no code implementations • 19 Dec 2023 • Burak Aksar, Yara Rizk, Tathagata Chakraborti
Chatbots have become one of the main pathways for the delivery of business automation tools.
no code implementations • 22 Nov 2023 • Turgay Caglar, Sirine Belhaj, Tathagata Chakraborti, Michael Katz, Sarath Sreedharan
This is the first work to look at the application of large language models (LLMs) for the purpose of model space edits in automated planning tasks.
1 code implementation • 13 Jun 2023 • Tathagata Chakraborti, Jungkoo Kang, Christian Muise, Sarath Sreedharan, Michael Walker, Daniel Szafir, Tom Williams
This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper.
1 code implementation • 14 Jun 2022 • Ethan Callanan, Rebecca De Venezia, Victoria Armstrong, Alison Paredes, Tathagata Chakraborti, Christian Muise
For over three decades, the planning community has explored countless methods for data-driven model acquisition.
2 code implementations • 23 Feb 2022 • Michael Walker, Thao Phung, Tathagata Chakraborti, Tom Williams, Daniel Szafir
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in research in recent years.
1 code implementation • 27 Sep 2021 • Mayank Agarwal, Tathagata Chakraborti, Sachin Grover, Arunima Chaudhary
While India has been one of the hotspots of COVID-19, data about the pandemic from the country has proved to be largely inaccessible at scale.
1 code implementation • 3 Mar 2021 • Mayank Agarwal, Tathagata Chakraborti, Quchen Fu, David Gros, Xi Victoria Lin, Jaron Maene, Kartik Talamadupula, Zhongwei Teng, Jules White
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line.
no code implementations • 22 Nov 2020 • Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti, David E. Smith, Subbarao Kambhampati
Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation.
no code implementations • 21 Nov 2020 • Sarath Sreedharan, Tathagata Chakraborti, Yara Rizk, Yasaman Khazaeni
A new design of an AI assistant that has become increasingly popular is that of an "aggregated assistant" -- realized as an orchestrated composition of several individual skills or agents that can each perform atomic tasks.
no code implementations • 27 Jul 2020 • Tathagata Chakraborti, Vatche Isahagian, Rania Khalaf, Yasaman Khazaeni, Vinod Muthusamy, Yara Rizk, Merve Unuvar
In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes.
no code implementations • 2 Jul 2020 • Anagha Kulkarni, Sarath Sreedharan, Sarah Keren, Tathagata Chakraborti, David Smith, Subbarao Kambhampati
Given structured environments (like warehouses and restaurants), it may be possible to design the environment so as to boost the interpretability of the robot's behavior or to shape the human's expectations of the robot's behavior.
no code implementations • 26 Feb 2020 • Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati
In this paper, we provide a comprehensive outline of the different threads of work in Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years and contrast that with earlier efforts in the field in terms of techniques, target users, and delivery mechanisms.
1 code implementation • 31 Jan 2020 • Mayank Agarwal, Jorge J. Barroso, Tathagata Chakraborti, Eli M. Dow, Kshitij Fadnis, Borja Godoy, Madhavan Pallan, Kartik Talamadupula
This whitepaper reports on Project CLAI (Command Line AI), which aims to bring the power of AI to the command line interface (CLI).
no code implementations • 8 Jan 2020 • Tathagata Chakraborti, Yasaman Khazaeni
This paper builds upon recent work in the declarative design of dialogue agents and proposes an exciting new tool -- D3BA -- Declarative Design for Digital Business Automation, built to optimize business processes using the power of AI planning.
no code implementations • 7 Jan 2020 • Yara Rizk, Abhishek Bhandwalder, Scott Boag, Tathagata Chakraborti, Vatche Isahagian, Yasaman Khazaeni, Falk Pollock, Merve Unuvar
Business process automation is a booming multi-billion-dollar industry that promises to remove menial tasks from workers' plates -- through the introduction of autonomous agents -- and free up their time and brain power for more creative and engaging tasks.
no code implementations • 17 Oct 2019 • Christian Muise, Tathagata Chakraborti, Shubham Agarwal, Ondrej Bajgar, Arunima Chaudhary, Luis A. Lastras-Montano, Josef Ondrej, Miroslav Vodolan, Charlie Wiecha
Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft.
no code implementations • 18 Mar 2019 • Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Subbarao Kambhampati
In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model.
no code implementations • 23 Nov 2018 • Tathagata Chakraborti, Anagha Kulkarni, Sarath Sreedharan, David E. Smith, Subbarao Kambhampati
There has been significant interest of late in generating behavior of agents that is interpretable to the human (observer) in the loop.
no code implementations • 3 Feb 2018 • Tathagata Chakraborti, Sarath Sreedharan, Sachin Grover, Subbarao Kambhampati
Recent work in explanation generation for decision making agents has looked at how unexplained behavior of autonomous systems can be understood in terms of differences in the model of the system and the human's understanding of the same, and how the explanation process as a result of this mismatch can be then seen as a process of reconciliation of these models.
no code implementations • 30 Jan 2018 • Tathagata Chakraborti, Subbarao Kambhampati
Effective collaboration between humans and AI-based systems requires effective modeling of the human in the loop, both in terms of the mental state as well as the physical capabilities of the latter.
no code implementations • 13 Sep 2017 • Tathagata Chakraborti, Kshitij P. Fadnis, Kartik Talamadupula, Mishal Dholakia, Biplav Srivastava, Jeffrey O. Kephart, Rachel K. E. Bellamy
In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making.
no code implementations • 1 Aug 2017 • Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati
In this paper, we bring these two concepts together and show how a planner can account for both these needs and achieve a trade-off during the plan generation process itself by means of a model-space search method MEGA.
no code implementations • 15 Jul 2017 • Tathagata Chakraborti, Subbarao Kambhampati, Matthias Scheutz, Yu Zhang
Among the many anticipated roles for robots in the future is that of being a human teammate.
1 code implementation • 19 May 2017 • Sailik Sengupta, Tathagata Chakraborti, Subbarao Kambhampati
Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example.
no code implementations • 28 Jan 2017 • Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang, Subbarao Kambhampati
When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior.
no code implementations • 16 Nov 2016 • Anagha Kulkarni, Yantian Zha, Tathagata Chakraborti, Satya Gautam Vadlamudi, Yu Zhang, Subbarao Kambhampati
In order to have effective human-AI collaboration, it is necessary to address how the AI agent's behavior is being perceived by the humans-in-the-loop.
no code implementations • 27 Sep 2016 • Tathagata Chakraborti, Kartik Talamadupula, Kshitij P. Fadnis, Murray Campbell, Subbarao Kambhampati
In this paper, we present UbuntuWorld 1. 0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system.
no code implementations • 24 Jun 2016 • Satya Gautam Vadlamudi, Tathagata Chakraborti, Yu Zhang, Subbarao Kambhampati
Proactive decision support (PDS) helps in improving the decision making experience of human decision makers in human-in-the-loop planning environments.
no code implementations • 25 May 2016 • Tathagata Chakraborti, Sarath Sreedharan, Sailik Sengupta, T. K. Satish Kumar, Subbarao Kambhampati
In this paper, we develop a computationally simpler version of the operator count heuristic for a particular class of domains.
no code implementations • 25 Nov 2015 • Yu Zhang, Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti, Hankz Hankui Zhuo, Subbarao Kambhampati
Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans.