Search Results for author: Michael Katz

Found 15 papers, 8 papers with code

Thought of Search: Planning with Language Models Through The Lens of Efficiency

no code implementations18 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.

Large Language Models as Planning Domain Generators

1 code implementation2 Apr 2024 James Oswald, Kavitha Srinivas, Harsha Kokel, JunKyu Lee, Michael Katz, Shirin Sohrabi

To this end, we investigate if large language models (LLMs) can be used to generate planning domain models from simple textual descriptions.

Some Orders Are Important: Partially Preserving Orders in Top-Quality Planning

1 code implementation1 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.

Unifying and Certifying Top-Quality Planning

no code implementations5 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.

Choosing a Classical Planner with Graph Neural Networks

no code implementations25 Jan 2024 Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen, Horst Samulowitz, Michael Katz

Thus, the ability to predict their performance on a given problem is of great importance.

Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning

no code implementations22 Nov 2023 Turgay Caglar, Sirine Belhaj, Tathagata Chakraborti, Michael Katz, Sarath Sreedharan

This is the first work to look at the application of large language models (LLMs) for the purpose of model space edits in automated planning tasks.

Generalized Planning in PDDL Domains with Pretrained Large Language Models

1 code implementation18 May 2023 Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Michael Katz

We investigate whether LLMs can serve as generalized planners: given a domain and training tasks, generate a program that efficiently produces plans for other tasks in the domain.

Reinforced Meta Active Learning

no code implementations9 Mar 2022 Michael Katz, Eli Kravchik

In stream-based active learning, the learning procedure typically has access to a stream of unlabeled data instances and must decide for each instance whether to label it and use it for training or to discard it.

Active Learning Informativeness +1

Hierarchical Reinforcement Learning with AI Planning Models

1 code implementation1 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).

Decision Making Hierarchical Reinforcement Learning +2

Efficient Meta Subspace Optimization

1 code implementation28 Oct 2021 Yoni Choukroun, Michael Katz

Subspace optimization methods have the attractive property of reducing large-scale optimization problems to a sequence of low-dimensional subspace optimization problems.

Reinforcement Learning (RL)

Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators

no code implementations30 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.

reinforcement-learning Reinforcement Learning (RL)

Efficient Black-Box Planning Using Macro-Actions with Focused Effects

2 code implementations28 Apr 2020 Cameron Allen, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro

Focused macros dramatically improve black-box planning efficiency across a wide range of planning domains, sometimes beating even state-of-the-art planners with access to a full domain model.

Implicit Abstraction Heuristics

no code implementations16 Jan 2014 Michael Katz, Carmel Domshlak

Indeed, some of the power of the explicit abstraction heuristics comes from precomputing the heuristic function offline and then determining h(s) for each evaluated state s by a very fast lookup in a database.

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