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

fact, we would expand nodes by judiciously selecting the more promising
nodes, where these nodes are identified by measuring their strength compared
to their competitive counterparts with the help of specialized intuitive
functions, called heuristic functions.
Heuristic search is generally employed for two distinct types of
problems: i) forward reasoning and ii) backward reasoning. We have already
discussed that in a forward reasoning problem we move towards the goal state
from a pre-defined starting state, while in a backward reasoning problem, we
move towards the starting state from the given goal. The former class of
search algorithms, when realized with heuristic functions, is generally called
heuristic Search for OR-graphs or the Best First search Algorithms. It may be
noted that the best first search is a class of algorithms, and depending on the
variation of the performance measuring function it is differently named. One
typical member of this class is the algorithm A*. On the other hand, the
heuristic backward reasoning algorithms are generally called AND-OR graph
search algorithms and one ideal member of this class of algorithms is the
AO* algorithm. We will start this section with the best first search algorithm.
4.3.1 Heuristic Search for OR Graphs
Most of the forward reasoning problems can be represented by an OR-graph,
where a node in the graph denotes a problem state and an arc represents an
application of a rule to a current state to cause transition of states. When a
number of rules are applicable to a current state, we could select a better state
among the children as the next state. We remember that in hill climbing, we
ordered the promising initial states in a sequence and examined the state
occupying the beginning of the list. If it was a goal, the algorithm was
terminated. But, if it was not the goal, it was replaced by its offsprings in any
order at the beginning of the list. The hill climbing algorithm thus is not free
from depth first flavor. In the best first search algorithm to be devised
shortly, we start with a promising state and generate all its offsprings. The
performance (fitness) of each of the nodes is then examined and the most
promising node, based on its fitness, is selected for expansion. The most
promising node is then expanded and the fitness of all the newborn children is
measured. Now, instead of selecting only from the generated children, all the
nodes having no children are examined and the most promising of these
fringe nodes is selected for expansion. Thus unlike hill climbing, the best
first search provides a scope of corrections, in case a wrong step has been
selected earlier. This is the prime advantage of the best first search algorithm
over hill climbing. The best first search algorithm is formally presented
below.