Generalized Planning
Key Idea
Our research in Generalized Planning focuses on building models and frameworks that reason, plan, and adapt across diverse tasks. We develop transformer-based architectures for sequential decision-making, design agentic systems inspired by Thinking, Fast and Slow, and create knowledge engineering ontologies that support structured reasoning.
Quick Start
- To get started with how we fine-tune LLMs for plan generation, explore our lab materials on GitHub (2024). For a detailed comparison of different language model architectures and their effectiveness in generating plans, read our benchmarking paper (2023).
- Explore our SOFAI Lab library (2025) to understand our architecture inspired by the principles of Thinking, Fast and Slow, and see how it improves planning performance in this paper (2024).
- Learn how we are building a comprehensive Plan Ontology to represent and leverage planning knowledge for downstream applications. Explore the project details and resources on our website.
Foundation Models for Planning
Collaborators:Vishal Pallagani,
Bharath Muppasani,
Biplav
Srivastava, Francesca
Rossi, Lior Horesh, Keerthiram Murugesan,
Kaushik Roy, Amit Sheth
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 surpassed state-of-the-art results in various natural language tasks such as
question-answering, summarization, and text generation.
Many ongoing efforts focus on understanding LLMs' capabilities, including their knowledge of the world,
syntax, and semantics.
However, extending the textual prowess of LLMs to symbolic reasoning has been slow and predominantly focused
on tackling problems related to the mathematical field.
In this work, we explore the use of LLMs for automated planning - a branch of AI concerned with the
realization of action sequences (plans) to achieve a goal, typically executed by intelligent agents,
autonomous robots, and unmanned vehicles.
Representative Publications
- Revisiting LLMs in Planning from Literature Review: a Semi-Automated Analysis Approach and Evolving Categories Representing Shifting Perspectives
The 35th International Conference on Automated Planning and Scheduling (ICAPS), 2025
[Paper] [BibTex] [Visualization Tool] [Github] - On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling
(APS)
The 34th International Conference on Automated Planning and Scheduling (ICAPS), 2024
[Paper] [BibTex] [Visualization Tool] [Github] - The Case for Developing a Foundation Model for Planning-like Tasks from
Scratch
Planning and Reinforcement Learning (PRL) Workshop at ICAPS, 2024
[Paper]
- Understanding the Capabilities of Large Language Models for Automated Planning
Preprint, 2023
[Paper] [BibTex] - Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers
Proc. of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI) Demonstrations Track, 2023
[Paper][Tool Website] [Demo][BibTex] - Plansformer: Generating Symbolic Plans using Transformers
Workshop on Generalization in Planning (GenPlan) at NeurIPS, 2023
[Paper] [BibTex]
Representative Activities
- Harnessing Large Language Models for Planning: A Lab on Strategies for Success and Mitigation of
Pitfalls
AAAI Conference on Artificial Intelligence, 2024
[Github] [Website] [BibTex]
Representative Patents
- P202203822US01 Plansformer - a transformer planner
Francesca Rossi, Lior Horesh, Vishal Pallagani, Biplav Srivastava, Andrea Loreggia
- USC 1702 Unsupervised Plan Summarization to Improve Planner Performance and Human Interpretability
Biplav Srivastava, Vishal Pallagani
Planning using Fast and
Slow AI Architecture
Collaborators: Vishal Pallagani,
Biplav
Srivastava, Francesca
Rossi, Lior Horesh, Keerthiram Murugesan
The newly introduced idea of Fast and Slow AI (SOFAI) architecture is inspired from the cognitive theories
mentioned by Daniel Kahneman in Thinking Fast and Slow.
This research project aims to build AI-supported machines that can
- make decisions with emergent behaviors similar to the human ones, and
- support human decision making through nudging and explanations.
To achieve these goals, the team is designing and building a cognitive architecture to mimic these two broad modalities in a machine. We adapt the SOFAI architecture to solving planning domains where incoming problems are solved by either system 1 (or ”fast” - S1) agents, also called solvers, that react by exploiting either past experience (case-based reasoning) or using a learnt model called as Plansformer, or by system 2 (or ”slow” - S2) agents, that are deliberately activated when there is the need to reason and search for optimal solutions beyond what is expected from the system 1 agent. SOFAI architecture with Plansformer as S1 solves more problems than the symbolic planner (FastDownward, which is also used as S2). Visit the dedicated website for more details on SOFAI.
Representative Publications
- Plan-SOFAI: A Neuro-Symbolic Planning Architecture
Neuro-Symbolic Learning and Reasoning in the era of Large Language Models (NuCLeaR) Workshop at AAAI 2024
[Paper] [BibTex] - Fast and Slow Planning
Preprint
[Paper] [BibTex] - Epistemic Planning in a Fast and Slow Setting
AAAI 2022 Fall Symposium Series, Thinking Fast and Slow and Other Cognitive Theories in AI track
[Paper] [BibTex]
Representative Activities
Knowledge Engineering for Planning
Collaborators: (Students) Bharath Muppasani, Nitin Gupta and Vishal Pallagani (Faculty) Biplav Srivastava, Raghava Mutharaju, Michael N. Huhns and Vignesh Narayanan
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 the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypothesize that given a large number of available planners and diverse planning domains; they carry essential information that can be leveraged to identify suitable planners and improve their performance for a domain. We use data on planning domains and planners from the International Planning Competition (IPC) to construct a planning ontology and demonstrate via experiments in two use cases that the ontology can lead to the selection of promising planners and improving their performance using macros - a form of action ordering constraints extracted from planning ontology. We also make the planning ontology and associated resources available to the community to promote further research.
Representative Publications
- Building a Plan Ontology to Represent and Exploit Planning Knowledge and Its Applications
Eighth International Conference on Data Science and Management of Data (CODS-COMAD '24), India, 2024.
[Paper] [Website]
Representative Patents
- USC 1649 Improving Planner Performance by Learning and Using Metadata of Experiences
Biplav Srivastava, Vishal Pallagani, Bharath Muppasani
