53 citations · 434 across the 45 of their papers we have counts for
8 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,…
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…