A Formal Basis for the Heuristic Determination of Minimum Cost Paths

Hart, P. D’Arcy; Nilsson, Nils J.; Raphael, Benny

doi:10.1109/tssc.1968.300136

Cited by 9,098 publications

(4,722 citation statements)

References 2 publications

Supporting

Mentioning

4,650

Contrasting

Unclassified

Order By: Relevance

“…• astar uses the A * pathfinding algorithm [31] to find the shortest route between two points in a road map.…”

Section: Analysis Of Ordered Irregular Algorithmsmentioning

confidence: 99%

A scalable architecture for ordered parallelism

Jeffrey¹,

Subramanian²,

Yan³

et al. 2015

Proceedings of the 48th International Symposium on Microarchitecture

View full text Add to dashboard Cite

We present Swarm, a novel architecture that exploits ordered irregular parallelism, which is abundant but hard to mine with current software and hardware techniques. In this architecture, programs consist of short tasks with programmer-specified timestamps. Swarm executes tasks speculatively and out of order, and efficiently speculates thousands of tasks ahead of the earliest active task to uncover ordered parallelism. Swarm builds on prior TLS and HTM schemes, and contributes several new techniques that allow it to scale to large core counts and speculation windows, including a new execution model, speculation-aware hardware task management, selective aborts, and scalable ordered commits.We evaluate Swarm on graph analytics, simulation, and database benchmarks. At 64 cores, Swarm achieves 51-122× speedups over a single-core system, and outperforms software-only parallel algorithms by 3-18×.

show abstract

“…• astar uses the A * pathfinding algorithm [31] to find the shortest route between two points in a road map.…”

Section: Analysis Of Ordered Irregular Algorithmsmentioning

confidence: 99%

A scalable architecture for ordered parallelism

Jeffrey¹,

Subramanian²,

Yan³

et al. 2015

Proceedings of the 48th International Symposium on Microarchitecture

View full text Add to dashboard Cite

show abstract

“…El algoritmo es una especialización de la búsqueda de costo uniforme [10] y, como tal, no funciona en grafos con aristas de coste negativo. En el siguiente seudocódigo se describen los pasos que sigue el algoritmo de Dijkstra para encontrar la ruta más corta [11]: Algoritmo de Búsqueda A*: presentado por primera vez en 1968 por Peter E. Hart, Nils J. Nilsson y Bertram Raphael [12]. Este algoritmo resuelve el problema del camino mínimo entre un par de vérti-ces, usando una heurística para intentar agilizar la búsqueda [9].…”

Section: El Problema Del Camino Mínimounclassified

Una Mirada al Análisis de Redes de Transporte en Cuba desde el Punto de Vista de los Datos

2013

View full text Add to dashboard Cite

Resumen. En la actualidad, la mayoría de las actividades de la sociedad humana se encuentran en un proceso de informatización. Servicios públicos tales como: hospitales, cuerpos de bomberos, policía, empresas de transporte, entre otros, pueden usar estos procesos para mejorar la calidad de los servicios que brindan. Entre las principales herramientas que soportan la informatización de la sociedad se encuentran los Sistemas de Información Geográfica (SIG) y las Infraestructuras de Datos Espaciales (IDE), que apoyan la toma de decisiones en la solución de diversos problemas actuales, a partir de la manipulación de datos espaciales. Países en vías de desarrollo, como Cuba, no están exentos de la informatización de su sociedad. Este artículo presenta un estudio de los datos y herramientas actuales para el uso del análisis de redes viales o de transporte en Cuba. El uso de estos permitiría mejorar los servicios generales de transportación por vía terrestre en las ciudades cubanas, logrando un significativo ahorro de combustible. El resultado más importante obtenido en este trabajo es que el uso de los datos de redes de transporte actuales de Cuba, arroja similares resultados en todas las herramientas analizadas, siendo factible el uso de cualquiera de estas, según el contexto donde se deseen utilizar.Palabras clave: Análisis de redes viales; bases de datos espaciales; camino mínimo; datos espaciales; sistemas información geográfica.Abstract. At present times, most of the activities of human society are in a process of computerization. This means that public services such as hospitals, fire departments, police, transport companies, among others, can use these processes in order to improve the quality of services they provide. Among the main tools that support the computerization of society are Geographic Information Systems (GIS) and Spatial Data Infrastructures (SDI) that support decision making for the solution of several problems using existing spatial data. Developing countries, such as Cuba, are not exempt from the computerization of their society, so this paper presents a study of current data and systems for the use of road or transport network analysis. The use of these data allows improving of general transportation services among Cuban cities, and thus achieving significant fuel savings. Most significant result of this paper is that all analyzed system throws similar result using Cuban transportation network data. Using any of those systems depends on the context of use.

show abstract

“…A * is an algorithm for goal-directed search which is very similar to Dijkstra's algorithm [22]. The difference between the two algorithms lies in the priority key.…”

Section: A * With Landmarksmentioning

confidence: 99%

“…For A * , the priority key of a node v is made up of two parts: the length of the tentative shortest path from the source to v (as in Dijkstra's algorithm), and an underestimation of the distance to reach the target from v. Thus, the key of v represents an estimation of the length of the shortest path from s to t passing through v, and nodes are sorted in the priority queue according to this criterion. The function which estimates the distance between a node and the target is called potential function π; the use of π has the effect of giving priority to nodes that are (supposedly) closer to the target node t. If the potential function is such that π(u) − π(v) ≤ c(u, v) ∀(u, v) ∈ A and π(t) ≤ 0, then A * computes shortest paths [22] and π(v) ≤ d(v, t) ∀v ∈ V . A * is equivalent to Dijkstra's algorithm on a graph where arc costs are the reduced costs [23].…”

Section: A * With Landmarksmentioning

confidence: 99%

Core Routing on Dynamic Time-Dependent Road Networks

Delling

Nannicini

2012

INFORMS Journal on Computing

View full text Add to dashboard Cite

Route planning in large scale time-dependent road networks is an important practical application of the shortest paths problem that greatly benefits from speedup techniques. In this paper we extend a two-level hierarchical approach for pointto-point shortest paths computations to the time-dependent case. This method, also known as core routing in the literature for static graphs, consists in the selection of a small subnetwork where most of the computations can be carried out, thus reducing the search space. We combine this approach with bidirectional goal-directed search in order to obtain an algorithm capable of finding shortest paths in a matter of milliseconds on continental sized networks. Moreover, we tackle the dynamic scenario where the piecewise linear functions that we use to model time-dependent arc costs are not fixed, but can have their coefficients updated requiring only a small computational effort.

show abstract

A Formal Basis for the Heuristic Determination of Minimum Cost Paths

Cited by 9,098 publications

References 2 publications

A scalable architecture for ordered parallelism

A scalable architecture for ordered parallelism

Una Mirada al Análisis de Redes de Transporte en Cuba desde el Punto de Vista de los Datos

Core Routing on Dynamic Time-Dependent Road Networks

Contact Info

Product

Resources

About