## About <img align="left" src="./static/sarath.jpg"> <br \> &nbsp; Contact:<br \> &nbsp;&nbsp;<span class="rwd-line">School of Computing, Informatics,</span><span class="rwd-line"> and Decision Systems Engineering (CIDSE)</span> <br \>&nbsp;&nbsp;Arizona State University<br \> &nbsp;&nbsp;USA<br \> &nbsp;&nbsp;Email: ssreedh3 [at] asu [dot] edu <br \> <br \> <br \> I am a Ph.D. student working under [Prof. Subbarao Kambhampati](http://rakaposhi.eas.asu.edu/) at Arizona State University. My research interests include challenges in planning with incomplete knowledge and human-aware planning. My recent work investigated algorithms for explicable planning and plan explanations, especially in settings where models diverge between the planner and the human in the loop. You can find a copy of my latest CV [here](./static/cv_current_sarath_2019.pdf). ## Research Interests - Human-aware planning for human-robot teams and decision support - Explicable planning, explanation generation for humans in the loop. - Model-Lite Planning - Representation and learning of incomplete models for plan generation, recognition and recommendation. ## Publications - Model-Free Model Reconciliation. S. Sreedharan, A. Olmo, A. Mishra and S. Kambhampati. IJCAI 2019. [\[pdf\]](https://yochan-lab.github.io/papers/files/papers/ModelFree_Explanations_IJCAI19.pdf) - Why Can't You Do That HAL? Explaining Unsolvability of Planning Tasks . S.Sreedharan, S.Srivastava, D. Smith, and S. Kambhampati. IJCAI 2019. [\[link\]](https://yochan-lab.github.io/papers/files/papers/IJCAI_19_Camera_Ready_Explaining_Unsolvability.pdf) - Balancing Explicability and Explanations for Human-Aware Planning - T. Chakraborti*, S. Sreedharan*, S. Kambhampati. International Joint Conference on Artificial Intelligence (IJCAI) 2019. [\[link\]](https://yochan-lab.github.io/papers/files/papers/balancing.pdf) - CAP: A Decision Support System for Crew Scheduling using Automated Planning. A. Mishra, S. Sengupta, S. Sreedharan, T. Chakraborti and S. Kambhampati. NDM 2019. [\[pdf\]](https://yochan-lab.github.io/papers/files/papers/CAP.pdf) - Plan Explanations as Model Reconciliation – An Empirical Study. T. Chakraborti, S. Sreedharan, S. Grover and S. Kambhampati. HRI 2019. [\[pdf\]](https://yochan-lab.github.io/papers/files/papers/hri-model-rec.pdf) - Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior. T. Chakraborti, A. Kulkarni, S. Sreedharan, D. Smith, and S. Kambhampati. ICAPS 2019 [\[link\]](https://yochan-lab.github.io/papers/files/papers/landscape.pdf) - A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction. S. Sreedharan, T. Chakraborti, C. Muise, and S. Kambhampati. ICAPS Workshop on Explainable Planning (XAIP), 2019. [\[link\]](https://openreview.net/forum?id=H1gyE6hm5V) - Design for Interpretability. A. Kulkarni*, S. Sreedharan*, T. Chakraborti, D. Smith and S. Kambhampati. ICAPS Workshop on Explainable Planning (XAIP), 2019. [\[link\]](https://yochan-lab.github.io/papers/files/papers/design.pdf) - Projection-Aware Task Planning and Execution for Human-in-the-Loop Operation of Robots in a Mixed-Reality Workspace. T. Chakraborti, S. Sreedharan, A. Kulkarni, and S. Kambhampati. IROS 2018. [\[link\]](https://yochan-lab.github.io/papers/files/papers/projection-aware.pdf) - Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations. S.Sreedharan, S.Srivastava, and S. Kambhampati. IJCAI 2018. [\[link\]](http://www.public.asu.edu/~ssreedh3/static/ijcai_helm_current_camer.pdf) - Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation. S.Sreedharan*, T. Chakraborti*, and S. Kambhampati. ICAPS 2018. (Robotics Track). [\[pdf\]](http://rakaposhi.eas.asu.edu/paper_files/icaps18-multi-mega.pdf) - Balancing Explicability and Explanations: Emergent Behaviors in Human-Aware Planning. T. Chakraborti*, S.Sreedharan*, and S. Kambhampati. AAMAS 2018 (Extended