Author

Siddhartha Srivastava

Arizona State University - Cited by 2,308 - Artificial Intelligence - Automated Planning - Robotics - Task and Motion Planning - AI Assessment

Biography

Dr.  Siddhartha Srivastava is currently working in Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, USA. He has published numerous research papers and articles in reputed journals and has various other achievements in the related studies. He has extended his valuable service towards the scientific community with his extensive research work. 
Title
Cited by
Year
Learning Generalized Reactive Policies using Deep Neural Networks
E Groshev, A Tamar, M Goldstein, S Srivastava, P AbbeelICAPS, 2018201
104
2018
Hierarchical Expertise Level Modeling for User-Specific Contrastive Explanations
S Sreedharan, S Srivastava, S KambhampatiIJCAI, 201875201
75
2018
A unified framework for planning in adversarial and cooperative environments
A Kulkarni, S Srivastava, S KambhampatiProceedings of the AAAI Conference on Artificial Intelligence 33 (01), 2479-2487, 2019201
68
2019
Platform-independent benchmarks for task and motion planning
F Lagriffoul, NT Dantam, C Garrett, A Akbari, S Srivastava, LE KavrakiIEEE Robotics and Automation Letters 3 (4), 3765-3772, 2018201
68
2018
Guided Search for Task and Motion Plans Using Learned Heuristics
PA Rohan Chitnis, Dylan Hadfield-Menell, Abhishek Gupta, Siddharth ...ICRA, 201665201
65
2016
Guided search for task and motion plans using learned heuristics
R Chitnis, D Hadfield-Menell, A Gupta, S Srivastava, E Groshev, C Lin, ...2016 IEEE International Conference on Robotics and Automation (ICRA), 447-454, 2016201
65
2016
Markovian State and Action Abstractions for MDPs via Hierarchical MCTS.
A Bai, S Srivastava, SJ RussellIJCAI, 3029-3039, 2016201
52
2016
Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning.
R Karia, S SrivastavaProc. AAAI, 2021202
29
2021
Metaphysics of planning domain descriptions
S Srivastava, S Russell, A PintoProceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016201
28
2016
Asking the Right Questions: Learning Interpretable Action Models Through Query Answering
P Verma, SR Marpally, S SrivastavaProc. AAAI, 2021202
22
2021
Learn and link: learning critical regions for efficient planning
D Molina, K Kumar, S Srivastava2020 IEEE International Conference on Robotics and Automation (ICRA), 10605 …, 2020202
22
2020
Learn and Link: Learning Critical Regions for Efficient Planning
D Molina, K Kumar, S SrivastavaIEEE International Conference on Robotics and Automation, 2020202
22
2020
Anytime integrated task and motion policies for stochastic environments
N Shah, DK Vasudevan, K Kumar, P Kamojjhala, S Srivastava2020 IEEE International Conference on Robotics and Automation (ICRA), 9285-9291, 2020202
21
2020
TLdR: Policy summarization for factored SSP problems using temporal abstractions
S Sreedharan, S Srivastava, S KambhampatiProceedings of the International Conference on Automated Planning and …,
20
2020
Signaling friends and head-faking enemies simultaneously: Balancing goal obfuscation and goal legibility
A Kulkarni, S Srivastava, S KambhampatiarXiv preprint arXiv:1905.10672, 2019201
16
2019
Using state abstractions to compute personalized contrastive explanations for AI agent behavior
S Sreedharan, S Srivastava, S KambhampatiArtificial Intelligence 301, 103570, 2021202
15
2021
Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning.
N Shah, S SrivastavaProc. AAMAS, 2022202
10
2022
Differential Assessment of Black-Box AI Agents
R Nayyar, P Verma, S SrivastavaProc. AAAI, 2022202
6
2022
JEDAI: A System for Skill-Aligned Explainable Robot Planning
N Shah, P Verma, T Angle, S SrivastavaProc. AAMAS, 2022202
6
2022
Unifying Principles and Metrics for Safe and Assistive AI
S SrivastavaProc. AAAI, 2021202
6
2021

Referred From: https://scholar.google.com/citations?hl=en&user=H3ZnCqYAAAAJ

Cited by
Citations 2308
h-index 22
i10-index 32
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