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

this problem is useful for the following reason. The design of the AI
algorithm should be such that the time required for solving the problem is a
polynomial (and not an exponential) function of the size (dimension) of the
problem. When the computational time is an exponential function of the
dimension of the problem, we call it a combinatorial exploration problem.
Further, the number of variables to be used for solving an AI problem should
also be minimum, and should not increase with the dimension of the
problem. A non-AI algorithm for an AI problem can hardly satisfy the above
two requirements and that is why an AI problem should be solved by an AI
approach.
{ X1 ≥ 2; X2 ≥3 ; X1 + X2 ≤ 6; X1 , X2 , X3 ∈ I
X1 = 2 X1 > 2
{ X1 =2, X2 ≥3 ; { X1 =3, X2 ≥3 ;
X1 + X2 ≤ 6; X j ∈ I, ∀j} X1 + X2 ≤ 6; Xj ∈ I, ∀j}
X2 =3 X2 >3 X2 =3 X 2 > 3
{X1 =2, X2 =3} { X1 =2, X2 =4} {X1 =3, X2 =3} No solution
Fig. 1.3: The constraint tree, where the arcs propagate the constraints, and
the nodes down the tree hold the reduced set of constraints.
1.4 The Disciplines of AI
The subject of AI spans a wide horizon. It deals with the various kinds
of knowledge representation schemes, different techniques of intelligent
search, various methods for resolving uncertainty of data and knowledge,
different schemes for automated machine learning and many others. Among
the application areas of AI, we have Expert systems, Game-playing, and
Theorem-proving, Natural language processing, Image recognition, Robotics
and many others. The subject of AI has been enriched with a wide discipline
of knowledge from Philosophy, Psychology, Cognitive Science, Computer