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SMA* or Simplified Memory Bounded A* is a shortest path algorithm based on the A* algorithm. The main advantage of SMA* is that it uses a bounded memory, while the A* algorithm might need exponential memory. All other characteristics of SMA* are inherited from A*.
The new egalitarian approach is to rely on memory-bound functions. As stated before, a memory-bound function is a function whose computation time is dominated by the time spent accessing memory. A memory-bound function accesses locations in a large region of memory in an unpredictable way, in such a way that using caches are not effective.
The most general version of the DPOP algorithm: "MB-DPOP: A New Memory-Bounded Algorithm for Distributed Optimization " (with Adrian Petcu), Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1452–1457, 2007
Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. [1] The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes.
Dijkstra's algorithm, as another example of a uniform-cost search algorithm, can be viewed as a special case of A* where = for all x. [ 12 ] [ 13 ] General depth-first search can be implemented using A* by considering that there is a global counter C initialized with a very large value.
Specific applications of search algorithms include: Problems in combinatorial optimization, such as: . The vehicle routing problem, a form of shortest path problem; The knapsack problem: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as ...
The edges traversed in this search form a Trémaux tree, a structure with important applications in graph theory. Performing the same search without remembering previously visited nodes results in visiting nodes in the order A, B, D, F, E, A, B, D, F, E, etc. forever, caught in the A, B, D, F, E cycle and never reaching C or G.
Best-first search is a class of search algorithms which explores a graph by expanding the most promising node chosen according to a specified rule.. Judea Pearl described best-first search as estimating the promise of node n by a "heuristic evaluation function () which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to ...