53 citations · 434 across the 45 of their papers we have counts for
50 papers
AWARE-RG: Abstraction Aware Explanation of Resiliency Graphs for Cyber Physical Systems
Shadaab Kawnain Bashir, Rakesh Podder, Philip D. Hopkins +3
Understanding the security posture of cyber physical systems (CPS) requires reasoning across both information technology (IT) and operational technology (OT). However, a security a…
Safety Graph: When Safety Responses Become Attack Surfaces in Cyber-Physical Systems
Rakesh Podder, Philip D. Hopkins, Sarath Sreedharan +2
Modern industrial control systems (ICS) are susceptible to cyber attacks triggering safety failures in the operational technology (OT) layer. The traditional way to mitigate any fa…
Evaluative Dynamics of AI Integration and Expert Performance under Epistemic Dependence across Heterogeneous Stakes
Dennis Kim, Roya Daneshi, Nikhil Krishnaswamy +2
AI is increasingly integrated into expert workflows, yet how integration affects perceptions of the expert, AI, and their combination remains unclear in domains where lay users are…
i-EXAM: Instructable and Explainable Attack Connectivity Graph Modeler
Rakesh Podder, Wadia Ganim, Sarath Sreedharan +2
i-EXAM is a planning-powered tool that helps system administrators to create security profiles of complex networks and perform what-if analyses to identify network hardening strate…
NestRL: A Nested Training Regime for Mutual Adaptation in Human-AI Teaming
Upasana Biswas, Durgesh Kalwar, Subbarao Kambhampati +1
Mutual adaptation is a central challenge in human-AI teaming, as humans naturally adjust their strategies in response to an AI agent's behavior. Existing approaches attempt to appr…
Asymmetric Trust Effects of Corrective AI in Expert Advisory Workflows under Epistemic Dependence
Dennis Kim, Roya Daneshi, Bruce Draper +1
The increasing integration of AI-powered tools into expert workflows, such as medicine, law, and finance, raises a critical question: how does AI involvement influence a user`s tru…
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…
SPEAR: Security Posture Evaluation using AI Planner-Reasoning on Attack-Connectivity Hypergraphs
Rakesh Podder, Turgay Caglar, Shadaab Kawnain Bashir +3
Graph-based frameworks are often used in network hardening to help a cyber defender understand how a network can be attacked and how the best defenses can be deployed. However, inc…
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…
Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI
Silvia Tulli, Stylianos Loukas Vasileiou, Sarath Sreedharan
"Human-aware" has become a popular keyword used to describe a particular class of AI systems that are designed to work and interact with humans. While there exists a surprising lev…
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,…
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…
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…