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Found 8 papers, 0 papers with code
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems.
Fine-Tuning Language Models Using Formal Methods Feedback
Although pre-trained language models encode generic knowledge beneficial for planning and control, they may fail to generate appropriate control policies for domain-specific tasks.
Safe Reinforcement Learning via Shielding under Partial Observability
Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment.
Byzantine-Resilient Distributed Hypothesis Testing With Time-Varying Network Topology
We study the problem of distributed hypothesis testing over a network of mobile agents with limited communication and sensing ranges to infer the true hypothesis collaboratively.
Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints
Recurrent neural networks (RNNs) have emerged as an effective representation of control policies in sequential decision-making problems.
Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks
The particular problem is to determine strategies that provably adhere to (probabilistic) temporal logic constraints.
Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes
The efficient verification of this MC gives quantitative insights into the quality of the inferred human strategy by proving or disproving given system specifications.
Quantifying homologous proteins and proteoforms
Many proteoforms - arising from alternative splicing, post-translational modifications (PTMs), or paralogous genes - have distinct biological functions, such as histone PTM proteoforms.
