| Associative Learning for a Robot Intelligence Contributor(s): Andreae, John H. (Author) |
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ISBN: 186094132X ISBN-13: 9781860941320 Publisher: Imperial College Press OUR PRICE: $80.75 Product Type: Hardcover Published: September 1998 Annotation: "provides a number of implementation details that will be helpful to anyone following up on PURR-PUSS (PP). The book has an excellent index and a substantial bibliography".Computing Reviews, 1999 |
| Additional Information |
| BISAC Categories: - Computers | Intelligence (ai) & Semantics - Technology & Engineering | Robotics - Computers | Computer Science |
| Dewey: 629.892 |
| LCCN: 98026352 |
| Series: Artificial Intelligence |
| Physical Information: 0.95" H x 8.86" W x 6.36" (1.38 lbs) 360 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: The explanation of brain functioning in terms of the association of ideas has been popular since the 17th century. Recently, however, the process of association has been dismissed as computationally inadequate by prominent cognitive scientists. In this book, a sharper definition of the term "association" is used to revive the process by showing that associative learning can indeed be computationally powerful. Within an appropriate organization, associative learning can be embodied in a robot to realize a human-like intelligence, which sets its own goals, exhibits unique unformalizable behaviour and has no hidden homunculi.Some believe that artificial intelligence is undergoing a paradigm shift. There are undoubtedly several competing ideas and ideals. Neural networks and dynamic systems are offered as alternatives to the information processing and digital computer models of the brain. One is asked to decide between symbolic and subsymbolic, between algorithmic and nonalgorithmic, and between information processing and interactive systems. Even in the short distance travelled in this book, associative learning is seen to embrace both sides of these dichotomies. |