org/records/23591761
AB - Artificial intelligence research has primarily focused on developing more capable models, agents, and intelligent systems. However, users may not ultimately desire intelligence as an object in itself. Instead, they may desire the resources, capabilities, services, and conditions that intelligence can help them obtain and maintain. This paper introduces the concept of Intelligent Possession (IP), a goal-oriented paradigm in which an intelligent system autonomously identifies, acquires, provisions, coordinates, and maintains access to resources required to satisfy a user's needs. Under this paradigm, the intelligence mechanism becomes an enabling infrastructure rather than the primary object of user value. A user seeking transportation, for example, may not need to own a vehicle, operate an autonomous-driving model, or manage a transportation agent. The desired outcome is reliable mobility. Similarly, a user may require computational capability without owning a particular model or computing device, or may need a continuously available service without managing its underlying infrastructure. This paper distinguishes Intelligent Possession from conventional AI assistance, autonomous task execution, resource provisioning, ownership, and service-based access. It develops a conceptual framework comprising need representation, resource discovery, acquisition and provisioning, orchestration, maintenance, adaptation, and termination. A formal model represents the relationship between user needs, resource configurations, access arrangements, intelligent coordination, and lifecycle costs. The framework considers physical ownership, temporary access, shared provisioning, delegated control, and hybrid arrangements as alternative means of satisfying a desired state. It further identifies research questions concerning preference inference, autonomous acquisition, cost-aware resource selection, reliability, user control, security, and the allocation of responsibility. The paper proposes an evaluation agenda comparing intelligence-centered and outcome-centered systems across satisfaction, total cost, availability, autonomy, resource utilization, and failure recovery. As a conceptual contribution, the framework provides a basis for investigating AI systems designed around what users need to have available rather than which intelligence systems they need to operate.
ER -