44 citations · 72 across the 11 of their papers we have counts for
5 papers · 1 filter
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
Vishal Pallagani, Kaushik Roy, Bharath Muppasani +7
Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 pap…
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…
A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency
Bharath Muppasani, Vishal Pallagani, Biplav Srivastava +3
Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we consider t…
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…
Plansformer: Generating Symbolic Plans using Transformers
Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan +5
Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP). From BERT to BLOOM, LLMs have surpass…