Intelligent Planning: A Decomposition and Abstraction Based Approach

260 Pages·1997·11.855 MB·English

by  Qiang Yang (auth.)

Artificial Intelligence
Editors: S. Amarel A. Biermann L. Bole P. Hayes A. Joshi
D. Lenat D. W. Loveland A. Mackworth D. N au R. Reiter
E. Sandewall S. Shafer Y.Shoham J. Siekmann W Wahlster
Springer
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QiangYang
Intelligent Planning
A Decomposition and Abstraction
Based Approach
Foreword by Martha Pollack
Springer
Professor Qiang Yang
Simon Fraser University
School of Computing Science
Ebco/Epic NSERC Industrial Chair
Burnaby, British Columbia
Canada V5A IS6
With 76 Figures and 49 Tables
Library of Congress Cataloging-in-Publication Data
Yang, Qiang, 1961- Intelligent Planning: a decomposition and abstraction based
approach / Qiang Yang; foreword by Martha Pollack. p. cm. - (Artificial intelligence)
Includes bibliographical references and index.
ISBN 978-3-642-64477-1 (hardcover: alk. paper).
I. Artificial intelligence - Congresses. 2. Planning - Data processing congresses. I. Title.
II. Series: Artificial intelligence (Berlin, Germany)
Q334.Y36 1997 006.3-dc21 96-29507 CIP
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ISBN-13: 978-3-642-64477-1 e-ISBN-13: 978-3-642-60618-2
001: 10.1007/978-3-642-60618-2
© Springer-Verlag Berlin Heidelberg 1997
Softcover reprint of the hardcover 1st edition 1997
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Foreword
"The central fact is that we are planning agents."
(M. Bratman, Intentions, Plans, and Practical Reasoning, 1987, p. 2)
Recent arguments to the contrary notwithstanding, it seems to be the case
that people-the best exemplars of general intelligence that we have to date
do a lot of planning. It is therefore not surprising that modeling the planning
process has always been a central part of the Artificial Intelligence enterprise.
Reasonable behavior in complex environments requires the ability to consider
what actions one should take, in order to achieve (some of) what one wants
and that, in a nutshell, is what AI planning systems attempt to do.
Indeed, the basic description of a plan generation algorithm has remained
constant for nearly three decades: given a desciption of an initial state I, a
goal state G, and a set of action types, find a sequence S of instantiated
actions such that when S is executed instate I, G is guaranteed as a result.
Working out the details of this class of algorithms, and making the elabora
tions necessary for them to be effective in real environments, have proven to
be bigger tasks than one might have imagined.
Initially, plan formation was approached as a formal process, in particular,
in Green's work on planning as theorem proving [59]. But beginning in the
early 1970s, the focus of the planning community shifted to system develop
ment, starting with the STRIPS planning system, which was used, amongst
other things, to enable Shakey-the-Robot to form plans to push blocks around
the halls of SRI International. STRIPS was followed by a series of ever larger,
more complex, and, alas, often more ad hoc planning systems. A major break
occurred in the late 1980s, marked by the publication of three key papers:
Chapman's paper on the TWEAK formalism [28], Pednault's paper on the
ADL formalism [104]' and McAllester and Rosenblitt's paper on the SNLP
algorithm [94]. These papers were intended not to add functionality to known
planning methods, but rather to capture the essential elements of these known
methods in a readily analyzable fashion. As such, they signaled the begin
ning of an effort, still ongoing within the planning community, to address the
planning problem more systematically, giving greater care to analyzing the
relationships among alternative representations and algorithms.
VI Foreword
With this shift, the field of planning was ready for a comprehensive book
on planning that gives the "lay ofthe land", showing what methods exist, how
they are related, and what their relative strengths and weaknesses are. In
telligent Planning: A Decomposition and Abstraction Based Approach is that
book. General AI textbooks can typically devote only a chapter or two to the
topic of planning, and so can give only a suggestion of the range of issues that
arise in plan generation, and of the range of solutions that have been pro
posed in response. In contrast, this book explores the issues and the solutions
in depth, giving a careful and thorough analysis of each. It takes the perspec
tive that effective planning relies on two techniques that are fundamental in
computer science-decomposition (also often known as divide-and-conquer)
and hierarchical abstraction-and it uses this perspective to structure the
material very effectively.
Comprehensive monographs are already available for several other subar
eas of AI, such as natural-language processing [4] and machine learning [86].
These books have played an important role, bringing together the major ideas
of their respective areas to provide a solid platform on which further research
can be based. Intelligent Planning: A Decomposition and Abstraction Based
Approach will do the same for AI planning. As a textbook, it will doubtless be
a valuable resource for graduate students and their professors. It will also be a
valuable resource for researchers actively working in the field of AI planning,
and those in other areas who need to know about AI planning, as it provides
ready access to the basic computational tools-representations, algorithms,
and analyses-on which further research into the nature of planning will rely.
Martha Pollack
Preface
Those who triumph
Plan at their headquarters
Considering many conditions
Prior to a challenge.
Those who are defeated
Plan at their headquarters
Considering few conditions
Prior to a challenge.
Much planning brings triumph
Little planning brings defeat
How much more so
With no planning at all!
Observing a planning process
I can see triumph and defeat.
Sun Tzu [400-320 B.C., China], The Art of Strategyl, Chapter One
Planning has captivated human interest for generations. The above quote,
translated from a classical Chinese text on the art of strategy, underscores
this fascination. To live our lives, we have to deal with a huge number of
problems, many of which require careful planning. However, until recently
the problem of how to plan has not been a subject of systematic study.
This situation, however, has completely changed with the dramatic progress
of computer technology and the enormous success of Artificial Intelligence
(AI).
This book is a monograph on Artificial Intelligence Planning (AI Plan
ning), an active research and applications field for the past several decades.
As a research field, planning can be defined broadly as the study of actions
