Planning has belonged to fundamental areas of AI since its beginning and sessions on planning are an integral part of major AI conferences. By generating activities necessary to achieve some goal, planning is also closely related to scheduling that deals with allocation of activities to scarce resources. Although the planning and scheduling communities are somehow separated, both areas have interacted more and more in recent years, especially when dealing with real-life problems. This FLAIRS special track attempts to make the conference attractive for the planning community, a traditional part of the AI family, and also the scheduling community -- especially for those using AImotivated solving techniques such as constraint satisfaction. FLAIRS 2008 hosted the first special track on AI planning and scheduling.
A model of story generation recently proposed by Riedl and Young casts it as planning, with the additional condition that story characters behave intentionally. This means that characters have perceivable motivation for the actions they take. I show that this condition can be compiled away (in more ways than one) to produce a classical planning problem that can be solved by an off-the-shelf classical planner, more efficiently than by Riedl and Young's specialised planner.
Talamadupula, Kartik (Arizona State University) | Kambhampati, Subbarao (Arizona State University) | Hu, Yuheng (Arizona State University) | Nguyen, Tuan Anh (Arizona State University) | Zhuo, Hankz Hankui (Sun Yat-sen University, Guangzhou, China)
An important application of human computation is crowdsourced planning and scheduling. In this paper, we present an architecture for an automated system that can significantly improve the effectiveness of the crowd in collaborating and coming up with effective plans by herding it. We define two main problems that have to be solved when designing such automated crowd-herding systems: interpretation, and steering; and discuss how automated planning techniques can be used to solve these problems.
In this commentary I argue that although PDDL is a very useful standard for the planning competition, its design does not properly consider the issue of domain modeling. Hence, I would not advocate its use in specifying planning domains outside of the context of the planning competition. Rather, the field needs to explore different approaches and grapple more directly with the problem of effectively modeling and utilizing all of the diverse pieces of knowledge we typically have about planning domains.