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

issues: first ‘what to search’ and secondly ‘where to search’. The first one is
generally referred to as ‘the key’, while the second one is termed ‘search
space’. In AI the search space is generally referred to as a collection of states
and is thus called state space. Unlike common search space, the state space in
most of the problems in AI is not completely known, prior to solving the
problem. So, solving a problem in AI calls for two phases: the generation of
the space of states and the searching of the desired problem state in that space.
Further, since the whole state space for a problem is quite large, generation of
the whole space prior to search may cause a significant blockage of storage,
leaving a little for the search part. To overcome this problem, the state space
is expanded in steps and the desired state, called “the goal”, is searched after
each incremental expansion of the state space.
Depending on the methodology of expansion of the state space and
consequently the order of visiting the states, search problems are differently
named in AI. For example, consider the state space of a problem that takes the
form of a tree. Now, if we search the goal along each breadth of the tree,
starting from the root and continuing up to the largest depth, we call it
breadth first search. On the other hand, we may sometimes search the goal
along the largest depth of the tree, and move up only when further traversal
along the depth is not possible. We then attempt to find alternative offspring
of the parent of the node (state) last visited. If we visit the nodes of a tree
using the above principles to search the goal, the traversal made is called
depth first traversal and consequently the search strategy is called depth first
search. We will shortly explore the above schemes of traversal in a search
space. One important issue, however, needs mention at this stage. We may
note that the order of traversal and hence search by breadth first or depth first
manner is generally fixed by their algorithms. Thus once the search space,
here the tree, is given, we know the order of traversal in the tree. Such types
of traversal are generally called ‘deterministic’. On the other hand, there exists
an alternative type of search, where we cannot definitely say which node will
be traversed next without computing the details in the algorithm. Further, we
may have transition to one of many possible states with equal likelihood at an
instance of the execution of the search algorithm. Such a type of search, where
the order of traversal in the tree is not definite, is generally termed ‘nondeterministic’
1 . Most of the search problems in AI are non-deterministic. We
will explore the details of both deterministic and non-deterministic search in
this chapter.
1 There exists also a third variety, called stochastic (random) search, where
random numbers are used to select the order of visiting the states in the search
space. The execution of such search algorithms twice at a given iteration need
not necessarily select the same state in the next visit.