Research efforts in the Automated Planning community predominantly focus on developing novel planning techniques and incorporating and/or combining them into domain-independent planning engines that can be exploited in a wide range of real-world applications (e.g., Space Exploration, Manufacturing, Urban Traffic Control). In contrast to domain-dependent approaches, where one has to develop an algorithm for solving planning problems in a specific domain, domain-independent approach provides a lot of flexibility by decoupling domain models and planning engines. For being able to exploit domain-independent planning engines, one has to develop a planning domain model which, roughly speaking, describes the environment and actions.
This tutorial focuses on audience from various areas of AI, who is interested in using of domain-independent Automated Planning engines in their research efforts. With regards to the domain modelling process, we will introduce available “machinery”, i.e., languages and knowledge engineering tools, that can be exploited, a “walk-through” of the process, and our practical experience with developing domain models for real-world applications. Attendees will get a basic understanding of the domain modelling process, tools they can exploit, and challenges they will face. A basic level of knowledge of Automated Planning is recommended (on the level of an undergraduate AI course).
- Introduction and Background
- AI Planning, formal concepts
- Planning Domain Modelling Languages and Tools
- Languages: PDDL, NDDL, ANML, HTN, Picat
- Tools: itSimple, Planning.Domains, KEWI, PDDL studio
- ICKEPS lessons
- Designing and Developing a Domain Model
- 15-puzzle, NoMystery Domain
- Road Traffic Accident Management
- Development of Real-World Planning Application
- Task Planning for Automous Underwater Vehicles
- Issues and Open Problems