Reasoning Robots: The Art and Science of Programming Robotic Agents

334 Pages·2005·8.89 MB·English

by  Michael Thielscher

TLFeBOOK
TLFeBOOK
Reasoning Robots
APPLIED LOGIC SERIES
VOLUME 33
Managing Editor
Dov M. Gabbay, Department of Computer Science, King’s College, London,
U.K.
Co-Editor
Jon Barwise†
Editorial Assistant
Jane Spurr,Department of Computer Science, King’s College, London, U.K.
SCOPE OF THE SERIES
Logic is applied in an increasingly wide variety of disciplines, from the traditional subjects
of philosophy and mathematics to the more recent disciplines of cognitive science, computer
science, artificial intelligence, and linguistics, leading to new vigor in this ancient subject.
Kluwer, through its Applied Logic Series, seeks to provide a home for outstanding books and
research monographs in applied logic, and in doing so demonstrates the underlying unity and
applicability of logic.
The titles published in this series are listed at the end of this volume.
Reasoning Robots
The Art and Science of
Programming Robotic Agents
by
MICHAELTHIELSCHER
Technische Universität Dresden, Germany
AC.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 10 1-4020-3068-1 (HB)
ISBN 10 1-4020-3069-X (e-book)
ISBN 13 978-1-4020-3068-1 (HB)
ISBN 13 978-1-4020-3069-X (e-book)
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Contents
Preface ix
1 Special Fluent Calculus 1
1.1 Fluents and States . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Actions and Situations . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 State Update Axioms . . . . . . . . . . . . . . . . . . . . . . . . 15
1.4 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 22
1.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2 Special FLUX 25
2.1 The Kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2 Specifying a Domain . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.3 Control Programs. . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.4 Exogenous Actions . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.5 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 55
2.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3 General Fluent Calculus 59
3.1 Incomplete States . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.2 Updating Incomplete States . . . . . . . . . . . . . . . . . . . . . 64
3.3 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 70
3.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4 General FLUX 75
4.1 Incomplete FLUX States . . . . . . . . . . . . . . . . . . . . . . . 75
4.2 FLUX Constraint Solver∗ . . . . . . . . . . . . . . . . . . . . . . 78
4.3 Correctness of the Constraint Solver∗ . . . . . . . . . . . . . . . 87
4.4 Updating Incomplete FLUX States . . . . . . . . . . . . . . . . . 90
4.5 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 98
4.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5 Knowledge Programming 103
5.1 Representing State Knowledge . . . . . . . . . . . . . . . . . . . 104
5.2 Inferring Knowledge in FLUX . . . . . . . . . . . . . . . . . . . . 107
vi CONTENTS
5.3 Knowledge Update Axioms . . . . . . . . . . . . . . . . . . . . . 111
5.4 Specifying a Domain in FLUX . . . . . . . . . . . . . . . . . . . 121
5.5 Knowledge Agent Programs . . . . . . . . . . . . . . . . . . . . . 130
5.6 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 138
5.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
6 Planning 143
6.1 Planning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 144
6.2 Plan Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.3 Planning with Complex Actions . . . . . . . . . . . . . . . . . . . 152
6.4 Conditional Planning . . . . . . . . . . . . . . . . . . . . . . . . . 157
6.5 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 169
6.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
7 Nondeterminism 173
7.1 Uncertain Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
7.2 Dynamic Fluents . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
7.3 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 187
7.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
8 Imprecision∗ 191
8.1 Modeling Imprecise Sensors . . . . . . . . . . . . . . . . . . . . . 192
8.2 Modeling Imprecise Effectors . . . . . . . . . . . . . . . . . . . . 197
8.3 Hybrid FLUX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
8.4 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 208
8.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
9 Indirect Effects: Ramification Problem∗ 211
9.1 Causal Relationships . . . . . . . . . . . . . . . . . . . . . . . . . 213
9.2 Inferring Ramifications of Actions. . . . . . . . . . . . . . . . . . 220
9.3 Causality in FLUX . . . . . . . . . . . . . . . . . . . . . . . . . . 230
9.4 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 239
9.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
10 Troubleshooting: Qualification Problem 243
10.1 Accidental Action Failure . . . . . . . . . . . . . . . . . . . . . . 244
10.2 Preferred Explanations . . . . . . . . . . . . . . . . . . . . . . . . 253
10.3 Troubleshooting in FLUX . . . . . . . . . . . . . . . . . . . . . . 257
10.4 Persistent Qualifications . . . . . . . . . . . . . . . . . . . . . . . 262
10.5 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 269
