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Found 7 papers, 2 papers with code
Uncertainty-Aware Signal Temporal Logic Inference
In this paper, we first investigate the uncertainties associated with trajectories of a system and represent such uncertainties in the form of interval trajectories.
Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks
It is important that the obtained knowledge is human-interpretable and amenable to formal analysis.
Learning Interpretable Temporal Properties from Positive Examples Only
To learn meaningful models from positive examples only, we design algorithms that rely on conciseness and language minimality of models as regularizers.
Distributed Differentially Private Control Synthesis for Multi-Agent Systems with Metric Temporal Logic Specifications
In this paper, we propose a distributed differentially private receding horizon control (RHC) approach for multi-agent systems (MAS) with metric temporal logic (MTL) specifications.
Learning Temporal Logic Properties: an Overview of Two Recent Methods
Learning linear temporal logic (LTL) formulas from examples labeled as positive or negative has found applications in inferring descriptions of system behavior.
Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference
To tackle this problem, we propose data-driven methods to over-approximate the unknown dynamics and to infer the unknown specifications such that both set-membership models of the unknown dynamics and LTL formulas are guaranteed to include the ground truth model and specification/task.
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals.
