61 citations · 335 across the 31 of their papers we have counts for
22 papers · 1 filter
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,…
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-…
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…
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…
Balancing Explicability and Explanation in Human-Aware Planning
Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati
Human aware planning requires an agent to be aware of the intentions, capabilities and mental model of the human in the loop during its decision process. This can involve generatin…
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang +1
When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved t…
Explicablility as Minimizing Distance from Expected Behavior
Anagha Kulkarni, Yantian Zha, Tathagata Chakraborti +3
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. When the agent's task plans…
UbuntuWorld 1.0 LTS - A Platform for Automated Problem Solving & Troubleshooting in the Ubuntu OS
Tathagata Chakraborti, Kartik Talamadupula, Kshitij P. Fadnis +2
In this paper, we present UbuntuWorld 1.0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system. Specifically, we propose to use the Bas…
Compliant Conditions for Polynomial Time Approximation of Operator Counts
Tathagata Chakraborti, Sarath Sreedharan, Sailik Sengupta +2
In this paper, we develop a computationally simpler version of the operator count heuristic for a particular class of domains. The contribution of this abstract is threefold, we (1…
Plan Explicability and Predictability for Robot Task Planning
Yu Zhang, Sarath Sreedharan, Anagha Kulkarni +3
Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomous…