Simulation and Comparison of Efficency in Pathfinding algorithms in Games

There are several routes to go from point A to point B in many computer games and computer player have to choose the best route. To do this, the pathfinding algorithms is used. Currently, several algorithms have been proposed for routing in games so that the general challenges of them is high consumption of memory and a long Execution time. Due to these problems, the development and introduction of new algorithms will be continued. At the first part of this article, in addition to basic and important used algorithms, the new algorithm BIDDFS is introduced.In the second part, these algorithms in the various modes, are simulated on 2D-Grid, and compared based on their efficency (memory consumption and execution time) , Simulated algorithms include: Dijkstra, Iddfs, Biddfs, Bfs (Breadth), Greedy Best First Search, Ida*, A*, Jump point search, HPA*

Pathfinding Algorithm Efficiency Analysis in 2D Grid

The main goal of this paper is to collect information about pathfinding algorithms A*, BFS, Dijkstra's algorithm, HPA* and LPA*, and compare them on different criteria, including execution time and memory requirements. Work has two parts, the first being theoretical and the second practical. The theoretical part details the comparison of pathfinding algorithms. The practical part includes implementation of specific algorithms and series of experiments using algorithms implemented. Such factors as various size two dimensional grids and choice of heuristics were taken into account while conducting experiments

Pathfinding by demand sensitive map abstraction

In this thesis, we present a new algorithm: Demand Sensitive Map Abstraction (DSMA). DSMA is a special kind of hierarchical pathfinding algorithm in which we vary the granularity of abstraction of the high-level map based on pathfinding request demand associated with various regions in the high level map and the search time of the last path request. Additionally, the low level A* search is not restricted by the boundaries of the high level sectors. By dynamically varying the abstraction we are able to maintain a balance between path quality and search time. We compare DSMA with two variations where the granularity of abstraction is constant; one of those contains maximum granularity throughout (Dense HA*) and the other contains the minimum (Sparse HA*). Our experimental results show that DSMA\u27s performance is a balance between Dense HA* and Sparse HA*. Depending on the resources available DSMA can behave either as Dense HA* or as Sparse HA* or lie somewhere in between. Moreover we do not pre-cache paths at any level, which gives us the added benefit of working with a flexible abstract map without the necessity of changing the pre-cached paths if the low level map changes

TROPOSPHERIC OZONE AND ITS RADIATIVE EFFECTS DUE TO ANTHROPOGENIC AND LIGHTNING EMISSIONS: GLOBAL AND REGIONAL MODELING

We analyze the contribution of North American (NA) lightning and anthropogenic emissions to summertime ozone concentrations, radiative forcing, and exports from North America using the global University of Maryland chemistry transport model (UMD-CTM) and the regional scale Weather Research and Forecasting model with chemistry (WRF-Chem). Lightning NO contributes by 15–20 ppbv to upper tropospheric ozone concentrations over the United States with the effects of NA lightning on ozone seen as far east as North Africa and Europe. Using the UMD-CTM, we compare changes in surface and column ozone amounts due to the NOx State Implementation Plan (SIP) Call with the natural variability in ozone due to changes in meteorology and lightning. Comparing early summer 2004 with 2002, surface ozone decreased by up to 5 ppbv due to the NOx SIP Call while changes in meteorology and lightning resulted in a 0.3–1.4 ppbv increase in surface ozone. Ozone column variability was driven primarily by changes in lightning NO emissions, especially over the North Atlantic. As part of our WRF-Chem analysis, we modify the radiation schemes to use model-calculated ozone (interactive ozone) instead of climatological ozone profiles and conduct multiple 4-day simulations of July 2007. We found that interactive ozone increased the outgoing longwave radiation (OLR) by 3 W m−2 decreasing the bias with respect to remotely sensed OLR. The improvement is due to a high bias in the climatological ozone profiles. The interactive ozone had a small impact on mean upper troposphere temperature (−0.15°C). The UMD-CTM simulations indicate that NA anthropogenic emissions are responsible for more ozone export but less ozone radiative forcing than lightning NO emissions. Over the North Atlantic, NA anthropogenic emissions contributed 0.15–0.30 W m−2 to the net downward radiative flux at the tropopause while NA lightning contributed 0.30–0.50 W m−2. The ozone export from anthropogenic emissions was almost twice as large as that from lightning emissions. The WRF-Chem simulations show that the export of reactive nitrogen was 23%–28% of the boundary layer emissions and 26%–38% of the total emissions including lightning NO

