Advances in Computers, Vol. 24

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In the same vein, we would like to be sure that our program will stick to the language we have given it, and not produce answers in terms of symbols whose individual meaning we have never defined. These notions do not just relate to the language of a symbolic program, but also to the reasoning we can perform using the language. Specifically, we want our AI program to carry out some kind of reasoning. We certainly do not want the program to be able to draw conclusions which are incorrect or contradictory; and, often, we want to be sure that it is able potentially to draw every conclusion which is correct.

The best known way to achieve this is to use neural networks: systems which emulate (in a fairly loose sense) the general structure and operation of the brain. A neural network consists of a number of nodes, connected together in a certain configuration. Each node is capable of reading 32 Readings in Music and Artificial Intelligence information from those immediately connected to it, and passing on an output to other nodes in some way dependent on those inputs. Normally, the nodes are quite simple processors, and they are usually all the same; it is their multiplicity and the connections between them that allow them to work on hard problems.

A third definition is suggested by a common usage of the word "intelligent" with respect to software. An "intelligent help system", for example, is one which determines the information to be provided to the user on the basis of the user's recent activities. In other words the behaviour of the system is sensitive to its environment. This is true of every piece of software - its output it determined by its input - but here there is a significant difference in the domain of the input. Normally software uses a very restricted input; so-called intelligent software instead attempts to receive input from as much as possible of its environment.

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