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An evaluation of search processes in Artificial Intelligencec

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Introduction

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. ...read more.

Middle

Sometimes, by chance, depth-first search may find a solution without examining much of the search space at all, which contrasts with breadth-first search in which all parts of the tree must be examined to a certain level before any nodes on the next level can be examined. Saying this, breadth first search will not get trapped along a blind alley which leads to nowhere, in comparison with depth-first where the search could be following a long path to nowhere for a long time. If we consider human problem solving, it is usually a combination of depth first and breadth first search. The tendency is maybe even towards depth first search of a small part of the search space, but this is after spotting the right portion of the search space to search, which to some extent is guided by heuristics. Heuristics are rules of thumb, almost like tour guides, in that they are good to the extent of pointing in the general direction, but may miss certain paths Heuristic search uses a heuristic evaluation function, which evaluates each state. On average, they improve the quality of the paths that are explored. They are the means by which humans can perform more efficient search. Hill climbing is one heuristic search process. Sometimes referred to as 'difference reduction,' it involves the problem solver aiming to reduce the difference between the current state and the goal state. ...read more.

Conclusion

One statement that describes search and problem solving in human terms is: "Human cognition is always purposeful, directed to achieving goals and to removing obstacles to those goals."(Anderson, 1983; Newell, 1980; Tolman, 1932)5 All of the search processes that have been discussed and evaluated can be applied to human problem solving to varying extents. Possibly the most useful one, which relates most closely to human problem solving are the heuristic search processes, although there are disadvantages with some heuristic searches, for example they do not always find the optimal solution. This may not be as much of an issue as it seems though, as Simon (1981) demonstrated.6 The concepts of Heuristic search processes can be applied thoroughly to human problem solving, for example, as Polya (1957)7 discusses in his book, if you are given a problem, look for a similar problem you have solved, and ask whether you can use the solution to that problem or the method used to solve the current one. This links to heuristic search processes, which rely on already obtained knowledge and rules of thumb. Artificial Intelligence, particularly the study of search processes, has indeed helped us to understand human cognition. The search processes which have been studied can help us to understand human problem solving in particular, in that it helps us to decipher the processes which a human brain goes through in everyday problem solving instances, and how some of these thought processes rely on stored knowledge and are guided by heuristics. ...read more.

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