AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes to abstract the planning task for abstract solutions that are then repaired into concrete plans for the input task, and showcases domains in which drawing an abstract plan and repairing it is significantly faster than solving the original problem.
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.
Guiding the CEGAR process by analysing multiple flaws of an abstract plan trace and selecting the best refinement can improve its performance as a tool to generate admissible heuristics in planning.
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.
Current open-weight models cannot guarantee satisfaction of the test constraints required for reliable automated model repair, and this paper evaluates the ability of recent open-weight large language models to perform this repair task using an LLM-only approach.
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...
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.
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