Behaviour Planning: A Toolkit for Diverse Planning
This work addresses the need for more flexible and explainable diverse planning in applications like risk management and malware detection, representing an incremental advancement with novel method integration.
The paper tackles the limited expressiveness and explanatory power of existing diverse planning methods by introducing a novel approach using an n-dimensional grid representation for customizable diversity models, resulting in a toolkit called Behaviour Planning that significantly outperforms current methods in generating diverse plans.
Diverse planning approaches are utilised in real-world applications like risk management, automated streamed data analysis, and malware detection. The current diverse planning formulations encode the diversity model as a distance function, which is computational inexpensive when comparing two plans. However, such modelling approach limits what can be encoded as measure of diversity, as well as the ability to explain why two plans are different. This paper introduces a novel approach to the diverse planning problem, allowing for more expressive modelling of diversity using a n-dimensional grid representation, where each dimension corresponds to a user-defined feature. Furthermore, we present a novel toolkit that generates diverse plans based on such customisable diversity models, called \emph{Behaviour Planning}. We provide an implementation for behaviour planning using planning-as-satisfiability. An empirical evaluation of our implementation shows that behaviour planning significantly outperforms the current diverse planning method in generating diverse plans measured on our new customisable diversity models. Our implementation is the first diverse planning approach to support planning categories beyond classical planning, such as over-subscription and numerical planning.