Evaluate the different types of search processes that have been used in
Artificial Intelligence. Can these processes be used to understand human
problem solving?
Evaluate the different types of search processes that have been used
in Artificial Intelligence.
Can these processes be used to understand human problem solving?
The two most fundamental concerns of AI researchers are knowledge
representation and search. Search is a problem-solving technique that
systematically explores a space of problem states, i.e., successive
and alternative stages in the problem solving process. In other words
it is 'the act of enumerating possibilities and deciding between
them.'[1] This essay will evaluate the different types of search
processes that have been used in artificial intelligence. It will then
go on to explore if and how these processes can be used to understand
human problem solving.
Problem solving is frequently referred to in terms of searching a
problem space, which consists of various states of the problem. A
state is a representation of the problem in some degree of solution.
The initial state is the initial situation of the problem solver,
while the intermediate states are the situations on the way to the
goal. The various states that the problem solver can achieve are
referred to as defining a problem space, or state space. Problem
solving operators can be conceived of as changing one state in the
space into another. The difficulty is to find some possible sequence
of operators that goes from the initial state to the goal state in the
problem space. We can conceive of the problem as a maze of states and
of the operators as paths for moving among the states. In this
concept, the solution to a problem is achieved through search, that
is, the problem solver must find an appropriate path through a maze of
states. This conception of problem solving as a search through a state
space was developed by Allen Newell and Herbert Simon of Carnegie
Mellon University and has become the dominant analysis of problem
solving.[2] In many real problems, this idea of the state space would
not really apply, as it would be inconceivably huge, but the metaphor
in thinking about the aim of search and how it operates is useful.
Problems can be represented in search trees. These are diagrams
representing the path of search, beginning at a start state or the
root of the tree and finishing at the goal state. Discussions of
problem solving often involve the use of search graphs or Search
trees. The search-space terminology is a descriptive way of
characterizing possible steps that the problem solver might take.
Before it is possible to search, operators must be acquired.
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