Pathfinding

The problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra's algorithm in this case is better than the other two algorithms because it always gets optimal results.

ABSTRACT. Working out  reliable scientific – not ideologically-biased -  model of social evolution, defining the criteria for Natural Selection any any level of  complexity or Universal Darwinism, and  sociobiology theoretical  rethinking therefore

Part of a course about “Interactive Objects in Gaming Applications: Basic principles and practical scenarios using the Unity platform”, in which I am covering the part about how objects can exhibit behavior. This part is on pathfinding or path planning.

Unmanned aerial vehicles (UAVs) are used in team for detecting targets and keeping them in its sensor range. There are various algorithms available for searching and monitoring targets. The complexity of the search algorithm increases if the number of nodes is increased. This paper focuses on multi UAVs path planning and Path Finding algorithms. Number of Path Finding and Search algorithms was applied to various environments, and their performance compared. The number of searches and also the computation time increases as the number of nodes increases. The various algorithms studied are Dijkstra’s algorithm, Bellman Ford’s algorithm, Floyd-Warshall’s algorithm and the AStar algorithm. These search algorithms were compared. The results show that the AStar algorithm performed better than the other search algorithms. These path finding algorithms were compared so that a path for communication can be established and monitored.

Carrying out the structural analysis of evolutionary dynamics of developing systems in search the Complex Engineering Model to ensure their further sustainable development Research starts from working through that pattern of Finding and Resolving the Root causes of Sustainability problem at thwink.com by applying the model of Evolutionary process, offered Evolutionary Manifesto of John Stewart Ref1

Using accumulated cost surfaces and various pathfinding techniques within Geographic Information Systems (GIS) software, archaeologists and other spatial scientists have developed increasingly sophisticated models of human movement. Despite their utility, these approaches can be limited because standard GIS software cannot model movement (1) from many origins to many destinations or (2) without specific origins and destinations. Absent these capabilities, it is particularly difficult to model networks of movement over a given tract of land if you are interested in obtaining a more general sense of movement dynamics, not specific site-to-site patterns. In this paper, we present an innovative way of modeling past movement that generates both natural-looking networks and also indicates the degree of traffic that may have existed on any particular segment of those networks. The "From Everywhere to Everywhere" (FETE) model generates networks based on topography and landcover without requiring that origin and destination points be supplied in advance. We apply the FETE model to a case from the southern Mexican state of Oaxaca, a region that has extensive archaeological and ethnohistoric data sets that serve as a test of the efficacy of our technique. A comparison of the FETE output with known late precolumbian and early colonial movement corridors indicates that the method is effective and should be useful for modeling networks in other areas.

Pathfinding has been one of major research areas in video games for many years. It is a key problem that most video games are confronted with. Search algorithm such as Dijkstra’s algorithm and A* algorithm are representing only half of the picture. The underlying map representations such as regular grid, visibility graph and navigation mesh also have significant impact on the performance. This paper reviews the current widely used solutions for pathfinding and proposes a new method which is expected to generate a higher quality path using less time and memory than other existing solutions. The deployment of methodology and techniques is described in detail. The aim and significance of the proposed method in future video games is addressed and the conclusion is given at the end.

Graph search algorithms are ubiquitous in artificial intelligence problem solving. In particular, Pathfinding Algorithms have a large domain of application: robotics (optimize the structure of the chips), computer gaming (strategy or role-playing games), mathematics (domain of study dedicated on Pathfinding Algorithms), military (simulations), to name a few. In the case of computer gaming, we could summarize the problem as begin the simple search of a path between a point A and B in a graph representing certain terrain. The main difficulties of this task arise from the following considerations:
1) the presence of obstacles on a (possibly) changing map, with (possible) intermediate points to pass by, etc.
2) the ever growing size of the problems we attempt solving using Pathfinding Algorithms,
3) limited computational resources (for the video-game industry).
Combining all these constraints results on a real performance problem. While companies write their own implementations of the algorithms, the field of research on Pathfinding Algorithm is still ongoing. While the reference algorithm for pathfinding is A*, several new alternatives have been proposed in recent years...

Abstract: below goes the project to work out the  probability causal  framework for life contingency in course of Big History of the Earth Biosphere. In order to develop this dynamical model the determinant frame  requirements for mutiltvel natural  selection of  BioSocioComplexAdaptiveSystems in Anthropocene were defined on the basis of  physical and informational load dynamics ensuring living systems’ behavior  evolutionary engineering. This frame of multilevel natural selection  may be  applied to as a starting point for working out pathfinding toward Ecological Resilience or Biosphere  Sustainability in other words 
10 oct 2019

In this paper, the pathfinding algorithm has been implemented using Unity 3D. Unity-3D is a game engine that provides a 3D environment to statistically incorporate various multimedia data into one platform. It was developed by Unity Technology in the year 2005 and has become one of the most popular platforms for developing 2D and 3D games. The gaming environment provides several elements such as the PhysX physics engine, animation system, terrain editor, etc. Pathfinding is a dynamic process in which the plotting of the shortest path or route between two points is being determined by a computer application. Algorithms are defined as the steps required for completing a particular objective or a task. I.

