no code implementations • 18 Apr 2024 • Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi
We analyse these properties of using LLMs for planning and highlight that recent trends abandon both soundness and completeness for the sake of inefficiency.
1 code implementation • 1 Apr 2024 • Michael Katz, JunKyu Lee, Jungkoo Kang, Shirin Sohrabi
The ability to generate multiple plans is central to using planning in real-life applications.
no code implementations • 5 Mar 2024 • Michael Katz, JunKyu Lee, Shirin Sohrabi
We show that task transformations found in the existing literature can be employed for the efficient certification of various top-quality planning problems and propose a novel transformation to efficiently certify loopless top-quality planning.
1 code implementation • 1 Mar 2022 • JunKyu Lee, Michael Katz, Don Joven Agravante, Miao Liu, Geraud Nangue Tasse, Tim Klinger, Shirin Sohrabi
Our approach defines options in hierarchical reinforcement learning (HRL) from AIP operators by establishing a correspondence between the state transition model of AI planning problem and the abstract state transition system of a Markov Decision Process (MDP).
no code implementations • 30 Sep 2021 • Clement Gehring, Masataro Asai, Rohan Chitnis, Tom Silver, Leslie Pack Kaelbling, Shirin Sohrabi, Michael Katz
In this paper, we propose to leverage domain-independent heuristic functions commonly used in the classical planning literature to improve the sample efficiency of RL.
no code implementations • 27 Aug 2014 • Shirin Sohrabi, Octavian Udrea, Anton V. Riabov
To capture the model description we propose a language called LTS++ and a web-based tool that enables the specification of the LTS++ model and a set of observations.