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.