Generalized Planning
NEW! Our lab titled 'Harnessing Large Language Models for Planning: A Lab on Strategies for Success and Mitigation of Pitfalls' has been accepted for presentation at AAAI 2024. The event will take place in Vancouver, Canada on February 21, 2024. For more information about the lab, you can visit our website.
Generative Models for
Automated 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
- 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] - Understanding the Capabilities of Large Language Models for Automated Planning
Preprint
[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
Planning using Fast and
Slow AI Architecture
Collaborators: Vishal Pallagani,
Bharath Muppasani,
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]
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
