25 citations · 115 across the 59 of their papers we have counts for
10 papers · 1 filter
Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts
Sheryl Paul, Jyotirmoy V. Deshmukh
Reinforcement learning (RL) has been successfully applied to solve the problem of finding obstacle-free paths for autonomous agents operating in stochastic and uncertain environmen…
Non-Markovian Reinforcement Learning using Fractional Dynamics
Gaurav Gupta, Chenzhong Yin, Jyotirmoy V. Deshmukh +1
Reinforcement learning (RL) is a technique to learn the control policy for an agent that interacts with a stochastic environment. In any given state, the agent takes some action, a…
Mining Interpretable Spatio-temporal Logic Properties for Spatially Distributed Systems
Sara Mohammadinejad, Jyotirmy V. Deshmukh, Laura Nenzi
The Internet-of-Things, complex sensor networks, multi-agent cyber-physical systems are all examples of spatially distributed systems that continuously evolve in time. Such systems…
DiffRNN: Differential Verification of Recurrent Neural Networks
Sara Mohammadinejad, Brandon Paulsen, Chao Wang +1
Recurrent neural networks (RNNs) such as Long Short Term Memory (LSTM) networks have become popular in a variety of applications such as image processing, data classification, spee…
Mining Environment Assumptions for Cyber-Physical System Models
Sara Mohammadinejad, Jyotirmoy V. Deshmukh, Aniruddh G. Puranic
Many complex cyber-physical systems can be modeled as heterogeneous components interacting with each other in real-time. We assume that the correctness of each component can be spe…
Automatic Testing With Reusable Adversarial Agents
Xin Qin, Nikos Aréchiga, Andrew Best +1
Autonomous systems such as self-driving cars and general-purpose robots are safety-critical systems that operate in highly uncertain and dynamic environments. We propose an interac…
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning
Kolby Nottingham, Anand Balakrishnan, Jyotirmoy Deshmukh +1
It is notoriously difficult to control the behavior of reinforcement learning agents. Agents often learn to exploit the environment or reward signal and need to be retrained multip…
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques
Sara Mohammadinejad, Jyotirmoy V. Deshmukh, Aniruddh G. Puranic +2
Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. Designers often look for tools to…
Time Series Learning using Monotonic Logical Properties
Marcell Vazquez-Chanlatte, Shromona Ghosh, Jyotirmoy V. Deshmukh +2
Cyber-physical systems of today are generating large volumes of time-series data. As manual inspection of such data is not tractable, the need for learning methods to help discover…
Logic-based Clustering and Learning for Time-Series Data
Marcell Vazquez-Chanlatte, Jyotirmoy V. Deshmukh, Xiaoqing Jin +1
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 d…