6 citations · 10 across the 7 of their papers we have counts for
7 papers
Neurosymbolic AI for Enhancing Instructability in Generative AI
Amit Sheth, Vishal Pallagani, Kaushik Roy
Generative AI, especially via Large Language Models (LLMs), has transformed content creation across text, images, and music, showcasing capabilities in following instructions throu…
PLANTS: A Novel Problem and Dataset for Summarization of Planning-Like (PL) Tasks
Vishal Pallagani, Biplav Srivastava, Nitin Gupta
Text summarization is a well-studied problem that deals with deriving insights from unstructured text consumed by humans, and it has found extensive business applications. However,…
The Case for Developing a Foundation Model for Planning-like Tasks from Scratch
Biplav Srivastava, Vishal Pallagani
Foundation Models (FMs) have revolutionized many areas of computing, including Automated Planning and Scheduling (APS). For example, a recent study found them useful for planning p…
On Solving the Rubik's Cube with Domain-Independent Planners Using Standard Representations
Bharath Muppasani, Vishal Pallagani, Biplav Srivastava +1
Rubik's Cube (RC) is a well-known and computationally challenging puzzle that has motivated AI researchers to explore efficient alternative representations and problem-solving meth…
Value-based Fast and Slow AI Nudging
Marianna B. Ganapini, Francesco Fabiano, Lior Horesh +7
Nudging is a behavioral strategy aimed at influencing people's thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techni…
Understanding the Capabilities of Large Language Models for Automated Planning
Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan +5
Automated planning is concerned with developing efficient algorithms to generate plans or sequences of actions to achieve a specific goal in a given environment. Emerging Large Lan…