Safe Learning of PDDL Domains with Conditional Effects - Extended Version

Grunnleggende konsepter

Learning safe action models with conditional effects may require an exponential number of samples, but Conditional-SAM offers a solution.

Sammendrag

The article discusses the challenges in learning safe action models with conditional effects in planning domains. It introduces the Conditional-SAM algorithm to address this issue and provides theoretical analysis on its complexity and sample requirements. The algorithm is extended to support lifted action models and effects with universal quantifiers. Experimental results on various planning domains are also presented.
Directory:

Abstract

Powerful domain-independent planners require a model of the acting agent's actions.

Introduction

Planning is essential for achieving desired outcomes.

Safe Action Model Learning

SAM algorithms focus on learning safe action models under different assumptions.

Challenges with Conditional Effects

Previous works lacked safety guarantees for learned models with conditional effects.

Proposed Solution: Conditional-SAM Algorithm

Introduces an algorithm capable of learning safe action models with conditional effects.

Theoretical Analysis

Discusses space, runtime, and sample complexity of Conditional-SAM.

Learning Lifted Action Models

Extension to support lifted action models in planning domains.

Learning Effects with Universal Quantifiers

Extension to support universal effects in planning domains.

Experimental Results

Conducted experiments on various planning domains to evaluate the effectiveness of Conditional-SAM.

Statistikk

We prove that learning non-trivial safe action models with conditional effects may require an exponential number of samples.

Sitater

"We aim for consistency between the validity of a plan as determined by the learned model and its validity within the actual model."
"Algorithms from the Safe Action Model Learning (SAM) family address the challenge of learning safe action models under different sets of assumptions."

Viktige innsikter hentet fra

Safe Learning of PDDL Domains with Conditional Effects -- Extended Version

by Argaman Mord... klokken arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15251.pdf

Dypere Spørsmål

How can SAM algorithms be improved to handle more complex domains

To improve SAM algorithms for handling more complex domains, several enhancements can be considered. One approach is to incorporate advanced learning techniques such as deep learning or reinforcement learning to capture intricate patterns in the action models. By leveraging these methods, SAM algorithms can adapt better to non-linear relationships and higher-dimensional data present in complex planning domains. Additionally, introducing mechanisms for dynamic adjustment of parameters based on domain complexity could enhance the algorithm's adaptability. This adaptive feature would allow SAM algorithms to scale effectively across a wide range of domain complexities without manual intervention.

What are potential drawbacks or limitations of using Conditional-SAM

While Conditional-SAM offers significant advantages in learning safe action models with conditional effects, there are potential drawbacks and limitations to consider. One limitation is the exponential increase in sample complexity when dealing with conditional effects that have multiple antecedents or universally quantified variables (UQVs). This high sample complexity can pose challenges in practical applications where large amounts of training data may not be readily available. Another drawback is the computational overhead associated with handling lifted action models and universal quantifiers, which can impact runtime performance and scalability for larger domains.

How does learning lifted action models impact overall planning efficiency

Learning lifted action models introduces additional considerations that impact overall planning efficiency. By incorporating UQVs into the modeling process, Conditional-SAM enables actions with parameterized preconditions and effects to be learned effectively. However, this extension may lead to increased computational complexity due to the combinatorial explosion of possible bindings between parameters and UQVs. As a result, planning efficiency may be affected by longer processing times required for reasoning about parameter-bound literals and their interactions within lifted action models. Despite these challenges, learning lifted action models enhances the expressiveness of learned action models and allows for more flexible representation of domain dynamics.