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# A* Search algorithm - Artificial Intellegence - Discussion - Assignment Example

Summary
Heuristic is a rule of thumb that can be of help in order to solve a given problem. It considers the knowledge of the problem to aid the search process. (Jones, 2008) and aims at minimizing the number…

## Extract of sample "A* Search algorithm - Artificial Intellegence - Discussion"

Running Head: A* Search algorithm - Artificial Intelligence A* Search algorithm - Artificial Intelligence [Institute’s A* Search algorithm - Artificial Intelligence
A heuristic resembles an algorithm except that it has a difference of a key. Heuristic is a rule of thumb that can be of help in order to solve a given problem. It considers the knowledge of the problem to aid the search process. (Jones, 2008) and aims at minimizing the number of nodes.
A* Search is an uninformed search method which in similarity with the best-search method uses a heuristic function. The major type of heuristic involved in A * search is the admissible heuristic. The A* search uses a heuristic function that is a distance-plus-cost which in turn is a sum of a path-cost function (g(x)) which may not necessarily be heuristic and an admissible heuristic (h(x)).
An admissible function is a function that is lowest-cost path to the target or in other words, a heuristic is admissible if it does not overestimate the cost (Russell & Norvig, 2002). It is the path evaluation function that A* uses to choose suitable paths through the search space (Copin, 2004). The other functions are consistent heuristic function and the incremental heuristic search. While former, solve problems without taking any step back, the later approaches solution by reusing previous searches’ information to pace.
Some conditions that guarantee an optimal minimum-cost path are; (Kaufman. 1998) firstly, each node of the graph has finite number of successors, if any. Secondly, all the arcs’ costs are greater than some positive amount and lastly, h function called optimistic estimator, for all nodes n in the search graph, never overestimates the actual value. Moreover, since these conditions are most likely met in real-world situations, A* search is optimal and complete (Copin, 2004).
References
Coppin, Ben. (2004). Artificial intelligence illuminated. Jones & Bartlett Publishers.
Jones, M. Tim. (2008). Artificial Intelligence: A Systems Approach. Jones & Bartlett Publishers.
Nilsson, Nils J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufman.
Russell, S. J and Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Prentice Hall. Read More
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