53 citations · 434 across the 45 of their papers we have counts for
38 papers · 1 filter
On the Role of Domain Experts in Creating Effective Tutoring Systems
Sarath Sreedharan, Kelsey Sikes, Nathaniel Blanchard +3
The role that highly curated knowledge, provided by domain experts, could play in creating effective tutoring systems is often overlooked within the AI for education community. In…
Seemingly Simple Planning Problems are Computationally Challenging: The Countdown Game
Michael Katz, Harsha Kokel, Sarath Sreedharan
There is a broad consensus that the inability to form long-term plans is one of the key limitations of current foundational models and agents. However, the existing planning benchm…
Make Planning Research Rigorous Again!
Michael Katz, Harsha Kokel, Christian Muise +2
In over sixty years since its inception, the field of planning has made significant contributions to both the theory and practice of building planning software that can solve a nev…
Inferring Implicit Goals Across Differing Task Models
Silvia Tulli, Stylianos Loukas Vasileiou, Mohamed Chetouani +1
One of the significant challenges to generating value-aligned behavior is to not only account for the specified user objectives but also any implicit or unspecified user requiremen…
Explainable Human-AI Interaction: A Planning Perspective
Sarath Sreedharan, Anagha Kulkarni, Subbarao Kambhampati
From its inception, AI has had a rather ambivalent relationship with humans -- swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives…
Reducing Human-Robot Goal State Divergence with Environment Design
Kelsey Sikes, Sarah Keren, Sarath Sreedharan
One of the most difficult challenges in creating successful human-AI collaborations is aligning a robot's behavior with a human user's expectations. When this fails to occur, a rob…
Expectation Alignment: Handling Reward Misspecification in the Presence of Expectation Mismatch
Malek Mechergui, Sarath Sreedharan
Detecting and handling misspecified objectives, such as reward functions, has been widely recognized as one of the central challenges within the domain of Artificial Intelligence (…
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,…
On the Planning Abilities of Large Language Models : A Critical Investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan +1
Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to e…
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
Lin Guan, Karthik Valmeekam, Sarath Sreedharan +1
There is a growing interest in applying pre-trained large language models (LLMs) to planning problems. However, methods that use LLMs directly as planners are currently impractical…
Planning for Attacker Entrapment in Adversarial Settings
Brittany Cates, Anagha Kulkarni, Sarath Sreedharan
In this paper, we propose a planning framework to generate a defense strategy against an attacker who is working in an environment where a defender can operate without the attacker…
On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)
Karthik Valmeekam, Sarath Sreedharan, Matthew Marquez +2
Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to e…
Goal Alignment: A Human-Aware Account of Value Alignment Problem
Malek Mechergui, Sarath Sreedharan
Value alignment problems arise in scenarios where the specified objectives of an AI agent don't match the true underlying objective of its users. The problem has been widely argued…
A Mental Model Based Framework of Trust
Zahra Zahedi, Sarath Sreedharan, Erin Chiou +1
Handling trust is a core requirement of effective interaction between people and AI agents. Thus, any decision-making framework designed to work with people must be able to estimat…
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion
Utkarsh Soni, Nupur Thakur, Sarath Sreedharan +4
There is a growing interest in developing automated agents that can work alongside humans. In addition to completing the assigned task, such an agent will undoubtedly be expected t…
A Mental-Model Centric Landscape of Human-AI Symbiosis
Zahra Zahedi, Sarath Sreedharan, Subbarao Kambhampati
There has been significant recent interest in developing AI agents capable of effectively interacting and teaming with humans. While each of these works try to tackle a problem qui…
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
Lin Guan, Sarath Sreedharan, Subbarao Kambhampati
Creating reinforcement learning (RL) agents that are capable of accepting and leveraging task-specific knowledge from humans has been long identified as a possible strategy for dev…
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems
Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma +2
Despite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant prob…
Not all users are the same: Providing personalized explanations for sequential decision making problems
Utkarsh Soni, Sarath Sreedharan, Subbarao Kambhampati
There is a growing interest in designing autonomous agents that can work alongside humans. Such agents will undoubtedly be expected to explain their behavior and decisions. While g…
Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction
Zahra Zahedi, Mudit Verma, Sarath Sreedharan +1
Trust between team members is an essential requirement for any successful cooperation. Thus, engendering and maintaining the fellow team members' trust becomes a central responsibi…
A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI
Sarath Sreedharan, Anagha Kulkarni, David E. Smith +1
Existing approaches for generating human-aware agent behaviors have considered different measures of interpretability in isolation. Further, these measures have been studied under…
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…
RADAR-X: An Interactive Mixed Initiative Planning Interface Pairing Contrastive Explanations and Revised Plan Suggestions
Karthik Valmeekam, Sarath Sreedharan, Sailik Sengupta +1
Decision support systems seek to enable informed decision-making. In the recent years, automated planning techniques have been leveraged to empower such systems to better aid the h…