Search Results for author: Marcell Vazquez-Chanlatte

Found 8 papers, 2 papers with code

Demonstration Informed Specification Search

1 code implementation20 Dec 2021 Marcell Vazquez-Chanlatte, Ameesh Shah, Gil Lederman, Sanjit A. Seshia

To address this deficit, we propose Demonstration Informed Specification Search (DISS): a family of algorithms parameterized by black box access to (i) a maximum entropy planner and (ii) an algorithm for identifying concepts, e. g., automata, from labeled examples.

Maximum Causal Entropy Specification Inference from Demonstrations

no code implementations26 Jul 2019 Marcell Vazquez-Chanlatte, Sanjit A. Seshia

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.

A Model Counter's Guide to Probabilistic Systems

no code implementations22 Mar 2019 Marcell Vazquez-Chanlatte, Markus N. Rabe, Sanjit A. Seshia

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

1 code implementation12 Feb 2019 Tommaso Dreossi, Daniel J. Fremont, Shromona Ghosh, Edward Kim, Hadi Ravanbakhsh, Marcell Vazquez-Chanlatte, Sanjit A. Seshia

We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.

Learning Task Specifications from Demonstrations

no code implementations NeurIPS 2018 Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K. Ho, Sanjit A. Seshia

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

no code implementations22 Dec 2016 Marcell Vazquez-Chanlatte, Jyotirmoy V. Deshmukh, Xiaoqing Jin, Sanjit A. Seshia

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.

General Classification Time Series

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