Practical Planning. Extending the Classical AI Planning Paradigm

203 Pages·1988·10.38 MB·English

by  David E. Wilkins (Auth.)

The MORGAN KAUFMANN Series in Representation and Reasoning
Series editor, Ronald J. Brachman (AT&T Bell Laboratories)
BOOKS
• Ronald J. Brachman and Hector J. Levesque, editors
Readings in Knoiuledge Representation (1985)
• Ernest Davis
Representations of Commonsense Knowledge (1989)
• Matthew L. Ginsberg, editor
Readings in Nonmonotonic Reasoning (1987)
• Judea Pearl
Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference (1988)
• David E. Wilkins
Practical Planning: Extending the Classical AI Planning Paradigm (1988)
PROCEEDINGS
• Principles of Knowledge Representation and Reasoning: Proceedings of the
First International Conference
edited by Ronald ]. Brachman, Hector ]. Levesque and Raymond Reiter (1989)
• The Frame Problem in Artificial Intelligence: Proceedings of the 1987 Conference
edited by Frank M. Brown (1987)
• Reasoning About Actions and Plans: Proceedings of the 1986 Workshop
edited by Michael P. Georgeff and Amy L. Lansky (1987)
• Theoretical Aspects of Reasoning About Knowledge: Proceedings of
the 1986 Conference
edited by Joseph Y Halpern (1986)
• Proceedings of the Second Conference on Theoretical Aspects of
Reasoning About Knowledge
edited by Moshe Y Vardi (1988)
Practical Planning:
Extending the Classical AI Planning Paradigm
David E. Wilkins
Artificial Intelligence Center
SRI International
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ISBN 0-934613-94-X
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93 92 91 90 89 5 4 3 2 1
Acknowledgments
Thanks to the Air Force Office of Scientific Research for supporting this
work under contracts F49620-79-C-0188 and F49620-85-K-0001. The SRI
International Artificial Intelligence Center provided the open, cooperative
environment that encourages new ideas, a state-of-the-art computing en-
vironment, and support for the implementation of the graphical interface.
Special thanks go to Stan Rosenschein, Nils Nilsson, and Michael Georgeff
for technical leadership, Marietta Elliott for administrative leadership, and
Paul Martin and Mabry Tyson for the computing environment. Ann Robin-
son helped formulate the original conception of SIPE. Michael Georgeff has
greatly influenced both the development of SIPE and the ideas expressed
in this paper, and has been instrumental in providing an excellent envi-
ronment for research and writing. Savel Kliachko has greatly improved
my writing skills and thus this book. Thanks to the Melbourne University
Computer Science Department and the Australian Artificial Intelligence
Institute for the use of their facilities during production of the final draft.
ν
List of Figures
1.1 The Classical Planning Problem 4
1.2 Features of Existing Systems 13
3.1 SIPE Modules and Flow of Control 28
3.2 SIPE's Graphical Interface 30
3.3 Representing the Initial World State 32
3.4 SIPE Fetch Robot-World Operator 34
3.5 SIPE Puton Block-World Operator 38
3.6 The Sussman Anomaly. 40
3.7 SIPE Plan Viewed from Different Perspectives. . .. 42
4.1 A Generic Plan from a Hierarchical Planner 48
4.2 Hierarchical Plan in the Robot Domain 50
4.3 Not-Yet Operator for Delaying Operator Application. 53
4.4 Original Fetch Operator 55
4.5 Fetch Operator with At Predicate 55
6.1 Overview of PTC Description 69
6.2 Effects with Variables in a Generic Plan 72
6.3 SIPE Deduce-Clear Block-World Deductive Operator. 75
6.4 Use of Existential Quantifier in Deduce-Clear 76
xi
xii List of Figures
6.5 No-Longer-Nextto Robot-World Causal Rule 79
7.1 SIPE Not-On Causal Rule and Deduce-Clear State
Rule 88
7.2 Node in Robot Plan 93
8.1 A Block-World Problem with a Helpful Interaction. 98
8.2 Plan Produced by SIPE after One Planning Level. . 99
8.3 Plan for Three-Conjunct Reverse-Stack Block-World
Problem 100
8.4 Block-World Problem with Variables 106
9.1 A Plan without Resources 117
9.2 A Plan with Resources 118
9.3 Fuel-Puton Block-World Operator using Consum-
able Resources 122
10.1 Algorithm for Producing a New Planning Level. . . 136
10.2 Major Search Control Techniques 141
11.1 Control and Data Flow in SIPE's Replanner 148
11.2 Block-World Problem and Executable Plan 151
11.3 Hierarchical Wedges with a Common Last Action. . 159
11.4 Initial World State and Colored-Block Problem. . . 162
11.5 Initial Plan Produced by SIPE 162
11.6 New Plan Produced for Continuing Execution. . . . 163
11.7 Subplan for Replacing Phantom P165 164
12.1 Structure of Control and Planning System for a Robot. 170
13.1 Update-AdjacentLoc State Rule with Efficient Con-
straints 184
13.2 Travel-Ground Operator from Travel-Planning Do-
main 186
List of Tables
1.1 Desirable Planning Capabilities 8
3.1 Representing which Operator to Apply 35
4.1 Symbolics 3600 CPU Time and Planning Levels for
Solutions 56
5.1 Categories of Constraints in SIPE 61
11.1 Current Algorithm of General Replanner 160
13.1 Efficiency Improvements from Comparing Pred Con-
straints 183
14.1 Typical SIPE Execution Times 194
xiii
CHAPTER 1
Reasoning about Actions and
Planning
Reasoning about actions is a necessary element of intelligent behavior. A
person can scarcely participate in a conversation or go to the store for
groceries without reasoning about how actions taken will affect the sur-
rounding world. In unfamiliar situations, people often invest significant
effort in deliberating about how to proceed. Decades of research in Arti-
ficial Intelligence (AI) and related disciplines have shown this particular
human capability to be extremely difficult to formalize. Reasoning about
actions is, however, an essential component of intelligent behavior, and an
essential part of future intelligent computer systems. This book describes
the continuing development of the most studied AI approach to reasoning
about actions. Before defining the task more precisely, we briefly discuss
the motivation for developing a solution.
1.1 Philosophical and Practical Importance
The automation of reasoning about actions is useful in many practical
problems with significant commercial potential. There is also the intellec-
tual and philosophical motivation of better understanding ourselves and
1
2 Reasoning about Actions and Planning
the world in which we live. The research described in this book attempts
