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Reasoning Robots: The Art and Science of Programming Robotic Agents

Michael Thielscher

Resumen/Descripción – provisto por la editorial

No disponible.

Palabras clave – provistas por la editorial

Philosophy of Technology; Programming Techniques; Artificial Intelligence (incl. Robotics)

Disponibilidad
Institución detectada Año de publicación Navegá Descargá Solicitá
No detectada 2005 SpringerLink

Información

Tipo de recurso:

libros

ISBN impreso

978-1-4020-3068-0

ISBN electrónico

978-1-4020-3069-7

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Información sobre derechos de publicación

© Springer 2005

Cobertura temática

electronica

Ingeniería eléctrica, electrónica e informática

filosofia

Filosofía, ética y religión

Tabla de contenidos

doi: 10.1007/1-4020-3069-X_1

Special Fluent Calculus

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 1-24

doi: 10.1007/1-4020-3069-X_2

Special FLUX

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 25-58

doi: 10.1007/1-4020-3069-X_3

General Fluent Calculus

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 59-73

doi: 10.1007/1-4020-3069-X_4

General FLUX

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 75-101

doi: 10.1007/1-4020-3069-X_5

Knowledge Programming

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 103-142

doi: 10.1007/1-4020-3069-X_6

Planning

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 143-171

doi: 10.1007/1-4020-3069-X_7

Nondeterminism

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 173-189

doi: 10.1007/1-4020-3069-X_8

Imprecision*

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 191-210

doi: 10.1007/1-4020-3069-X_9

Indirect Effects: Ramification Problem*

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 211-242

doi: 10.1007/1-4020-3069-X_10

Troubleshooting: Qualification Problem

Michael Thielscher

In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.

Pp. 243-272