In multi-agent system design, a crucial aspect is to ensure robustness,
...
We study a class of reinforcement learning (RL) tasks where the objectiv...
Anomaly detection is essential in many application domains, such as cybe...
This paper provides the first comprehensive evaluation and analysis of m...
Learning linear temporal logic (LTL) formulas from examples labeled as
p...
Angluin's L* algorithm learns the minimal (complete) deterministic finit...
We consider the problem of explaining the temporal behavior of black-box...
Virtually all verification and synthesis techniques assume that the form...
While most of the current synthesis algorithms only focus on
correctness...
Formal verification has emerged as a powerful approach to ensure the saf...
It is widely accepted that every system should be robust in that "small"...
Apple recently revealed its deep perceptual hashing system NeuralHash to...
Linear temporal logic (LTL) is a specification language for finite seque...
Temporal logic inference is the process of extracting formal description...
We address the problem of inferring descriptions of system behavior usin...
While most approaches in formal methods address system correctness, ensu...
Parameterized synthesis offers a solution to the problem of constructing...
This paper presents a property-directed approach to verifying recurrent
...
We propose a novel approach to understanding the decision making of comp...
This paper presents LEXR, a framework for explaining the decision making...
We address the problem of learning human-interpretable descriptions of a...
Infinite-duration games with disturbances extend the classical framework...
Linear Temporal Logic (LTL) is the standard specification language for
r...
Incorporating high-level knowledge is an effective way to expedite
reinf...
We propose a machine learning framework to synthesize reactive controlle...
Linear Temporal Logic (LTL) is the standard specification language for
r...
Runtime verification is commonly used to detect and, if possible, react ...
We present two novel algorithms for learning formulas in Linear Temporal...
We design learning algorithms for synthesizing invariants using Horn
imp...
We propose a framework for synthesizing inductive invariants for incompl...
Recently, Dallal, Neider, and Tabuada studied a generalization of the
cl...