Understanding Real-World AI Planning Domains: A Conceptual Framework

TL;DR

This paper introduces a comprehensive conceptual framework for understanding and categorizing the key aspects of real-world AI planning domains, aiding system development and application.

Contribution

It develops a novel framework that identifies and categorizes real-world planning domain aspects, providing a common terminology and detailed overview, exemplified through sustainable buildings.

Findings

Framework offers a structured overview of planning aspects.

Supports better design and development of AI planning systems.

Applicable to diverse real-world domains.

Abstract

Planning is a pivotal ability of any intelligent system being developed for real-world applications. AI planning is concerned with researching and developing planning systems that automatically compute plans that satisfy some user objective. Identifying and understanding the relevant and realistic aspects that characterise real-world application domains are crucial to the development of AI planning systems. This provides guidance to knowledge engineers and software engineers in the process of designing, identifying, and categorising resources required for the development process. To the best of our knowledge, such support does not exist. We address this research gap by developing a conceptual framework that identifies and categorises the aspects of real-world planning domains in varying levels of granularity. Our framework provides not only a common terminology but also a comprehensive overview of a broad range of planning aspects exemplified using the domain of sustainable buildings as a prominent application domain of AI planning. The framework has the potential to impact the design, development, and applicability of AI planning systems in real-world application domains.