Practical Planning Systems
Steve Chien and Brian Drabble
Automated planning is the generation of a low-level sequence of actions
to achieve some desired world state while obeying domain
constraints. Planning systems can be used to automate procedure
generation problems and have been applied to such diverse fields as
science data analysis, image processing, crisis response, space payload
operations, and operating a network of communications
antennas. Automated planning technology has the potential to reduce
operations costs, decrease manual errors, and reduce dependency on key
This tutorial will cover the basic concepts in domain-independent
artificial intelligence planning including: search, representing
planning knowledge, plan and state space planning, operator- based
planning and hierarchical task network planning. Advanced concepts such
as planning and scheduling, decision theoretic planning, and mixed
initiative planning will also be briefly discussed.
Important questions relevant to planning will be covered in the tutorial
The tutorial will be broadly beneficial to a number of different
groups, in particular:
- Are planning techniques applicable to my problem?
- If so, what are the most appropriate planning representations and
techniques to use?
- How to acquire, verify, and maintain, my planning knowledge base?
- How to embed a planning system into an operational setting?
- AI practitioners seeking a thorough overview of the state-of the
art in AI planning technology and key issues in fielding
- Planning and related AI researchers seeking an overview of the
current state of the art in AI planning and insights into key
bottlenecks in fielding AI planning systems.
Knowledge of basic concepts from Artificial Intelligence will be
presumed : search, expert systems, logic-like
representations. Familiarity with some planning and scheduling systems,
basic search strategies, reactive systems, and/or scripting languages
would be helpful but not essential.
About the Lecturers
is Technical Group Supervisor of the Artificial Intelligence Group, at
the Jet Propulsion Laboratory, California Institute of Technology where
he leads efforts in automated planning and scheduling for spacecraft
mission planning, maintenance of space transportation systems, and Deep
Space Network Antenna operations. He holds a B.S., M.S., and Ph.D. in
Computer Science, all from the University of Illinois. Dr. Chien is also
an Adjunct Assistant Professor in Computer Science at the University of
is a Research Associate at the Computational Intelligence Research
Laboratory (CIRL) at the University of Oregon. His current
responsibilities are to transition the planning and scheduling research
being undertaken at CIRL into industry, commerce and military
applications. Previous to joining CIRL he spent 8 years as a member of
the Artificial Intelligence Applications Institute at the University of
Edinburgh. His responsibilities included being project leader and
co-principal investigator on the O-Plan project which is part of the $66
million DARPA/Rome Laboratory Planning and Scheduling Initiative. In
addition he has worked with a number of clients including Toshiba,
Hitachi, European Space Agency, and the British Government, to bring
intelligent planning and scheduling into their organisations and
products. Brian Drabble holds a B.Sc. from Staffordshire University and
a Ph.D. from Aston University both in the U.K
Last modified: Thu Feb 20 12:09:53 JST 1997