2018 Formal Methods in Computer Aided Design (FMCAD) 2018 # Learning Linear Temporal Properties

**Abstract:** We present two novel algorithms for learning formulas in Linear Temporal Logic (LTL) from examples. The first learning algorithm reduces the learning task to a series of satisfiability problems in propositional Boolean logic and produces a smallest LTL formula (in terms of the number of subformulas) that is consistent with the given data. Our second learning algorithm, on the other hand, combines the SAT-based learning algorithm with classical algorithms for learning decision trees. The result is a learning al…

Help me understand this report

Search citation statements

Paper Sections

Select...

3

1

1

Citation Types

0

105

0

Year Published

2019

2022

Publication Types

Select...

5

3

1

Relationship

1

8

Authors

Journals

(105 citation statements)

0

105

0

“…We do not distinguish these names, sometimes we use them interchangeably, and here we just use the term model learning or learning model instead. Similarly, the learning specification problems also have different names under research, typically are specification mining [38]- [43], specification inference [44], requirements mining [45], mining properties [46], learning logic formulae [47], learning specifications [48], [49], learning properties [50], [51], etc. Here we use the term learning specification (or specification learning) in analogy with learning model (or model learning).…”

confidence: 99%

“…We do not distinguish these names, sometimes we use them interchangeably, and here we just use the term model learning or learning model instead. Similarly, the learning specification problems also have different names under research, typically are specification mining [38]- [43], specification inference [44], requirements mining [45], mining properties [46], learning logic formulae [47], learning specifications [48], [49], learning properties [50], [51], etc. Here we use the term learning specification (or specification learning) in analogy with learning model (or model learning).…”

confidence: 99%

“…A ¬ψ,v ) if ψ is syntactically co-safe (resp. syntactically safe) 8 for k = 0 to K do 9 Initialize p ψ,v L (L, q k ) 10 for = L to 2, j = 0 to K do 11 For each k ∈ [0, K], calculate c j,k p ψ,v…”

confidence: 99%

“…Our approach of inferring GTL formulas from data is closely related to inferring temporal logic formulas from data. The work in [2,5,8] focus on inferring temporal logic formulas for classifying two sets of trajectories, while the work in [3,6,9,15] focus on identifying temporal logic formulas from system trajectories.…”

confidence: 99%

“…We show that for all the above representations the automatic signature synthesis problem can be viewed as an instance of the language learning from the informant problem. For DFA and MM representations, we rely on existing automata learning algorithms, whereas for PLTL, we propose a new algorithm, an extension of prior work [43]. For runtime monitoring of these signature representations in PHOENIX, we use standard algorithms [27].…”

confidence: 99%