AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It is shown a proof-of-the-concept planner can sometimes solve the bounded proper planning problem by using a domain-independent heuristic that guides search over sequences of actions.
It is shown a proof-of-the-concept lifted planner can sometimes solve the BPP problem by using a domain-independent heuristic that guides search for a plan.
This paper introduces Conditional TPOs (cTPOs), which extend TPOs with richer relative-timing constraints and conditional event activations based on environmental conditions and proves that this decomposition is complete and preserves plan optimality while improving the interpretability of complex tasks.
It is argued that domain abstractions offer a framework that lends itself much better to simple numeric planning, with abstract state spaces that are computed incrementally using counterexample-guided abstraction refinement (CEGAR), avoiding the exhaustive exploration of PDBs.
Improvements show that an explicit graph world model harness can substantially improve the reliability and efficiency of long-horizon embodied planning across compact and frontier hosted LLM capabilities.
A framework to exploit task-level contracts, expressed as assumptions and guarantees at the beginning and end of tasks, to automatically synthesize plans that are guaranteed to be correct on any system satisfying the contracts is proposed.
The results show that this form of regression can dramatically generalize partial states across multiple domains, reducing the number of variables considered for execution monitoring by up to 70%, and demonstrate that the resulting execution monitor is robust enough to recover frequently in an environment with unexpect...
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