10.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
11 Robotics 273
11.1 Control Architectures . . . . . . . . . . . . . . . . . . . . . . . . 273
11.2 Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275
11.3 Navigation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
CONTENTS vii
11.4 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . . . . . 282
11.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
A FLUX Manual 285
A.1 Kernel Predicates . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
A.2 User-Defined Predicates . . . . . . . . . . . . . . . . . . . . . . . 297
Bibliography 313
Index 325
Preface
The creation of intelligent robots is surely one of the most exciting and chal-
lenging goals of Artificial Intelligence. A robot is, first of all, nothing but an
inanimate machine with motors and sensors. In order to bring life to it, the
machineneedstobeprogrammedsoastomakeactiveuseofitshardwarecom-
ponents. This turns a machine into an autonomous robot. Since about the mid
nineties of the past century, robot programming has made impressive progress.
State-of-the-art robots are able to orient themselves and move around freely in
indoor environments or negotiate difficult outdoor terrains, they can use stereo
vision to recognize objects, and they are capable of simple object manipulation
with the help of artificial extremities.
At a time where robots perform these tasks more and more reliably, we are
ready to pursue the next big step, which is to turn autonomous machines into
reasoning robots. Areasoningrobotexhibitshighercognitivecapabilitieslike
followingcomplexandlong-termstrategies,makingrationaldecisionsonahigh
level, drawing logical conclusions from sensor information acquired over time,
devising suitable plans, and reacting sensibly in unexpected situations. All of
these capabilities are characteristics of human-like intelligence and ultimately
distinguish truly intelligent robots from mere autonomous machines.
What are Robotic Agents?
AfundamentalparadigmofArtificialIntelligencesaysthathigherintelligenceis
grounded in a mental representation of the world and that intelligent behavior
is the result of correct reasoning with this representation. A robotic agent is
a high-level control program for a robot—or, for that matter, for a proactive
softwareagent—inwhichsuchmentalmodelsareemployedtodrawlogicalcon-
clusions about the world. Intelligent robots need this technique for a variety of
purposes:
(cid:120) Reasoning about the current state.
What follows from the current sensor input in the context of the
world model?
(cid:120)(cid:3)Reasoning about action preconditions.
Which actions are currently possible?
x PREFACE
(cid:120) Reasoning about effects.
What holds after an action has been taken?
(cid:120) Planning.
What needs to be done in order to achieve a given goal?
(cid:120) Intelligent troubleshooting.
What went wrong and why, and what could be done to recover?
Research on how to design an automatic system for reasoning about actions
has a long history in Artificial Intelligence. The earliest formal model for the
ability of humans to solve problems by reasoning has been the so-called situa-
tion calculus, whose roots go back to the early sixties. In the late sixties this
model has been used to build an automatic problem solver. However, this first
implementation did not scale up beyond domains with a small state space and
just a few actions becauseit suffered fromwhat soon became a classic in Artifi-
cial Intelligence, the so-called frame problem. In a nutshell, the challenge is to
describe knowledge of effects of actions in a succinct way so that an automatic
system can efficiently update an internal world model upon the performance
of an action. The frame problem has haunted researchers for many years, and
only in the early nineties the first satisfactory solutions have emerged. These
formal models for reasoning about actions are now being developed into actual
programming languages and systems for the design of robotic agents.
One successful approach to the frame problem is provided by a formalism
known as fluent calculus. This book is concerned with this model of rational
thought as a way to sepcify mental models of dynamic worlds and to reason
about actions on the basis of these models. Very recently the calculus has
evolved into the programming method and system FLUX, which supports the
problem-driven,top-downdesignofroboticagentswiththecognitivecapabilities
of reasoning, planning, and intelligent troubleshooting.
Why Fluent Calculus?
Fluentcalculusoriginatesintheclassicalsituationcalculus. Itprovidesthefor-
mal underpinnings for an effective and computationally efficient solution to the
fundamental frame problem. To this end, fluent calculus extends situation cal-
culusbythebasicnotionofastate,whichallowstodefineeffectsverynaturally
in terms of how an action changes the state of the world. Based on classical
predicate logic, fluent calculus is a very versatile formalism, which captures a
variety of phenomena that are crucial for robotic agents, such as incomplete
knowledge, nondeterministic actions, imprecise sensors and effectors, and indi-
recteffectsofactionsaswellasunexpectedfailureswhenanactionisperformed
in the real, unpredicatble world.