Real-Time Heuristic Search for Pathfinding in Video Games

Bulitko, Vadim; Björnsson, Yngvi; Sturtevant, Nathan R.; Lawrence, Ramon

doi:10.1007/978-1-4419-8188-2_1

Cited by 26 publications

(15 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in [20]. The graph data structure is widely used by many applications, such as the representation of transport networks [1], video-game design [8], automated design of digital circuits [19], making the study of snapshot objects pertinent even to these areas.…”

Section: Introductionmentioning

confidence: 99%

Efficient Partial Snapshot Implementations

Kallimanis,

Kanellou,

Kiosterakis

2020

Preprint

View full text Add to dashboard Cite

A snapshot object is a concurrent data structure that has numerous applications in concurrent programming. Snapshots can be used to record the state of the system, so they can provide solutions to problems where an action should be taken when the global state of the system satisfies some conditions. A snapshot object consists of m components, each storing a value from a given set. Processes can read/modify the state of the object by performing U P DAT E and SCAN operations. An U P DAT E operation gives processes the ability to change the value of a component, while the SCAN operation returns a "consistent" view of all the components. In most literature, two variants (in terms of the number active scanners) of snapshot objects are studied. The first one is the single-scanner snapshot object, where at most one SCAN operation is performed at any given time (whilst supporting many concurrent U P DAT E operations). The second one is the multi-scanner snapshot object that can support multi concurrent SCAN operations at any given time.In this work, we propose the λ-scanner snapshot, a variation of the snapshot object, which supports any fixed amount of 0 < λ ≤ n different SCAN operations being active at any given time. Whenever λ is equal to the number of processes n in the system, the λ-scanner object implements a multi-scanner object, while in case that λ is equal to 1, the λ-scanner object implements a single-scanner object. We present the λ−Snap snapshot object, a waitfree λ-scanner snapshot implementation that has a step complexity of O(λ) for U P DAT E operations and O(λm) for SCAN operations. The space complexity of λ − Snap is O(λm). λ − Snap provides a trade-off between the step/space complexity and the maximum number of SCAN operations that the system can afford to be active on any given point in time. The low space complexity that our implementations provide makes them more appealing in real system applications. Moreover, we provide a slightly modified version of the λ − Snap implementation, which is called partial λ − Snap, that is able to support dynamic partial scan operations. In such an object, processes can execute modified SCAN operations called P ART IAL SCAN that could obtain a part of the snapshot object avoiding to read the whole set of components.In this work, we first provide a simple single-scanner version of λ − Snap, which is called 1 − Snap. We provide 1 − Snap just for presentation purposes, since it is simpler than λ − Snap. The U P DAT E in 1 − Snap has a step complexity of O(1), while the SCAN has a step complexity of O(m). This implementation uses O(m) CAS registers.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient Partial Snapshot Implementations

Kallimanis,

Kanellou,

Kiosterakis

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Real-time search is a wellestablished area addressing this through dedicated heuristic search algorithms (e.g. Korf 1990; Bulitko and Lee 2006;Koenig and Sun 2009;Bulitko et al 2011;Hernández and Baier 2012;Kiesel, Burns, and Ruml 2015). One important issue in this context is completeness: guaranteeing that the agent will eventually reach a goal state.…”

Section: Introductionmentioning

confidence: 99%

Refining Abstraction Heuristics during Real-Time Planning

Eifler

Fickert

Hoffmann

et al. 2019

AAAI

View full text Add to dashboard Cite

In real-time planning, the planner must select the next action within a fixed time bound. Because a complete plan may not have been found, the selected action might not lead to a goal and the agent may need to return to its current state. To preserve completeness, real-time search methods incorporate learning, in which heuristic values are updated. Previous work in real-time search has used table-based heuristics, in which the values of states are updated individually. In this paper, we explore the use of abstraction-based heuristics. By refining the abstraction on-line, we can update the values of multiple states, including ones the agent has not yet generated. We test this idea empirically using Cartesian abstractions in the Fast Downward planner. Results on various benchmarks, including the sliding tile puzzle and several IPC domains, indicate that the approach can improve performance compared to traditional heuristic updating. This work brings abstraction refinement, a powerful technique from offline planning, into the real-time setting.

show abstract

“…There has been a significant amount of research in recent years on pathfinding, especially in computer games (Bulitko, 2011). Games, especially real time strategy games 1 , are strong drivers of research in this field, as they require the implementation of an increasing numbers of AI agents 2 with ever increasing complexity.…”

Section: Introductionmentioning

confidence: 99%

Importing Data from Shapefiles and Pathfinding along Generated Nodes

Brestoiu¹

2017

JSST

View full text Add to dashboard Cite

ABSTRACT:The Shapefile format is a particular standard for storing GIS (Geographic Information System) data, designed and developed by the Environmental Systems Research Institute (ESRI). The purpose of this project was to extract the binary data describing the City of Lethbridge from ESRI Shapefiles, and then to demonstrate an ability to utilize and modify this data. The utilization component centered on pathfinding and visually drawing the data, while the modification component involved the creation of a new, human-readable file type which contained the processed Shapefile data. These goals were accomplished by converting the Shapefile data into custom 'Node' objects in C++ code. These nodes form the basis for further development, as more attributes can easily be added to them as needed. The implemented pathfinding is a matter of picking a starting and ending node, and travelling across their adjacent nodes until a shortest path is found, a search algorithm called A* (read: A Star). Although further work is necessary for a robust product, this platform is already highly modular and is freely available open source.Le format Shapefile est un standard particulier pour le stockage des données du système d'information géographique (SIG), conçu et développé par l'Institut de Recherche des Systèmes Environnementaux (ESRI). Le but de ce project était d'extraire les données binaires qui décrivent la ville the Lethbridge des Shapefiles ESRI, et de démontrer que ces données peuvent être utilisées et modifiées. Le composant d'utilisation était centré sur la navigation et la visualization des données, tandis que le composant de modification a demandé la création d'un nouveau format lisible aux humains qui contient les données Shepfile traitées. Ces buts ont été accomplies en convertissant l'information Shapefile en objets 'noeud' personnalisés dans le langage de programmation C++. Ces noeuds forment la base pour les développements plus approfondis, car plus d'attributs peuvent être facilement ajoutés aux noeuds lorsque nécessaire. Le système de navigation implémentée est alors une question de choisir un noeud de départ et de terminaison, puis voyager à travers leurs noeuds adjacents jusqu'à la découverte de la route la plus courte. Ce procès informatique est l'algorithme de recherche A* (lu : A Star). Quoi qu'encore plus de travail soient nécessaire pour le développement d'un produit fiable, cette plateforme est déjà très modulaire et disponible à l'open-source.

show abstract

Real-Time Heuristic Search for Pathfinding in Video Games

Cited by 26 publications

References 15 publications

Efficient Partial Snapshot Implementations

Efficient Partial Snapshot Implementations

Refining Abstraction Heuristics during Real-Time Planning

Importing Data from Shapefiles and Pathfinding along Generated Nodes

Contact Info

Product

Resources

About