AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work introduces a formally verified transformation of Signal Temporal Logic (STL) formulas into a simplified form to ease their encoding as constraints in optimal control problems and identifies a minimal set of expressive formula patterns.
A logical dependency tracking framework that propagates uncertainty through the STL structure and captures the causal contribution of reachable sets to satisfaction and extract in milliseconds a compact Disjunctive Normal Form of sufficient constraints, avoiding combinatorial enumeration is introduced.
This work introduces a semantic-preserving PDDL-to-Lean conversion, and uses an LLM to generate both the generalized plan and the formal proof that it solves every instance satisfying the domain constraints, and evaluates this approach on 13 commonly used benchmark domains.
This paper proposes and formalizes two new minimization algorithms that guarantee subset-minimal reasons and ensures cardinality-minimal reasons in the AMOSUM constraint and demonstrates that extending the solver wasp with these minimization strategies leads to substantial performance improvements.
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.
This work presents a deductive verification framework based on a weighted assertion language and an intermediate verification language, whose weight domains are ordered structures with implication and coimplication, which let verification conditions express lower- and upper-bound obligations internally.
Combinatorial search—finding solutions that meet constraints within an exponentially large space of candidates—underpins problems from hardware verification to scheduling and combinatorial design. The most successful approach in practice is Propositional Satisfiability (SAT) solving, but modelling a problem for a SAT s...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.