Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

improve reliability by realizing fragments of computational models onto a
number of software or hardware modules.
1.6.4 Open System
The design of an intelligent system should be made, so that it can be readily
extended for spatio-temporal changes in the environment. For example, one
should use a dynamic knowledge base, which can automatically acquire
knowledge from its environment. Further, the learning systems should adapt
their parameters to take into account the new problem instances. An open
system also allows hardware/software interfacing with the existing system.
1.7 Programming Languages for AI
Generally relational languages like PROLOG [38] or functional languages like
LISP are preferred for symbolic computation in AI. However, if the program
requires much arithmetic computation (say, for the purpose of uncertainty
management) then procedural language could be used. There is a dilemma
between the choice of programming languages for solving AI problems to
date. A procedural language that offers a call for a relational function or a
relational language that allows interface with a procedural one is probably the
best choice. Currently, a number of shells (for ES) are available, where the
user needs to submit knowledge only and the shell offers the implementation
of both numeric as well as symbolic processing simultaneously.
1.8 Architecture for AI Machines
During the developmental phase of AI, machines used for conventional
programming were also used for AI programming. However, since AI
programs deal more with relational operators than number crunching, the need
for special architectures for the execution of AI programs was felt. Gradually,
it was discovered that due to non-determinism in the AI problems, it supports
a high degree of concurrent computing. The architecture of an AI machine thus
should allow symbolic computation in a concurrent environment. Further, for
minimizing possible corruption of program resources (say variables or
procedures), concurrent computation may be realized in a fine grain distributed
environment. Currently PROLOG and LISP machines are active areas of AI
research, where the emphasis is to incorporate the above issues at the hardware
and software levels. Most of these architectures are designed for research
laboratories and are not available in the open commercial market to date. We
hope for a better future for AI, when these special architectures will find
extensive commercial exploitation.