Author : Vishal Pallagani
Advisor: Dr. Biplav Srivastava
Date: March 26th, 2026
Time: 9 am
Place: Seminar Room, AI Institute
Link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_NzkzZTk0NGItOGQx…

Abstract

Planning is a fundamental capability for intelligent systems, yet classical planners remain difficult to scale and generalize across domains due to their reliance on hand-engineered models, brittle search procedures, and limited adaptability. These limitations constrain real-world applications that require flexible reasoning, robust decision-making, and the ability to operate across diverse environments.

Large language models have emerged as powerful learners supported by their architectures and large-scale training data, demonstrating strong capabilities in tasks such as question answering, code generation, summarization, and reasoning. Motivated by their broad generalization abilities, I investigate whether language models can be systematically leveraged to advance automated planning.

I begin by conducting a comprehensive study of existing literature to categorize how language models are being used for automated planning, resulting in a structured taxonomy that captures the full landscape of techniques, objectives, and use cases, along with a semi-automated platform for tracking the evolution of this rapidly growing area. Building on this foundation, I examine how language models can be used for effective plan generation by evaluating their pretrained capabilities and quantifying the benefits of fine-tuning on planning-specific data.

To alleviate the limitations of relying solely on language models, I introduce a complementary approach for plan generation by training a compact foundational model from scratch. This approach adopts a state-centric perspective to generalized planning, learning transition dynamics over graph-based state representations. In addition, I develop a planning ontology to systematically capture metadata, enabling more structured data representation and facilitating improved training of future models. I also introduce plan summarization as a downstream task, demonstrating how these trained models can be effectively leveraged for concise and structured plan understanding.

Next, I develop neurosymbolic architectures, grounded in the SOFAI ``Thinking, Fast and Slow'' paradigm, that integrate language models with symbolic planning under metacognitive control to achieve more robust and generalizable plan generation. These architectures address key limitations of language models in planning and demonstrate how symbolic reasoning can complement learning-based models. Finally, I evaluate the resulting generalized planners in real-world applications including dialog-based information retrieval, trustworthy conversational AI for sensitive domains, and adaptive replanning in stochastic manufacturing environments, showing how generalized planners combining language models and symbolic methods are practical tools for deployment.

Collectively, my contributions advance the scientific understanding of how language models can support, improve, and generalize automated planning, offering a coherent path toward planning systems that combine the strengths of learning and symbolic reasoning.