Origin of oxidized mercury in the summertime free troposphere over the southeastern US

We collected mercury observations as part of the Nitrogen, Oxidants, Mercury, and Aerosol Distributions, Sources, and Sinks (NOMADSS) aircraft campaign over the southeastern US between 1 June and 15 July 2013. We use the GEOS-Chem chemical transport model to interpret these observations and place new constraints on bromine radical initiated mercury oxidation chemistry in the free troposphere. We find that the model reproduces the observed mean concentration of total atmospheric mercury (THg) (observations: 1.49 +/- 0.16 ngm(-3), model: 1.51 +/- 0.08 ngm(-3)), as well as the vertical profile of THg. The majority (65 %) of observations of oxidized mercury (Hg(II)) were below the instrument's detection limit (detection limit per flight: 58-228 pgm(-3)), consistent with model-calculated Hg(II) concentrations of 0-196 pgm(-3). However, for observations above the detection limit we find that modeled Hg(II) concentrations are a factor of 3 too low (observations: 212 +/- 112 pgm-3, model: 67 +/- 44 pgm(-3)). The high-est Hg(II) concentrations, 300-680 pgm(-3), were observed in dry (RH <35 %) and clean air masses during two flights over Texas at 5-7 km altitude and off the North Carolina coast at 1-3 km. The GEOS-Chem model, back trajectories and observed chemical tracers for these air masses indicate subsidence and transport from the upper and middle troposphere of the subtropical anticyclones, where fast oxidation of elemental mercury (Hg(0)) to Hg(II) and lack of Hg(II) removal lead to efficient accumulation of Hg(II). We hypothesize that the most likely explanation for the model bias is a systematic underestimate of the Hg(0) + Br reaction rate. We find that sensitivity simulations with tripled bromine radical concentrations or a faster oxidation rate constant for Hg(0) + Br, result in 1.5-2 times higher modeled Hg(II) concentrations and improved agreement with the observations. The modeled tropospheric lifetime of Hg(0) against oxidation to Hg(II) decreases from 5 months in the base simulation to 2.8-1.2 months in our sensitivity simulations. In order to maintain the modeled global burden of THg, we need to increase the in-cloud reduction of Hg(II), thus leading to faster chemical cycling between Hg(0) and Hg(II). Observations and model results for the NOMADSS campaign suggest that the subtropical anticyclones are significant global sources of Hg(II).Peer reviewe

Origin of oxidized mercury in the summertime free troposphere over the southeastern US

We collected mercury observations as part of the Nitrogen, Oxidants, Mercury, and Aerosol Distributions, Sources, and Sinks (NOMADSS) aircraft campaign over the southeastern US between 1 June and 15 July 2013. We use the GEOS-Chem chemical transport model to interpret these observations and place new constraints on bromine radical initiated mercury oxidation chemistry in the free troposphere. We find that the model reproduces the observed mean concentration of total atmospheric mercury (THg) (observations: 1.49 +/- 0.16 ngm(-3), model: 1.51 +/- 0.08 ngm(-3)), as well as the vertical profile of THg. The majority (65 %) of observations of oxidized mercury (Hg(II)) were below the instrument's detection limit (detection limit per flight: 58-228 pgm(-3)), consistent with model-calculated Hg(II) concentrations of 0-196 pgm(-3). However, for observations above the detection limit we find that modeled Hg(II) concentrations are a factor of 3 too low (observations: 212 +/- 112 pgm-3, model: 67 +/- 44 pgm(-3)). The high-est Hg(II) concentrations, 300-680 pgm(-3), were observed in dry (RH < 35 %) and clean air masses during two flights over Texas at 5-7 km altitude and off the North Carolina coast at 1-3 km. The GEOS-Chem model, back trajectories and observed chemical tracers for these air masses indicate subsidence and transport from the upper and middle troposphere of the subtropical anticyclones, where fast oxidation of elemental mercury (Hg(0)) to Hg(II) and lack of Hg(II) removal lead to efficient accumulation of Hg(II). We hypothesize that the most likely explanation for the model bias is a systematic underestimate of the Hg(0) + Br reaction rate. We find that sensitivity simulations with tripled bromine radical concentrations or a faster oxidation rate constant for Hg(0) + Br, result in 1.5-2 times higher modeled Hg(II) concentrations and improved agreement with the observations. The modeled tropospheric lifetime of Hg(0) against oxidation to Hg(II) decreases from 5 months in the base simulation to 2.8-1.2 months in our sensitivity simulations. In order to maintain the modeled global burden of THg, we need to increase the in-cloud reduction of Hg(II), thus leading to faster chemical cycling between Hg(0) and Hg(II). Observations and model results for the NOMADSS campaign suggest that the subtropical anticyclones are significant global sources of Hg(II)

