5 citations · 24 across the 16 of their papers we have counts for
18 papers
What is Formal Verification without Specifications? A Survey on mining LTL Specifications
Daniel Neider, Rajarshi Roy
Virtually all verification techniques using formal methods rely on the availability of a formal specification, which describes the design requirements precisely. However, formulati…
The Complexity of Learning Temporal Properties
Benjamin Bordais, Daniel Neider, Rajarshi Roy
We consider the problem of learning temporal logic formulas from examples of system behavior. Learning temporal properties has crystallized as an effective mean to explain complex…
Learning Branching-Time Properties in CTL and ATL via Constraint Solving
Benjamin Bordais, Daniel Neider, Rajarshi Roy
We address the problem of learning temporal properties from the branching-time behavior of systems. Existing research in this field has mostly focused on learning linear temporal p…
Learning Temporal Properties is NP-hard
Benjamin Bordais, Daniel Neider, Rajarshi Roy
We investigate the complexity of LTL learning, which consists in deciding given a finite set of positive ultimately periodic words, a finite set of negative ultimately periodic wor…
Synthesizing Efficiently Monitorable Formulas in Metric Temporal Logic
Ritam Raha, Rajarshi Roy, Nathanael Fijalkow +2
In runtime verification, manually formalizing a specification for monitoring system executions is a tedious and error-prone process. To address this issue, we consider the problem…
Inferring Properties in Computation Tree Logic
Rajarshi Roy, Daniel Neider
We consider the problem of automatically inferring specifications in the branching-time logic, Computation Tree Logic (CTL), from a given system. Designing functional and usable sp…
GraPhSyM: Graph Physical Synthesis Model
Ahmed Agiza, Rajarshi Roy, Teodor Dumitru Ene +3
In this work, we introduce GraPhSyM, a Graph Attention Network (GATv2) model for fast and accurate estimation of post-physical synthesis circuit delay and area metrics from pre-phy…
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
Yash Paliwal, Rajarshi Roy, Jean-Raphaël Gaglione +5
We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals. In this setting, a common approach is to represen…
Analyzing Robustness of Angluin's L Algorithm in Presence of Noise
Lina Ye, Igor Khmelnitsky, Serge Haddad +5
Angluin's L algorithm learns the minimal deterministic finite automaton (DFA) of a regular language using membership and equivalence queries. Its probabilistic approximatively…
Learning Temporal Logic Properties: an Overview of Two Recent Methods
Jean-Raphaël Gaglione, Rajarshi Roy, Nasim Baharisangari +3
Learning linear temporal logic (LTL) formulas from examples labeled as positive or negative has found applications in inferring descriptions of system behavior. We summarize two me…
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise
Igor Khmelnitsky, Serge Haddad, Lina Ye +5
Angluin's L* algorithm learns the minimal (complete) deterministic finite automaton (DFA) of a regular language using membership and equivalence queries. Its probabilistic approxim…
Learning Interpretable Temporal Properties from Positive Examples Only
Rajarshi Roy, Jean-Raphaël Gaglione, Nasim Baharisangari +3
We consider the problem of explaining the temporal behavior of black-box systems using human-interpretable models. To this end, based on recent research trends, we rely on the fund…
Specification sketching for Linear Temporal Logic
Simon Lutz, Daniel Neider, Rajarshi Roy
Virtually all verification and synthesis techniques assume that the formal specifications are readily available, functionally correct, and fully match the engineer's understanding…
Robust Computation Tree Logic
Satya Prakash Nayak, Daniel Neider, Rajarshi Roy +1
It is widely accepted that every system should be robust in that ``small'' violations of environment assumptions should lead to ``small'' violations of system guarantees, but it is…
Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic
Ritam Raha, Rajarshi Roy, Nathanaël Fijalkow +1
Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and m…
Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach
Jean-Raphaël Gaglione, Daniel Neider, Rajarshi Roy +2
We address the problem of inferring descriptions of system behavior using Linear Temporal Logic (LTL) from a finite set of positive and negative examples. Most of the existing appr…
Property-Directed Verification of Recurrent Neural Networks
Igor Khmelnitsky, Daniel Neider, Rajarshi Roy +6
This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a surrogate model from a g…
Learning Interpretable Models in the Property Specification Language
Rajarshi Roy, Dana Fisman, Daniel Neider
We address the problem of learning human-interpretable descriptions of a complex system from a finite set of positive and negative examples of its behavior. In contrast to most of…