Admissible heuristic

About: Admissible heuristic is a research topic. Over the lifetime, 197 publications have been published within this topic receiving 15329 citations. The topic is also known as: admissible heuristics.

An intelligent search method for project scheduling problems

Abstract: We develop an approach for implementing a real time admissible heuristic search algorithm for solving project scheduling problems with resource constraints. This algorithm is characterized by the complete heuristic learning process: state selection, heuristic learning, and search path review. The implementation approach is based on the network structure and the activity status of a project; which consists of definition of states, state transition operator, heuristic estimation, and state transition cost. The performance analysis with a benchmark problem shows that, the accumulation of heuristic learning during the search process leads to the re-scheduling of more promising activities, and finds an optimal schedule efficiently.

A New Approach of Iterative Deepening Bi- Directional Heuristic Front-to-Front Algorithm (IDBHFFA)

Abstract: 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 Bi - directional 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 Bi - directional 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.

New Strategies in Learning Real Time Heuristic Search

Abstract: In contrast to off-line search algorithms such as A ° and IDA’, in real-time heuristic search we have to commit a move within a limited search horizon or time. One well known algorithm in this class is RTA’. An algorithm is said to learn if it improves its performance over successive problem trials. In RTA" the heuristic estimation is in general not admissible. Thus RTA ° has to be modified to a variant LRTA" that is capable of learning. The aim of the strategies proposed in this paper is to improve the estimations found in LRTA’. First, we examine two new schemas forward updating and backward updating for LRTA*. Then we propose CRTA" which works similar to RTA* but terminates with admissible heuristic values. It is shown that the strategy used in CRTA* can be made efficiently. Combined with lazy evaluation updating CRTA* leads to an improved real time learning algorithm called SLRTA’. Experimentally we show that CRTA* expands significantly less nodes than LRTA" and thus converges faster to the optimal values.

A epsilon - An Efficient Near Admissible Heuristic Search Algorithm.

Abstract: Two drawbacks to A* explain its high complexity. First, A* tends to do much backtracking due to the invariable choice of n' as the node to be expanded next. A* tends to expand many nodes not in the final solution path since h (being a heuristic) fluctuates in quality and hence various near optimal paths take random turns appearing to be optimal. Such paths effectively "race" with one another to reach their goals. So we can trace the cause of backtracking to A*'s desire to fine tune an optimal cost solution. The algorithm A* (Nilsson, 1979) presents two significant drawbacks. First, in seeking strict optimal solution paths it necessarily has high order of complexity. Second, the algorithm does not explicitly descriminate between the cost of a solution path and the cost of finding the solution path. To confront these problems we propose the algorithm AE, a generalization of A*. Instead of seeking an optimal solution, it seeks one which is within a factor (1+e) of optimum (e > 0). The basic idea is to avoid doing any search at all on most near optimal partial solutions by sticking to a small number of most f ru i t fu l paths. Various strategies for searching for near optimal partial solutions are discussed. Experimental results are presented indicating that A e has average complexity of lower order than A* and compares favorably to the related algorithm Af* (Pearl and Kim, 1982).

An Optimal Bidirectional Search Algorithm

Abstract: Several optimality theorems hold for the A* algorithm, one important says: A* dominates every other admissible heuristic search algorithm assumed that the underlying heuristic function is monotone. An analogous result is proved for the bidirectional case. A bidirectional heuristic search algorithm, named OBDS, is presented which is admissible and dominates every other admissible bidirectional heuristic search algorithm B assumed that the underlying heuristic function is bi-monotone.