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Found 16 papers, 4 papers with code
Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning
Automata-conditioned reinforcement learning (RL) has given promising results for learning multi-task policies capable of performing temporally extended objectives given at runtime, done by pretraining and freezing automata embeddings prior to training the downstream policy.
SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs
In this paper, we explore the possibility of enhancing SD maps by incorporating information from road manuals using LLMs.
Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning is a powerful way to control an AI agent's behavior at runtime.
Diffusion-Based Failure Sampling for Cyber-Physical Systems
Validating safety-critical autonomous systems in high-dimensional domains such as robotics presents a significant challenge.
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language
Reports normally contain uncertainty about the exact details of the incidents which we represent through a Probabilistic Programming Language (PPL), Scenic.
Entropy-regularized Point-based Value Iteration
However, model-based planners may be brittle under these types of uncertainty because they rely on an exact model and tend to commit to a single optimal behavior.
$L^*LM$: Learning Automata from Examples using Natural Language Oracles
Due to the expressivity of natural language, we observe a significant improvement in the data efficiency of learning DFAs from expert demonstrations.
Learning Formal Specifications from Membership and Preference Queries
Active learning is a well-studied approach to learning formal specifications, such as automata.
Demonstration Informed Specification Search
This paper considers the problem of learning temporal task specifications, e. g. automata and temporal logic, from expert demonstrations.
Maximum Causal Entropy Specification Inference from Demonstrations
In many settings (e. g., robotics) demonstrations provide a natural way to specify tasks; however, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the tasks, such as rewards or policies, can be safely composed and/or do not explicitly capture history dependencies.
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques
Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data.
A Model Counter's Guide to Probabilistic Systems
In this paper, we systematize the modeling of probabilistic systems for the purpose of analyzing them with model counting techniques.
VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems
We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.
Time Series Learning using Monotonic Logical Properties
Cyber-physical systems of today are generating large volumes of time-series data.
Learning Task Specifications from Demonstrations
In this paper, we formulate the specification inference task as a maximum a posteriori (MAP) probability inference problem, apply the principle of maximum entropy to derive an analytic demonstration likelihood model and give an efficient approach to search for the most likely specification in a large candidate pool of specifications.
Logic-based Clustering and Learning for Time-Series Data
To effectively analyze and design cyberphysical systems (CPS), designers today have to combat the data deluge problem, i. e., the burden of processing intractably large amounts of data produced by complex models and experiments.
