arXiv:2605.28666v1 Announce Type: new Abstract: In modern industry, dynamic environments and the complexity of modular and reconfigurable resources require automated planning of process sequences. Capability-based planning approaches address this by automatically generating plans from semantic knowledge models that describe resource functions in a machine-interpretable form. Their practical use, however, remains limited: solver feedback, especially in the case of unsatisfiability, is difficult to interpret, and the knowledge models require adaptation as operational conditions change or request
The increasing complexity of industrial automation and the maturity of LLM technologies are converging, making sophisticated planning assistance both necessary and feasible.
This development could significantly enhance the flexibility and efficiency of automated planning in complex industrial environments, overcoming current practical limitations.
Automated planning systems will become more adaptable to dynamic conditions and easier to manage, reducing human intervention and improving operational resilience.
- · Industrial automation sector
- · Manufacturing companies
- · Logistics and supply chain management
- · AI software developers
- · Companies reliant on rigid, traditional planning systems
- · Manual process planners
Improved efficiency and resilience in reconfigurable manufacturing and complex industrial processes.
Accelerated adoption of highly modular and dynamic production systems across various industries.
Potential for new business models centered on highly adaptive, intelligent industrial services.
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Read at arXiv cs.AI