Search Results for author: Tathagata Chakraborti

Found 27 papers, 5 papers with code

MACQ: A Holistic View of Model Acquisition Techniques

1 code implementation14 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.

Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A Survey and Virtual Design Element Taxonomy

1 code implementation23 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.

Mixed Reality

COVID-19 India Dataset: Parsing COVID-19 Data in Daily Health Bulletins from States in India

1 code implementation27 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.

NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands

no code implementations3 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.

A Bayesian Account of Measures of Interpretability in Human-AI Interaction

no code implementations22 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.

Explainable Composition of Aggregated Assistants

no code implementations21 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.

From Robotic Process Automation to Intelligent Process Automation: Emerging Trends

no code implementations27 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.

Designing Environments Conducive to Interpretable Robot Behavior

no code implementations2 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.

The Emerging Landscape of Explainable AI Planning and Decision Making

no code implementations26 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.

Decision Making

Project CLAI: Instrumenting the Command Line as a New Environment for AI Agents

1 code implementation31 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).

D3BA: A Tool for Optimizing Business Processes Using Non-Deterministic Planning

no code implementations8 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.

A Unified Conversational Assistant Framework for Business Process Automation

no code implementations7 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.

Planning for Goal-Oriented Dialogue Systems

no code implementations17 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.

Goal-Oriented Dialogue Systems slot-filling +1

Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning

no code implementations18 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.

Decision Making

Plan Explanations as Model Reconciliation -- An Empirical Study

no code implementations3 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.

Decision Making Explanation Generation

Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration

no code implementations30 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.

Visualizations for an Explainable Planning Agent

no code implementations13 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.

Decision Making

Balancing Explicability and Explanation in Human-Aware Planning

no code implementations1 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.

Decision Making Explanation Generation

AI Challenges in Human-Robot Cognitive Teaming

no code implementations15 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.

MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

1 code implementation19 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.

Classification General Classification

Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy

no code implementations28 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.

Explicablility as Minimizing Distance from Expected Behavior

no code implementations16 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.

UbuntuWorld 1.0 LTS - A Platform for Automated Problem Solving & Troubleshooting in the Ubuntu OS

no code implementations27 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.

Proactive Decision Support using Automated Planning

no code implementations24 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.

Decision Making

Compliant Conditions for Polynomial Time Approximation of Operator Counts

no code implementations25 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.

Plan Explicability and Predictability for Robot Task Planning

no code implementations25 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.

Motion Planning Robot Task Planning

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