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Reasoning Robots: The Art and Science of Programming Robotic Agents (Applied Logic Series)
 
 
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Reasoning Robots: The Art and Science of Programming Robotic Agents (Applied Logic Series) [Hardcover]

Michael Thielscher (Author)

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Book Description

October 6, 2005 1402030681 978-1402030680 1
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.

Editorial Reviews

Review

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)

From the Back Cover

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.

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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
knowledge update axiom, state update axioms, fluent calculus signature, mail delivery world, resulting knowledge state, clausal definition, cleaning robot domain, boiler domain, domain axiomatization, different computation trees, kernel predicate, imprecise sensing, exogenous actions, imprecise sensors, personal software assistant, initial knowledge state, foundational axioms, auxiliary axioms, ground fluents, sensing result, precondition axioms, constraint handling rules, steam output, internal world model, initial state specification
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Knows Val, Wumpus World, Constraint Handling Rules, Valve Open, Open Valve, Physical Agent Figure
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