The creation of intelligent robots is surely one of the most exciting and ch- lenginggoals of Arti?cial Intelligence. A robot is, ?rst of all, nothing but an inanimate machine with motors and sensors. In order to bring life to it, the machine needs to be programmed so as to make active use of its hardware c- 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 di?cult outdoor terrains, they can use stereo vision to recognize objects, and they are capable of simple object manipulation with the help of arti?cial extremities. At a time where robots perform these tasks more and more reliably,weare ready to pursue the next big step, which is to turn autonomous machines into reasoning robots.Areasoning robot exhibits higher cognitive capabilities like following complex and long-term strategies, making rational decisions on a high 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.

From the reviews:

"This book presents the 'fluent calculus' as one possible approach to solve the frame problem in artificial intelligence. The book is very understandable and recommendable for anybody who wants to solve AI problems by means of predicate logics and logic programming. It shows that this approach is more and more applicable to the solution of real-world problems. It will be an excellent textbook for appropriate AI or robotics courses and very appropriate for graduate students." (Christian Posthoff, Zentralblatt MATH, Vol. 1092 (18), 2006)

Autorentext

Michael Genesereth is an associate professor in the Computer Science Department at Stanford University. He received his Sc.B. in Physics from M.I.T. and his Ph.D. in Applied Mathematics from Harvard University. He is best known for his research on Computational Logic and its applications. He has been teaching Logic to Stanford students and others for more than 20 years. He is the current director of the Logic Group at Stanford and founder and research director of CodeX (The Stanford Center for Legal Informatics).Michael Thielscher is a Professor and head of the Computational Logic Group at Dresden University in Germany since 1997. He received his PhD in Computer Science from Darmstadt University of Technology, Germany. His research is mainly in Knowledge Representation, Cognitive Robotics, Commonsense Reasoning, Game Playing, and Constraint Logic Programming. He has developed the action programming language and system FLUX and has published numerous papers and two books on knowledge representation for actions, on comparisons of different action languages, and on implementations of action programming systems. In 1998, his Habilitation thesis was honored with the award for research excellence by the alumni of Darmstadt University of Technology. He co-authored the program FLUXPLAYER, which in 2006 was crowned the world champion at the Second General Game Playing Competition in Boston.

Klappentext

The book provides an in-depth and uniform treatment of a mathematical

model for reasoning robotic agents. The book also contains an introduction

to a programming method and system based on this model.

The mathematical model, known as the "Fluent Calculus,'' describes how

to use classical first-order logic to set up symbolic models of dynamic

worlds and to represent knowledge of actions and their effects. Robotic

agents use this knowledge and their reasoning facilities to make decisions

when following high-level, long-term strategies. The book covers

the issues of reasoning about sensor input, acting under incomplete

knowledge and uncertainty, planning, intelligent troubleshooting, and many

other topics.

The mathematical model is supplemented by a programming method which

allows readers to design their own reasoning robotic agents. The usage of

this method, called "FLUX,'' is illustrated by many example programs. The

book includes the details of an implementation of FLUX using the standard

programming language PROLOG, which allows readers to re-implement or

to modify and extend the generic system.

The design of autonomous agents, including robots, is one of the most

exciting and challenging goals of Artificial Intelligence. Reasoning robotic

agents constitute a link between knowledge representation and reasoning on

the one hand, and agent programming and robot control on the other. The

book provides a uniform mathematical model for the problem-driven,

top-down design of rational agents, which use reasoning for decision

making, planning, and troubleshooting. The implementation of the

mathematical model by a general PROLOG program allows readers to

practice the design of reasoning robotic agents. Since all implementation

details are given, the generic system can be easily modified and extended.

Inhalt
Special Fluent Calculus.- Special FLUX.- General Fluent Calculus.- General FLUX.- Knowledge Programming.- Planning.- Nondeterminism.- Imprecision*.- Indirect Effects: Ramification Problem*.- Troubleshooting: Qualification Problem.- Robotics.