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ORCIDSarath Sreedharan
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- affiliation: Colorado State University, Fort Collins, CO, USA
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2020 – today
- 2025
[j6]Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati:
Explain it as simple as possible, but no simpler - Explanation via model simplification for addressing inferential gap. Artif. Intell. 340: 104279 (2025)- 2024
- [j5]Sarath Sreedharan
, Tathagata Chakraborti, Christian Muise, Subbarao Kambhampati:
Planning with mental models - Balancing explanations and explicability. Artif. Intell. 335: 104181 (2024) - [c48]Malek Mechergui, Sarath Sreedharan:
Goal Alignment: Re-analyzing Value Alignment Problems Using Human-Aware AI. AAAI 2024: 10110-10118 - [c47]Turgay Caglar, Sirine Belhaj, Tathagata Chakraborti, Michael Katz, Sarath Sreedharan:
Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning. AAAI 2024: 20061-20069 - [c46]Dylan Pallickara, Sarath Sreedharan:
A Wireframe-Based Approach for Classifying and Acquiring Proficiency in the American Sign Language (Student Abstract). AAAI 2024: 23606-23607 - [c45]Turgay Caglar, Sarath Sreedharan:
HELP! Providing Proactive Support in the Presence of Knowledge Asymmetry. AAMAS 2024: 234-243 - [c44]Malek Mechergui, Sarath Sreedharan:
Expectation Alignment: Handling Reward Misspecification in the Presence of Expectation Mismatch. NeurIPS 2024 - [i37]Sarath Sreedharan, Malek Mechergui:
Handling Reward Misspecification in the Presence of Expectation Mismatch. CoRR abs/2404.08791 (2024) - [i36]Kelsey Sikes, Sarah Keren, Sarath Sreedharan:
Reducing Human-Robot Goal State Divergence with Environment Design. CoRR abs/2404.15184 (2024) - [i35]Silvia Tulli, Stylianos Loukas Vasileiou, Sarath Sreedharan:
Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI. CoRR abs/2405.07773 (2024) - [i34]Sarath Sreedharan, Anagha Kulkarni, Subbarao Kambhampati:
Explainable Human-AI Interaction: A Planning Perspective. CoRR abs/2405.15804 (2024) - 2023
- [j4]Sarath Sreedharan:
Human-aware AI - A foundational framework for human-AI interaction. AI Mag. 44(4): 460-466 (2023) - [c43]Sarath Sreedharan:
Human-Aware AI - A Foundational Framework for Human-AI Interaction. AAAI 2023: 15455 - [c42]Brittany Cates, Anagha Kulkarni, Sarath Sreedharan:
Planning for Attacker Entrapment in Adversarial Settings. ICAPS 2023: 86-94 - [c41]Sarath Sreedharan, Christian Muise, Subbarao Kambhampati:
Generalizing Action Justification and Causal Links to Policies. ICAPS 2023: 417-426 - [c40]Malek Mechergui, Sarath Sreedharan:
Goal Alignment: Re-analyzing Value Alignment Problems Using Human-Aware AI. AAMAS 2023: 2331-2333 - [c39]Zahra Zahedi, Mudit Verma, Sarath Sreedharan, Subbarao Kambhampati:
Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction. HRI 2023: 281-289 - [c38]Manas Gaur, Efthymia Tsamoura, Sarath Sreedharan, Sudip Mittal:
KiL 2023 : 3rd International Workshop on Knowledge-infused Learning. KDD 2023: 5857-5858 - [c37]Lin Guan, Karthik Valmeekam, Sarath Sreedharan, Subbarao Kambhampati:
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning. NeurIPS 2023 - [c36]Sarath Sreedharan, Michael Katz:
Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates. NeurIPS 2023 - [c35]Karthik Valmeekam, Matthew Marquez, Alberto Olmo Hernandez, Sarath Sreedharan, Subbarao Kambhampati:
PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change. NeurIPS 2023 - [c34]Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, Subbarao Kambhampati:
On the Planning Abilities of Large Language Models - A Critical Investigation. NeurIPS 2023 - [c33]Indrajit Ray, Sarath Sreedharan, Rakesh Podder, Shadaab Kawnain Bashir, Indrakshi Ray:
Explainable AI for Prioritizing and Deploying Defenses for Cyber-Physical System Resiliency. TPS-ISA 2023: 184-192 - [i33]Zahra Zahedi, Sarath Sreedharan, Subbarao Kambhampati:
A Mental Model Based Theory of Trust. CoRR abs/2301.12569 (2023) - [i32]Malek Mechergui, Sarath Sreedharan:
