Index Term— Interative Deepining Bi-directional Heuristic Front-to-Front Algorithm (IDBHFFA), Bi-directional Heuristic Front-to-Front Algorithm (BHFFA), Bi-directional Depth-First Iterative Deepening (DFID) S earch, Bi-directional Heuristic Path (original) (raw)

Artificial Intelligence (AI) is a subject that studies techniques for making computers exhibit intelligent behavior. S earching still remains one of the problem in AI. Bi -directional search is performed by searching simultaneously in forward direction from the initial node and in backward direction from the goal node. Bi-directional heuristic search algorithms need less time and space than their unidirectional versions. Bi -directional Heuristic Front to Front Algorithm (BHFFA) is one of the Bidirectional heuristic search algorithm. However, it has some disadvantages. It needs to store many unnecessary nodes prior to termination. Moreover, in large problem spaces the computational overhead for the selection of the next node to be expanded increases significantly. This paper presents a modification to the BHFFA called Iterative Deepening Bidirectional Heuristic Front-to-Front Algorithm (IDBHFFA) that has been analyzed and implemented using the 8-puzzle problem. The proposed algorithm performs BHFFA in a number of iterations. For each iteration, two thresholds are maintained, one for each search frontier. In each iteration, some non-promising nodes on a particular search frontier having cost estimates greater than the corresponding threshold value are pruned. The process continues with successive iterations with the thresholds increasing with each iteration. The proposed algorithm will return optimal solutions with an admissible heuristic function. It can minimize the computational time and memory space requirement of BHFFA considerably.