Toward human-like pathfinding with hierarchical approaches and the GPS of the brain theory

Pathfinding for autonomous agents and robots has been traditionally driven by finding optimal paths. Where typically optimality means finding the shortest path between two points in a given environment. However, optimality may not always be strictly necessary. For example, in the case of video games, often computing the paths for non-player characters (NPC) must be done under strict time constraints to guarantee real time simulation. In those cases, performance is more important than finding the shortest path, specially because often a sub-optimal path can be just as convincing from the point of view of the player. When simulating virtual humanoids, pathfinding has also been used with the same goal: finding the shortest path. However, humans very rarely follow precise shortest paths, and thus there are other aspects of human decision making and path planning strategies that should be incorporated in current simulation models. In this thesis we first focus on improving performance optimallity to handle as many virtual agents as possible, and then introduce neuroscience research to propose pathfinding algorithms that attempt to mimic humans in a more realistic manner.In the case of simulating NPCs for video games, one of the main challenges is to compute paths as efficiently as possible for groups of agents. As both the size of the environments and the number of autonomous agents increase, it becomes harder to obtain results in real time under the constraints of memory and computing resources. For this purpose we explored hierarchical approaches for two reasons: (1) they have shown important performance improvements for regular grids and other abstract problems, and (2) humans tend to plan trajectories also following an topbottom abstraction, focusing first on high level location and then refining the path as they move between those high level locations. Therefore, we believe that hierarchical approaches combine the best of our two goals: improving performance for multi-agent pathfinding and achieving more human-like pathfinding. Hierarchical approaches, such as HNA* (Hierarchical A* for Navigation Meshes) can compute paths more efficiently, although only for certain configurations of the hierarchy. For other configurations, the method suffers from a bottleneck in the step that connects the Start and Goal positions with the hierarchy. This bottleneck can drop performance drastically.In this thesis we present different approaches to solve the HNA* bottleneck and thus obtain a performance boost for all hierarchical configurations. The first method relies on further memory storage, and the second one uses parallelism on the GPU. Our comparative evaluation shows that both approaches offer speed-ups as high as 9x faster than A*, and show no limitations based on hierarchical configuration. Then we further exploit the potential of CUDA parallelism, to extend our implementation to HNA* for multi-agent path finding. Our method can now compute paths for over 500K agents simultaneously in real-time, with speed-ups above 15x faster than a parallel multi-agent implementation using A*. We then focus on studying neurosience research to learn about the way that humans build mental maps, in order to propose novel algorithms that take those finding into account when simulating virtual humans. We propose a novel algorithm for path finding that is inspired by neuroscience research on how the brain learns and builds cognitive maps. Our method represents the space as a hexagonal grid, based on the GPS of the brain theory, and fires memory cells as counters. Our path finder then combines a method for exploring unknown environments while building such a cognitive map, with an A* search using a modified heuristic that takes into account the GPS of the brain cognitive map.El problema de Pathfinding para agentes autónomos o robots, ha consistido tradicionalmente en encontrar un camino óptimo, donde por óptimo se entiende el camino de distancia mínima entre dos posiciones de un entorno. Sin embargo, en ocasiones puede que no sea estrictamente necesario encontrar una solución óptima. Para ofrecer la simulación de multitudes de agentes autónomos moviéndose en tiempo real, es necesario calcular sus trayectorias bajo condiciones estrictas de tiempo de computación, pero no es fundamental que las soluciones sean las de distancia mínima ya que, con frecuencia, el observador no apreciará la diferencia entre un camino óptimo y un sub-óptimo. Por tanto, suele ser suficiente con que la solución encontrada sea visualmente creíble para el observado. Cuando se simulan humanoides virtuales en aplicaciones de realidad virtual que requieren avatares que simulen el comportamiento de los humanos, se tiende a emplear los mismos algoritmos que en video juegos, con el objetivo de encontrar caminos de distancia mínima. Pero si realmente queremos que los avatares imiten el comportamiento humano, tenemos que tener en cuenta que, en el mundo real, los humanos rara vez seguimos precisamente el camino más corto, y por tanto se deben considerar otros aspectos que influyen en la toma de decisiones de los humanos y la selección de rutas en el mundo real. En esta tesis nos centraremos primero en mejorar el rendimiento de la búsqueda de caminos para poder simular grandes números de humanoides virtuales autónomos, y a continuación introduciremos conceptos de investigación con mamíferos en neurociencia, para proponer soluciones al problema de pathfinding que intenten imitar con mayor realismo, el modo en el que los humanos navegan el entorno que les rodea. A medida que aumentan tanto el tamaño de los entornos virtuales como el número de agentes autónomos, resulta más difícil obtener soluciones en tiempo real, debido a las limitaciones de memoria y recursos informáticos. Para resolver este problema, en esta tesis exploramos enfoques jerárquicos porque consideramos que combinan dos objetivos fundamentales: mejorar el rendimiento en la búsqueda de caminos para multitudes de agentes y lograr una búsqueda de caminos similar a la de los humanos. El primer método presentado en esta tesis se basa en mejorar el rendimiento del algoritmo HNA* (Hierarchical A* for Navigation Meshes) incrementando almacenamiento de datos en memoria, y el segundo utiliza el paralelismo para mejorar drásticamente el rendimiento. La evaluación cuantitativa realizada en esta tesis, muestra que ambos enfoques ofrecen aceleraciones que pueden llegar a ser hasta 9 veces más rápidas que el algoritmo A* y no presentan limitaciones debidas a la configuración jerárquica. A continuación, aprovechamos aún más el potencial del paralelismo ofrecido por CUDA para extender nuestra implementación de HNA* a sistemas multi-agentes. Nuestro método permite calcular caminos simultáneamente y en tiempo real para más de 500.000 agentes, con una aceleración superior a 15 veces la obtenida por una implementación paralela del algoritmo A*. Por último, en esta tesis nos hemos centrado en estudiar los últimos avances realizados en el ámbito de la neurociencia, para comprender la manera en la que los humanos construyen mapas mentales y poder así proponer nuevos algoritmos que imiten de forma más real el modo en el que navegamos los humanos. Nuestro método representa el espacio como una red hexagonal, basada en la distribución de ¿place cells¿ existente en el cerebro, e imita las activaciones neuronales como contadores en dichas celdas. Nuestro buscador de rutas combina un método para explorar entornos desconocidos mientras construye un mapa cognitivo hexagonal, utilizando una búsqueda A* con una nueva heurística adaptada al mapa cognitivo del cerebro y sus contadores

