Learning regular sets from queries and counterexamples
Information and Computation
An introduction to computational learning theory
An introduction to computational learning theory
Exact learning Boolean functions via the monotone theory
Information and Computation
Predicate abstraction for software verification
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Construction of Abstract State Graphs with PVS
CAV '97 Proceedings of the 9th International Conference on Computer Aided Verification
Special issue on learning techniques for compositional reasoning
Formal Methods in System Design
Decision Procedures: An Algorithmic Point of View
Decision Procedures: An Algorithmic Point of View
Automated Assume-Guarantee Reasoning by Abstraction Refinement
CAV '08 Proceedings of the 20th international conference on Computer Aided Verification
Refining interface alphabets for compositional verification
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
Comparing learning algorithms in automated assume-guarantee reasoning
ISoLA'10 Proceedings of the 4th international conference on Leveraging applications of formal methods, verification, and validation - Volume Part I
Automata learning with automated alphabet abstraction refinement
VMCAI'11 Proceedings of the 12th international conference on Verification, model checking, and abstract interpretation
Automatically inferring quantified loop invariants by algorithmic learning from simple templates
APLAS'10 Proceedings of the 8th Asian conference on Programming languages and systems
Predicate generation for learning-based quantifier-free loop invariant inference
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
Deriving invariants by algorithmic learning, decision procedures, and predicate abstraction
VMCAI'10 Proceedings of the 11th international conference on Verification, Model Checking, and Abstract Interpretation
Automated assume-guarantee reasoning through implicit learning
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
Termination analysis with algorithmic learning
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
Termination analysis with algorithmic learning
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
BULL: a library for learning algorithms of boolean functions
TACAS'13 Proceedings of the 19th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Learning universally quantified invariants of linear data structures
CAV'13 Proceedings of the 25th international conference on Computer Aided Verification
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Classical learning algorithms for Boolean functions assume that unknown targets are Boolean functions over fixed variables. The assumption precludes scenarios where indefinitely many variables are needed. It also induces unnecessary queries when many variables are redundant. Based on a classical learning algorithm for Boolean functions, we develop two learning algorithms to infer Boolean functions over enlarging sets of ordered variables. We evaluate their performance in the learning-based loop invariant generation framework.