no code implementations • 18 Apr 2024 • Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi
We analyse the cost of using LLMs for planning and highlight that recent trends are profoundly uneconomical.
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.