Video game pathfinding and improvements to discrete search on grid-based maps

The most basic requirement for any computer controlled game agent in a video game is to be able to successfully navigate the game environment. Pathfinding is an essential component of any agent navigation system. Pathfinding is, at the simplest level, a search technique for finding a route between two points in an environment. The real-time multi-agent nature of video games places extremely tight constraints on the pathfinding problem. This study aims to provide the first complete review of the current state of video game pathfinding both in regards to the graph search algorithms employed as well as the implications of pathfinding within dynamic game environments. Furthermore this thesis presents novel work in the form of a domain specific search algorithm for use on grid-based game maps: the spatial grid A* algorithm which is shown to offer significant improvements over A* within the intended domain. CopyrightDissertation (MSc)--University of Pretoria, 2011.Computer Scienceunrestricte

HPA* Enhancements

In video games, pathfinding must be done quickly and accurately. Not much computational time is allowed for pathfinding, but realistic looking paths are required. One approach to pathfinding which attempts to satisfy both of these constraints is to perform pathfinding on abstractions of the map. Botea et al.'s Hierarchical Pathfinding A* (HPA*) does this by dividing the map into square sectors and defining entrances between them. Although HPA* performs quick pathfinding which produces near-optimal paths, some improvements can be introduced. Here we discuss a faster path smoothing method, an alternative way to compute the weights of abstract edges, and lazy edge weight computations