Abstract). [\[pdf\]](http://rakaposhi.eas.asu.edu/paper_files/aamas18-emergent-behaviors.pdf) - Balancing Explicability and Explanation in Human-Aware Planning. S.Sreedharan*, T. Chakraborti*, and S. Kambhampati. AAAI Fall Symposium 2017. [\[link\]](http://authors.elsevier.com/sd/article/S0004370216301539) - Explanations as Model Reconciliation - A Mutli-Agent Perspective. S.Sreedharan*, T. Chakraborti*, and S. Kambhampati. AAAI Fall Symposium 2017. [\[pdf\]](http://rakaposhi.eas.asu.edu/multi-mega_human_fss.pdf) - Robust planning with incomplete domain models. T. Nguyen, S. Kambhampati, S. Sreedharan. Artificial Intelligence Journal 2017. [\[pdf\]](https://arxiv.org/pdf/1708.00543.pdf) - Explanation Generation as Model Reconciliation in Multi-Model Planning. T. Chakraborti*, S. Sreedharan*, S. Kambhampati and Y. Zhang. International Joint Conference on Artificial Intelligence (IJCAI) 2017. [\[pdf\]](https://arxiv.org/pdf/1701.08317.pdf) - Plan Explicability and Predictability for Robot Task Planning. Y. Zhang, S. Sreedharan, A. Kulkarni, T. Chakraborti, H. H. Zhuo and S. Kambhampati. International Conference on Robotics and Automation (ICRA) 2017. [\[pdf\]](http://rakaposhi.eas.asu.edu/icra17-explicability.pdf) - Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots. T. Chakraborti, S. Sreedharan, A. Kulkarni, and S. Kambhampati. ICAPS 2017 Workshop on User Interfaces and Scheduling and Planning (UISP); and ICAPS 2017 System Demonstrations and Exhibits. [\[pdf\]](https://arxiv.org/pdf/1703.08930.pdf) - RADAR - A Proactive Decision Support System for Human-in-the-Loop Planning. S. Sengupta, T. Chakraborti, S. Sreedharan, S. G. Vadlamudi, and S. Kambhampati. ICAPS 2017 Workshop on User Interfaces and Scheduling and Planning (UISP); and ICAPS 2017 System Demonstrations and Exhibits [\[pdf\]](http://www.public.asu.edu/~ssengu15/files/radar_uisp.pdf) - Compliant Conditions for Polynomial Time Approximation of Operator Counts. T. Chakraborti, S. Sreedharan, S. Sengupta, T. K. Satish, and S. Kambhampati. Symposium on Combinatorial Search (SOCS) 2016. [\[pdf\]](http://rakaposhi.eas.asu.edu/socs-16.pdf) - Plan Explainability and Predictability for Robot Task Planning. Y. Zhang,S. Sreedharan, A. Kulkarni, T. Chakraborti, H. H. Zhuo, and S. Kambhampati. In RSS 2016 Workshop on Planning for Human-Robot Interaction: Shared Autonomy and Collaborative Robotics. [\[pdf\]](http://rakaposhi.eas.asu.edu/rss16-wkshp.pdf) - A Formal Analysis of Required Cooperation in Multi-agent Planning . Y. Zhang, S. Sreedharan and S. Kambhampati. International Conference on Automated Planning and Scheduling (ICAPS) 2016. [\[pdf\]](http://rakaposhi.eas.asu.edu/yu-zhang-icaps16.pdf) - Capability Models and their application in Multi-agent planning. Y. Zhang, S. Sreedharan and S. Kambhampati. Antonomous Agents and Multiagent Sytems (AAMAS) 2015. [\[pdf\]](http://rakaposhi.eas.asu.edu/AAMAS15.pdf) ### MS Thesis An Investigation of Topics in Model-Lite Planning and Multi-Agent Planning. ASU June 2016 [\[pdf\]](http://rakaposhi.eas.asu.edu/sarath-ms-thesis.pdf) ## Projects - Cloudy with a chance of synergy - A framework for supporting coordination between humans and robots through structured communication mediums like AR cues and EEG responses. [\[url\]](http://www.ae-robots.com/) - Simoorg - Fault injection system to test highly scalable distributed systems. [\[github\]](https://github.com/linkedin/simoorg) - Explanation as Model Reconciliation - Generating explanations for plans when the planner's model differs from the explainee's expectation from the planner. [\[github\]](https://github.com/TathagataChakraborti/mmp) ## Awards - National Finalist US Imagine cup 2017 (organized by Microsoft). - Received CSE Outstanding Masters Student award from CIDSE, ASU. - Received Central Board of Secondary Education (CBSE) Merit certificate for Physics in class 12th.