to use knowledge to guide purposeful, intelligent behavior. Because little
is understood about the way human beings reason about actions, a model
of this human capability is not presented here. Nevertheless, studying the
ways that information is able to guide behavior increases our understanding
of the problems involved, and provides insights that may later prove useful
in understanding our interactions with the world and with each other.
Practical problems that can be solved by intelligent reasoning about ac-
tions appear everywhere. For example, in most manufacturing, there is a
trend to higher capitalization in plant, smaller batch sizes (particularly in
high-technology areas such as aerospace), and tighter controls on inven-
tories. All these trends argue for computer control of the manufacturing
process which requires reasoning about actions at almost every level, from
process routing (i.e., planning which processes/stations a part should go
through during manufacture), to setting up the job shop, to the highest-
level decisions made by management.
An example of the need for planning at lower levels is the need to re-
configure factories quickly. In competitive markets with small batch sizes,
manufacturers must be able to reconfigure in response to shifts in cus-
tomer demands for products and to contractual obligations. Mathematical-
modeling and operations-research techniques, while appropriate for long-
range predictions, cannot accomplish shorter-term tactical planning when
it is important to have a clear understanding of the interactions between
processes and the causal relationships between activities under differing cir-
cumstances. Moreover, such techniques are computationally expensive and
do not allow timely response to unanticipated changes in the situations.
With the ability to reason about actions, a system could use knowledge
about alternative process routings that might be available, the current
status of the alternative equipment, the effects of different proposed con-
figurations, the causal connections between configurations and processes,
and the rationale behind the current factory setup. This knowledge could
be used to generate plans for a reconfiguration, to detect possible problems
in such plans proposed by others, and to react to unexpected events.
Similar capabilities are similarly needed at higher levels of management.
As reduced-inventory manufacturing becomes more important, managers'
decisions have to be increasingly accurate. The ability to plan and schedule
factory operations efficiently often means the difference between making a
profit or bringing a product to the market after the demand has dropped.
Planning to use resources efficiently can reduce the cost of producing or
shipping a product. Decision makers have to respond more quickly to
unanticipated events that might disrupt the production schedule, such as
yield crashes, equipment malfunctions, or a sudden demand for a product.
A system that could reason about the effects of decisions would support
investigation of different management strategies by using "what-if analy-
ses on various scenarios. Such a system could predict which goals might
1.2 The Classical AI Planning Problem 3
fail in the future if a particular strategy were employed, and could also be
used to generate its own alternative strategies.
In a similar manner, reasoning about the effects of actions can also
solve other practical problems in business as well as the military. Accurate
planning and forecasting can keep projects on schedule and prevent cost
overruns. Forseeing the future implications of business decisions can help
companies to avoid mistakes. Planning the routing of communications
can avoid bottlenecks and overload conditions. Planning faster and more
completely than an opponent can favorably decide the outcome of a battle
or a war, or prevent a confrontation from leading to hostilities.
Within AI and computer science, the need to reason about actions is
ubiquitous. The following are a few examples of problems that require
reasoning about actions: (1) generating a plan of action for a robot, (2)
interpreting an utterance by reasoning about the goals a speaker is trying
to achieve, (3) automatically writing a program to solve a problem, (4)
producing utterances in a conversation with another agent, and (5) allo-
cating the use of resources, computational or otherwise. While some of
these problems are far from being solved in commercial applications, oth-
ers are closer. The long-term potential of solving such problems is great,
especially if an intelligent robot can be created.
If a robot is to function in a world more dynamic and complex than a
production line, it will have to reason about its own actions. A robot capa-
ble of intelligently planning actions could potentially have a large economic
impact. Reasoning robots could function in conditions that are dangerous
to humans: for example, (1) building a space station in a vacuum, (2) clean-
ing up toxic or radioactive waste, (3) constructing or retrieving underwater
objects, or (4) accomplishing missions on a battlefield. Robots would also
be valuable in safer environments: for example, running household errands,
and accomplishing storage and retrieval tasks in a warehouse.
Obviously, there is a broad range of practical problems that can ben-
efit from the ability to reason about actions. Reasoning about actions is
a key problem that must be solved if intelligent computer systems are to
make a significant leap forward in their capabilities. To advance beyond
the current expert-systems technology, intelligent systems must be able to
construct novel responses to unanticipated situations rather than finding
the prespecified rules appropriate for the situation. In most interesting do-
mains, constructing such novel responses requires reasoning about actions.
1.2 The Classical AI Planning Problem
Research on reasoning about actions is generally published and presented
as part of a subfield of AI labeled "planning." As we shall see, this sub-
field of AI is addressing an extremely difficult problem. The first attempts
at AI planning, such as the STRIPS and NOAH systems developed at
SRI International during the late sixties and early seventies, made certain