Summary of On-the-fly Synthesis For Ltl Over Finite Traces: An Efficient Approach That Counts, by Shengping Xiao et al.
On-the-fly Synthesis for LTL over Finite Traces: An Efficient Approach that Counts
by Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Y. Vardi
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an efficient framework for synthesizing Linear Temporal Logic (LTL) formulas over finite traces, leveraging deterministic automata construction. The proposed method converts LTLf into Transition-based DFA (TDFA) to enable parallelized synthesis, which addresses the inefficiency of existing approaches. This is achieved by developing an algorithm that traverses the state space using a global forward and local backward method, detecting strongly connected components. Two optimization techniques, model-guided synthesis and state entailment, are introduced to enhance practical efficiency. Experimental results demonstrate superior performance on tested benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to quickly create formulas for understanding and predicting things that happen over time. It’s like a recipe for building a special kind of machine that can recognize patterns in data. The usual way of doing this is very slow because it involves creating a huge machine first, which takes a lot of effort. This new approach lets you build the machine piece by piece as you go along, making it much faster and more practical. |
Keywords
» Artificial intelligence » Optimization
