61 citations · 335 across the 31 of their papers we have counts for
34 papers
How Good Are LLMs at Processing Tool Outputs?
Kiran Kate, Yara Rizk, Poulami Ghosh +4
Most realistic task automation problems require large language models (LLMs) to call tools, which often return complex JSON responses. These responses must be further processed to…
TESS: A Multi-intent Parser for Conversational Multi-Agent Systems with Decentralized Natural Language Understanding Models
Burak Aksar, Yara Rizk, Tathagata Chakraborti
Chatbots have become one of the main pathways for the delivery of business automation tools. Multi-agent systems offer a framework for designing chatbots at scale, making it easier…
Enabling Robots to Identify Missing Steps in Robot Tasks for Guided Learning from Demonstration
Maximilian Diehl, Tathagata Chakraborti, Karinne Ramirez-Amaro
Learning from Demonstration (LfD) systems are commonly used to teach robots new tasks by generating a set of skills from user-provided demonstrations. These skills can then be sequ…
Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning
Turgay Caglar, Sirine Belhaj, Tathagata Chakraborti +2
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. To set the stage for this union,…
TOBY: A Tool for Exploring Data in Academic Survey Papers
Tathagata Chakraborti, Jungkoo Kang, Christian Muise +4
This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper. The visualization consists of four components: a hierarchical vi…
MACQ: A Holistic View of Model Acquisition Techniques
Ethan Callanan, Rebecca De Venezia, Victoria Armstrong +3
For over three decades, the planning community has explored countless methods for data-driven model acquisition. These range in sophistication (e.g., simple set operations to full-…
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A Survey and Virtual Design Element Taxonomy
Michael Walker, Thao Phung, Tathagata Chakraborti +2
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in research in recent years. However, the HRI community lacks a…
COVID-19 India Dataset: Parsing COVID-19 Data in Daily Health Bulletins from States in India
Mayank Agarwal, Tathagata Chakraborti, Sachin Grover +1
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. Much of the data exists in unstructur…
NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands
Mayank Agarwal, Tathagata Chakraborti, Quchen Fu +6
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line. Participants were tasked with building models that can t…
A Bayesian Account of Measures of Interpretability in Human-AI Interaction
Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti +2
Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation. In this paper we posit that, in the design and depl…
Explainable Composition of Aggregated Assistants
Sarath Sreedharan, Tathagata Chakraborti, Yara Rizk +1
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…
Designing Environments Conducive to Interpretable Robot Behavior
Anagha Kulkarni, Sarath Sreedharan, Sarah Keren +3
Designing robots capable of generating interpretable behavior is a prerequisite for achieving effective human-robot collaboration. This means that the robots need to be capable of…
From Robotic Process Automation to Intelligent Process Automation: Emerging Trends
Tathagata Chakraborti, Vatche Isahagian, Rania Khalaf +4
In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes. Over the last decade, there has been steady progress towards th…
The Emerging Landscape of Explainable AI Planning and Decision Making
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 an…
D3BA: A Tool for Optimizing Business Processes Using Non-Deterministic Planning
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, bui…
Project CLAI: Instrumenting the Command Line as a New Environment for AI Agents
Mayank Agarwal, Jorge J. Barroso, Tathagata Chakraborti +5
This whitepaper reports on Project CLAI (Command Line AI), which aims to bring the power of AI to the command line interface (CLI). The CLAI platform sets up the CLI as a new envir…
A Unified Conversational Assistant Framework for Business Process Automation
Yara Rizk, Abhishek Bhandwalder, Scott Boag +5
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 --…
Planning for Goal-Oriented Dialogue Systems
Christian Muise, Tathagata Chakraborti, Shubham Agarwal +6
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,…
Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning
Sarath Sreedharan, Tathagata Chakraborti, Christian Muise +1
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 di…
Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior
Tathagata Chakraborti, Anagha Kulkarni, Sarath Sreedharan +2
There has been significant interest of late in generating behavior of agents that is interpretable to the human (observer) in the loop. However, the work in this area has typically…
Plan Explanations as Model Reconciliation -- An Empirical Study
Tathagata Chakraborti, Sarath Sreedharan, Sachin Grover +1
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…
Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration
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 capabili…
Visualizations for an Explainable Planning Agent
Tathagata Chakraborti, Kshitij P. Fadnis, Kartik Talamadupula +4
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. Imposing transparency and…
AI Challenges in Human-Robot Cognitive Teaming
Tathagata Chakraborti, Subbarao Kambhampati, Matthias Scheutz +1
Among the many anticipated roles for robots in the future is that of being a human teammate. Aside from all the technological hurdles that have to be overcome with respect to hardw…