Goal Alignment: A Human-Aware Account of Value Alignment Problem. CoRR abs/2302.00813 (2023) - [i31]Karthik Valmeekam, Sarath Sreedharan, Matthew Marquez, Alberto Olmo Hernandez, Subbarao Kambhampati:
On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark). CoRR abs/2302.06706 (2023) - [i30]Brittany Cates, Anagha Kulkarni, Sarath Sreedharan:
Planning for Attacker Entrapment in Adversarial Settings. CoRR abs/2303.00822 (2023) - [i29]Lin Guan, Karthik Valmeekam, Sarath Sreedharan, Subbarao Kambhampati:
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning. CoRR abs/2305.14909 (2023) - [i28]Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, Subbarao Kambhampati:
On the Planning Abilities of Large Language Models - A Critical Investigation. CoRR abs/2305.15771 (2023) - [i27]Tathagata Chakraborti, Jungkoo Kang, Christian Muise, Sarath Sreedharan, Michael E. Walker, Daniel Szafir, Tom Williams:
TOBY: A Tool for Exploring Data in Academic Survey Papers. CoRR abs/2306.10051 (2023) - [i26]Turgay Caglar, Sirine Belhaj, Tathagata Chakraborti, Michael Katz, Sarath Sreedharan:
Towards More Likely Models for AI Planning. CoRR abs/2311.13720 (2023) - 2022
- [c32]Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma, Yantian Zha, Lin Guan:
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems. AAAI 2022: 12262-12267 - [c31]Karthik Valmeekam, Sarath Sreedharan, Sailik Sengupta, Subbarao Kambhampati:
RADAR-X: An Interactive Mixed Initiative Planning Interface Pairing Contrastive Explanations and Revised Plan Suggestions. ICAPS 2022: 508-517 - [c30]Zahra Zahedi, Sarath Sreedharan, Mudit Verma, Subbarao Kambhampati:
Modeling the Interplay between Human Trust and Monitoring. HRI 2022: 1119-1123 - [c29]Sarath Sreedharan, Utkarsh Soni, Mudit Verma, Siddharth Srivastava, Subbarao Kambhampati:
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations. ICLR 2022 - [c28]Lin Guan, Sarath Sreedharan, Subbarao Kambhampati:
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity. ICML 2022: 7949-7967 - [c27]Sarath Sreedharan, Pascal Bercher, Subbarao Kambhampati:
On the Computational Complexity of Model Reconciliations. IJCAI 2022: 4657-4664 - [i25]Lin Guan, Sarath Sreedharan, Subbarao Kambhampati:
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity. CoRR abs/2202.02886 (2022) - [i24]Zahra Zahedi, Sarath Sreedharan, Subbarao Kambhampati:
A Mental-Model Centric Landscape of Human-AI Symbiosis. CoRR abs/2202.09447 (2022) - [i23]Karthik Valmeekam, Alberto Olmo Hernandez, Sarath Sreedharan, Subbarao Kambhampati:
Large Language Models Still Can't Plan (A Benchmark for LLMs on Planning and Reasoning about Change). CoRR abs/2206.10498 (2022) - [i22]Utkarsh Soni, Sarath Sreedharan, Mudit Verma, Lin Guan, Matthew Marquez, Subbarao Kambhampati:
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion. CoRR abs/2210.15096 (2022) - 2021
- [j3]Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati:
Foundations of explanations as model reconciliation. Artif. Intell. 301: 103558 (2021) - [j2]Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati:
Using state abstractions to compute personalized contrastive explanations for AI agent behavior. Artif. Intell. 301: 103570 (2021) - [c26]Karthik Valmeekam, Sarath Sreedharan, Sailik Sengupta, Subbarao Kambhampati:
RADAR-X: An Interactive Interface Pairing Contrastive Explanations with Revised Plan Suggestions. AAAI 2021: 16051-16053 - [c25]Sarath Sreedharan, Anagha Kulkarni, David E. Smith, Subbarao Kambhampati:
A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI Interaction. IJCAI 2021: 4602-4610 - [c24]Utkarsh Soni, Sarath Sreedharan, Subbarao Kambhampati:
Not all users are the same: Providing personalized explanations for sequential decision making problems. IROS 2021: 6240-6247 - [i21]Sarath Sreedharan, Anagha Kulkarni, David E. Smith, Subbarao Kambhampati:
A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI. CoRR abs/2104.10743 (2021) - [i20]Zahra Zahedi, Mudit Verma, Sarath Sreedharan, Subbarao Kambhampati:
Trust-Aware Planning: Modeling Trust Evolution in Longitudinal Human-Robot Interaction. CoRR abs/2105.01220 (2021) - [i19]Alberto Olmo Hernandez, Sarath Sreedharan, Subbarao Kambhampati:
GPT3-to-plan: Extracting plans from text using GPT-3. CoRR abs/2106.07131 (2021) - [i18]Utkarsh Soni, Sarath Sreedharan, Subbarao Kambhampati:
Not all users are the same: Providing personalized explanations for sequential decision making problems. CoRR abs/2106.12207 (2021) - [i17]Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma, Yantian Zha, Lin Guan:
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems. CoRR abs/2109.09904 (2021) - 2020
- [c23]Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Subbarao Kambhampati:
Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations. AAAI 2020: 2518-2526 - [c22]Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati:
TLdR: Policy Summarization for Factored SSP Problems Using Temporal Abstractions. ICAPS 2020: 272-280 - [c21]Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Yasaman Khazaeni, Subbarao Kambhampati:
- D3WA+ - A Case Study of XAIP in a Model Acquisition Task for Dialogue Planning. ICAPS 2020: 488-498 - [c20]Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati:
The Emerging Landscape of Explainable Automated Planning & Decision Making. IJCAI 2020: 4803-4811 - [c19]Anagha Kulkarni, Sarath Sreedharan, Sarah Keren, Tathagata Chakraborti, David E. Smith, Subbarao Kambhampati:
Designing Environments Conducive to Interpretable Robot Behavior. IROS 2020: 10982-10989 - [i16]Sarath Sreedharan, Utkarsh Soni, Mudit Verma, Siddharth Srivastava, Subbarao Kambhampati:
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Black Box Simulators. CoRR abs/2002.01080 (2020) - [i15]Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati:
The Emerging Landscape of Explainable AI Planning and Decision Making. CoRR abs/2002.11697 (2020) - [i14]Anagha Kulkarni, Sarath Sreedharan, Sarah Keren, Tathagata Chakraborti, David E. Smith, Subbarao Kambhampati:
Designing Environments Conducive to Interpretable Robot Behavior. CoRR abs/2007.00820 (2020) - [i13]Karthik Valmeekam, Sarath Sreedharan, Sailik Sengupta, Subbarao Kambhampati:
RADAR-X: An Interactive Interface Pairing Contrastive Explanations with Revised Plan Suggestions. CoRR abs/2011.09644 (2020) - [i12]Sarath Sreedharan, Tathagata Chakraborti, Yara Rizk, Yasaman Khazaeni:
Explainable Composition of Aggregated Assistants. CoRR abs/2011.10707 (2020) - [i11]Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti, David E. Smith, Subbarao Kambhampati:
A Bayesian Account of Measures of Interpretability in Human-AI Interaction. CoRR abs/2011.10920 (2020)
2010 – 2019
- 2019
- [c18]Tathagata Chakraborti, Anagha Kulkarni, Sarath Sreedharan, David E. Smith, Subbarao Kambhampati:
Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior. ICAPS 2019: 86-96 - [c17]Tathagata Chakraborti, Sarath Sreedharan, Sachin Grover, Subbarao Kambhampati:
Plan Explanations as Model Reconciliation. HRI 2019: 258-266 - [c16]Zahra Zahedi, Alberto Olmo Hernandez, Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati:
Towards Understanding User Preferences for Explanation Types in Model Reconciliation. HRI 2019: 648-649 - [c15]Sarath Sreedharan, Alberto Olmo Hernandez, Aditya Prasad Mishra, Subbarao Kambhampati:
Model-Free Model Reconciliation. IJCAI 2019: 587-594 - [c14]Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati:
Balancing Explicability and Explanations in Human-Aware Planning. IJCAI 2019: 1335-1343 - [c13]Sarath Sreedharan, Siddharth Srivastava, David E. Smith, Subbarao Kambhampati:
Why Can't You Do That HAL? Explaining Unsolvability of Planning Tasks. IJCAI 2019: 1422-1430 - [i10]Sarath Sreedharan, Alberto Olmo Hernandez, Aditya Prasad Mishra, Subbarao Kambhampati:
Model-Free Model Reconciliation. CoRR abs/1903.07198 (2019) - [i9]Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Subbarao Kambhampati:
Planning with Explanatory Actions: A Joint Approach to Plan Explicability and Explanations in Human-Aware Planning. CoRR abs/1903.07269 (2019) - [i8]Sarath Sreedharan, Siddharth Srivastava, David E. Smith, Subbarao Kambhampati:
Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice. CoRR abs/1903.08218 (2019) - 2018
- [c12]Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati:
Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation. ICAPS 2018: 518-526 - [c11]Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati:
Explicability versus Explanations in Human-Aware Planning. AAMAS 2018: 2180-2182 - [c10]Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati:
Hierarchical Expertise Level Modeling for User Specific Contrastive Explanations. IJCAI 2018: 4829-4836 - [c9]Tathagata Chakraborti, Sarath Sreedharan, Anagha Kulkarni, Subbarao Kambhampati:
Projection-Aware Task Planning and Execution for Human-in-the-Loop Operation of Robots in a Mixed-Reality Workspace. IROS 2018: 4476-4482 - [i7]Tathagata Chakraborti, Sarath Sreedharan, Sachin Grover, Subbarao Kambhampati:
Plan Explanations as Model Reconciliation - An Empirical Study. CoRR abs/1802.01013 (2018) - [i6]Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati:
Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations. CoRR abs/1802.06895 (2018) - [i5]Tathagata Chakraborti, Anagha Kulkarni, Sarath Sreedharan, David E. Smith, Subbarao Kambhampati:
Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior. CoRR abs/1811.09722 (2018) - 2017
- [j1]Tuan Nguyen, Sarath Sreedharan, Subbarao Kambhampati:
Robust planning with incomplete domain models. Artif. Intell. 245: 134-161 (2017) - [c8]Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati:
Balancing Explicability and Explanation in Human-Aware Planning. AAAI Fall Symposia 2017: 61-68 - [c7]Sailik Sengupta, Tathagata Chakraborti, Sarath Sreedharan, Satya Gautam Vadlamudi, Subbarao Kambhampati:
RADAR - A Proactive Decision Support System for Human-in-the-Loop Planning. AAAI Fall Symposia 2017: 269-276 - [c6]Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati:
Explanations as Model Reconciliation - A Multi-Agent Perspective. AAAI Fall Symposia 2017: 277-283 - [c5]Yu Zhang, Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti, Hankz Hankui Zhuo, Subbarao Kambhampati:
Plan explicability and predictability for robot task planning. ICRA 2017: 1313-1320 - [c4]Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang, Subbarao Kambhampati:
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy. IJCAI 2017: 156-163 - [i4]Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang, Subbarao Kambhampati:
Explanation Generation as Model Reconciliation in Multi-Model Planning. CoRR abs/1701.08317 (2017) - [i3]Tathagata Chakraborti, Sarath Sreedharan, Anagha Kulkarni, Subbarao Kambhampati:
Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots. CoRR abs/1703.08930 (2017) - [i2]Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati:
Balancing Explicability and Explanation in Human-Aware Planning. CoRR abs/1708.00543 (2017) - 2016
- [c3]Yu Zhang, Sarath Sreedharan, Subbarao Kambhampati:
A Formal Analysis of Required Cooperation in Multi-Agent Planning. ICAPS 2016: 335-344 - [c2]Tathagata Chakraborti, Sarath Sreedharan, Sailik Sengupta, T. K. Satish Kumar, Subbarao Kambhampati:
Compliant Conditions for Polynomial Time Approximation of Operator Counts. SOCS 2016: 123-124 - [i1]Tathagata Chakraborti, Sarath Sreedharan, Sailik Sengupta, T. K. Satish Kumar, Subbarao Kambhampati:
Compliant Conditions for Polynomial Time Approximation of Operator Counts. CoRR abs/1605.07989 (2016) - 2015
- [c1]Yu Zhang, Sarath Sreedharan, Subbarao Kambhampati:
Capability Models and Their Applications in Planning. AAMAS 2015: 1151-1159
Coauthor Index
[j6] [j5] [i34] [c41] [c39] [c37] [c35] [c34] [i33] [i31] [i29] [i28] [c32] [c31] [c30] [c29] [c28] [c27] [i25] [i24] [i23] [i22] [j3] [j2] [c26] [c25] [c24] [i21] [i20] [i19] [i18] [i17] [c23] [c22] [c21] [c20] [c19] [i16] [i15] [i14] [i13] [i11] [c18] [c17] [c16] [c15] [c14] [c13] [i10] [i9] [i8] [c12] [c11] [c10] [c9] [i7] [i6] [i5] [j1] [c8] [c7] [c6] [c5] [c4] [i4] [i3] [i2] [c3] [c2] [i1] [c1]
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