🤖 AI Summary
This work proposes a behavior-based planning approach for generating diverse solutions tailored to complex scenarios such as risk assessment, urban design, and game evaluation, where varied planning outcomes are essential. By explicitly modeling diversity within the planning process and supporting multiple objective types, the method enables the production of heterogeneous yet purposeful plans. The study pioneers the application of the behavior planning paradigm across three distinct domains—narrative generation, urban planning, and game critique—and demonstrates its cross-domain adaptability and practical utility through case studies and qualitative evaluations. Beyond advancing the theoretical foundations of diversity-aware planning, this research establishes a reusable framework and evaluation methodology for developing intelligent systems capable of producing diverse, context-sensitive solutions.
📝 Abstract
The primary objective of a diverse planning approach is to generate a set of plans that are distinct from one another. Such an approach is applied in a variety of real-world domains, including risk management, automated stream data analysis, and malware detection. More recently, a novel diverse planning paradigm, referred to as behaviour planning, has been proposed. This approach extends earlier methods by explicitly incorporating a diversity model into the planning process and supporting multiple planning categories. In this paper, we demonstrate the usefulness of behaviour planning in real-world settings by presenting three case studies. The first case study focuses on storytelling, the second addresses urban planning, and the third examines game evaluation.