Abstract
In the previous chapters we looked at the difficulties associated with sensory processing and now we turn our attention to the design and construction of knowledge bases which form the heart of intelligent systems. Such knowledge bases contain information derived from many sources, including sensors, and are used to support reasoning and planning processes. A knowledge base is a store of information but should not be confused with a database. Knowledge bases record data but are also able to manipulate, refine, modify and create data. Consequently, they are not just passive stores of facts but involve processing and self-modification.
What is all knowledge too but recorded experience, and a product of history; of which, therefore, reasoning and belief, no less than action and passion, are essential materials?
Thomas Carlyle
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Further reading material
Basic material on the representation methods described here can be found in many AI textbooks; the general references cited in Chapter 1 are recommended.
Avaluable collection of classic papers is Readings in Knowledge Representation, edited by R. J. Brachman and H. J. Levesque (Morgan Kaufmann, 1985).
A special issue of the journal IEEE Computer (October 1983), volume 16 number 10, was revised and reprinted as a book: The Knowledge Frontier: Essays in the Representation of Knowledge, edited by Nick Cercone and Gordon McCalla (Springer-Verlag, 1987).
Areview of the nature of knowledge and the important features that concern AI is: `Knowledge Representation: Features of Knowledge’, by James Delgrande and John Mylopoulos in Lecture Notes in Computer Science, number 232, edited by W. Bibel and Ph. Jorrand (Springer-Verlag, 1986 ).
In an attempt to improve representation schemes to handle considerations like necessity, possibility, belief, time, and relative knowledge, AI workers have explored the potential of some of the more exotic non-standard systems of logic. Logics for Artificial Intelligence by R. Turner (Ellis Horwood Ltd., 1984), is a good review text giving details of modal logics, temporal logics and fuzzy logic.
The original frames idea is described in a readable article by Marvin Minsky entitled `A Framework for Representing Knowledge’, in The Psychology of Computer Vision, edited by P. H. Winston (McGraw-Hill, 1975).
Acollection of papers on production system topics is Pattern-Directed Inference Systems, edited by D. A. Waterman and F. Hayes-Roth (Academic Press, 1978).
Readers interested in AI programming languages should consult Ivan Bratko’s excellent book Prolog Programming for Artificial Intelligence (Addison-Wesley, 1986). A good starting point for LISP programmers is Common Lisperaft by R. Wilensky (W.W. Norton, 1986).
Rights and permissions
Copyright information
© 1989 Mark H. Lee
About this chapter
Cite this chapter
Lee, M.H. (1989). Building a knowledge base. In: Intelligent robotics. Open University Press Robotics Series. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-6237-1_5
Download citation
DOI: https://doi.org/10.1007/978-1-4684-6237-1_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4684-6239-5
Online ISBN: 978-1-4684-6237-1
eBook Packages: Springer Book Archive