and changes, covering topics concerning action and plan representation, plan
synthesis and reasoning, analysis of planning algorithms, plan execution and
monitoring, plan reuse and learning. Lately there has been a dramatic in
crease of interest in automatic and semi-automatic planning in AI and other
related fields. It has been demonstrated that research in planning is of great
importance to most subfields of AI as well as general computer science, en
gineering, management science, and cognitive science.
The quotes in this book are adapted from an excellent translation by R.L. Wing
1
[137]. The book is also known as The Art of War.
VIII Preface
Features
To cover the entire field in a single volume is impossible. In writing the book,
I have chosen to focus on a clear, thorough coverage of key areas of classical
AI planning. Classical AI planning is concerned mainly with the generation
of plans to achieve a set of pre-defined goals in situations where most relevant
conditions in the outside world are known, and where the plan's success is
not affected by changes in the outside world. As we will see, the planning
task is extremely hard even under these situations.
The book's main purpose is to build more intelligence OIl a set of ba
sic methods for reasoning about and generating plans. This is done by first
explaining these basic methods, then developing advanced techniques to en
hance them using problem decomposition and abstraction. In addition, the
book presents techniques for analyzing and comparing planning algorithms.
In order to be accessible to readers from a wide variety of backgrounds,
the book takes a ground-zero approach. It begins with a gentle introduction
and is self-contained; most key algorithms and techniques can be found in the
book itself, rather than referenced from other sources. As a result, it requires
minimal preconditions on the part of the reader and will benefit not only
seasoned researchers, but undergraduate and graduate students, as well as
researchers in other related fields such as mechanical engineering, business
administration and software project management. Many useful algorithms
and techniques are compiled in a single volume, expressed in a common syn
tactic framework. Many illustrations, examples, algorithms, analyses, tables,
and references help make the explanation clear. Each chapter ends with a
background survey of the current state of research, the source of the material
under discussion, and an exploration of open problems.
Contents and Intended Audience
The book consists of three main parts. Part I, "Representation, Basic Algo
rithms and Analytical Techniques," lays the foundation. This part provides
a general introduction to plan representation, generation, and analysis. It
reviews past and current representations and algorithms in AI planning and
general computer science that are basic but foundational.
Parts II and III of the book present my own contribution to planning in
the past decade. Both parts develop advanced planning techniques that are
based on the basic planning algorithms and methods from Part 1. Part II,
"Problem Decomposition and Solution Combination," presents a complete
suite of analysis and algorithm tools for decomposition planning. Decom
position planning refers to the task of breaking apart a complex problem
into smaller pieces and solving them individually, and later combining their
solutions into a global solution.
Preface IX
Part III is entitled "Hierarchical Abstraction," and presents a theory of
plan generation using the idea of abstraction. Abstraction refers to the task
of solving a problem by tackling the most important components first, then
using the "skeleton" solution as a guide to solve the rest of the problem.
This part presents analysis, comparisons between planning with and without
abstraction, methods for generating an abstraction hierarchy automatically,
properties of hierarchical task network (HTN) planning, and effect abstrac
tion.
This book can be used as a one-semester or one-quarter textbook in an
Introduction to AI course, or an AI Planning course. It can also be used as
a reference book for graduate seminar courses. As a teaching resource, it can
be used in both graduate or undergraduate courses. As a reference resource,
it can be used by researchers and practitioners in AI, computer science, en
gineering, and other related fields. Knowledge of basic data structures, logic,
and algorithm analysis would be helpful, but is not strictly required. Any
programming experience would also be very useful.
Acknowledgment
The book surveys the field of planning and reports work resulting from a
long and enjoyable collaboration with many colleagues and students. Many
people helped enrich my view of planning during my graduate studies at the
University of Maryland, in the USA, and during my tenure at the University
of Waterloo and Simon Fraser University in Canada. I would like to thank my
former supervisors Dana S. Nau and Jim Hendler, who sparked my interest in
the field of planning and collaborated with me on many challenging problems.
Thanks also to my former students at Waterloo whose talent and enthusiasm
motivated me throughout my research and teaching career: Steven Woods,
Eugene Fink, Philip Fong, Alex Chan, Cheryl Murray, Stephanie Ellis, and
to my research associates at Simon Fraser University Edward Kim and Toby
Donaldson. Among my close colleagues, I'd like to express my special thanks
to Ming Li, Fahiem Bacchus, Josh Tenenberg, and Craig Knoblock. Specific
mention of collaborative projects with my colleagues can be found at the end
of each chapter.
The book project received strong encouragement from Martha Pollack,
a prominent researcher in AI planning and winner of the prestigious Com
puter and Thought Award. Martha read through the manuscript carefully de
spite her busy schedule, providing many suggestions. Russ Greiner and Henry
Kautz gave many in-depth comments on selected chapters. Diane Wudel pro
vided professional editing for the entire manuscript. Thanks also to my former
supervisor Dana Nau for introducing me to the Springer-Verlag series.
I would also like to express my deepest appreciation for the support and
understanding of my parents Haishou Yang and Xiuying Li, and my wife
Jill and son Xin Xin (Andrew). Thank you Jill for tolerating my absence
X Preface
during many evenings and weekends, and for all the late suppers. Without
the support and encouragement from my family the project would never have
reached completion.
Finally, the work would not have been possible without the strong sup
port from the Natural Sciences and Engineering Research Council of Canada,
individual research grant OGP0089686, and support from the University of
Waterloo and Simon Fraser University. The book is formatted using 19\TEX.
May 1997 Qiang Yang