One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. The two main components for basic real-time pathfinding are (i) travelling towards a specified goal and (ii) avoiding dynamic and static obstacles that may litter the path to this goal. The focus of this paper is how machine learning techniques, such as Artificial Neural Networks and Genetic Algorithms, can be used to enhance an AI agent’s ability to handle pathfinding in real-time by giving them an awareness of the virtual world around them through sensors. Thus the agents should be able to react in real-time to any dynamic changes that may occur in the game.

We present a novel approach to adaptive navigation in the interactive virtual world by using data from the user. Our method constructs automatically a navigation mesh that provides new paths for agents by referencing the user movements. To acquire accurate data samples from all the user data in the interactive world, we use the following techniques: an agent of interest (AOI), a region of interest (ROI) map, and a discretized path graph (DPG). Our method enables adaptive changes to the virtual world over time and provides user-preferred path weights for smartagent path planning. We have tested the usefulness of our algorithm with several example scenarios from interactive worlds such as video games. In practice, our framework can be applied easily to any type of navigation in an interactive world. In addition, it may prove useful for solving previous pathfinding problems in static navigation planning.

Game developers are often faced with very demanding requirements on huge numbers of agents moving naturally through increasingly large and detailed virtual worlds. With the advent of multi-core architectures, new approaches to accelerate expensive pathfinding operations are worth being investigated. Traditional single-processor pathfinding strategies, such as A and its derivatives, have been long praised for their flexibility. We implemented several parallel versions of such algorithms to analyze their intrinsic behavior, concluding that they have a large overhead, yield far from optimal paths, do not scale up to many cores or are cache unfriendly. In this article, we propose Parallel Ripple Search, a novel parallel pathfinding algorithm that largely solves these limitations. It utilizes a high-level graph to assign local search areas to CPU cores at "equidistant" intervals. These cores then use A flooding behavior to expand towards each other, yielding good "guesstimate points" at border touch on. The process does not rely on expensive parallel programming synchronization locks but instead relies on the opportunistic use of node collisions among cooperating cores, exploiting the multi-core's shared memory architecture. As a result, all cores effectively run at full speed until enough way-points are found. We show that this approach is a fast, practical and scalable solution and that it flexibly handles dynamic obstacles in a natural way.

Cleanliness is one of the requirements to create a healthy living environment. But in reality, not everyone have a concern for environmental hygiene and health. For this reason, learning about the importance of caring for environmental cleanliness must be instilled early on. One interesting method to provide learning about environmental cleanliness can be through games. With the 3D visualization of this environmental cleanliness game, it is an effort to raise awareness of the importance of environmental hygiene and health in the community. The location used in making this game is Campus 1 Poltekkes Surakarta. The purpose of this study is implementation of Navmesh with the A Star (A*) Pathfinding Algorithm on the characters so that players can find the trash location point and the location point of the tree with the shortest route. Making this game uses the GDLC (Game Development Life Cycle) method with stages of Initiation, Pre Production, Production, Testing, Beta and Release. This game can be played on desktop and mobile devices. The results of the research that has been done are seen from the algorithm simulation testing is the nodes that are passed by as many as 50 nodes, where there are duplicate data as many as 7 nodes due to real time systems that read nodes over and over. Thus, the application accuracy rate reaches 86%.

Netrin-1 was previously shown to be required for the tangential migration and survival of neurons that will form the inferior olivary nucleus (ION). Surprisingly, the compared analysis of mutant mice lacking either Netrin-1 or its major receptor DCC reveals striking phenotypic differences besides common features. Although ectopic stops of ION cell bodies occur in the same positions along the migratory stream in both mutants, the ION neurons' number is not affected by the lack of DCC whereas it is reduced in Netrin-1 mutant mice. Thus, cell death results from the absence of Netrin-1 and not from neuron mis-routing, arguing for a role of Netrin-1 as a survival factor in vivo. The secretion of Netrin-1 by the floor plate (FP) is strictly requiredwhereas DCC is notto avoid ION axons' repulsion by the FP and allows them to cross it. Leading processes of neurons of other caudal precerebellar nuclei (PCN) cannot cross the FP in either mutant mouse, suggesting differential sensitivity or mechanism of action of Netrin-1 for leading processes of ION and other PCN neurons. j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y m c n e ARTICLE IN PRESS Please cite this article as: Marcos, S., et al., Differential roles of Netrin-1 and its receptor DCC in inferior olivary neuron migration, Mol. Cell.

We review the in vivo evidence for aVerent Wber guidance to the inner ear sensory epithelia and the central nuclei of termination. SpeciWcally, we highlight our current molecular understanding for the role of hair cells and sensory epithelia in guiding aVerents, how disruption of certain signals can alter Wber pathways, even in the presence of normal hair cells, and what role neurotrophins play in Wber guidance of sensory neurons to hair cells. The data suggest that the neurotrophin BDNF is the most important molecule known for inner ear aVerent Wber guidance to hair cells in vivo. This suggestion is based on experiments on Ntf3 transgenic mice expressing BDNF under Ntf3 promoter that show deviations of Wber growth in the ear to areas that express BDNF but have no hair cells. However, Wber growth can occur in the absence of BDNF as demonstrated by double mutants for BDNF and Bax. We directly tested the signiWcance of hair cells or sensory epithelia for Wber guidance in mutants that lose hair cells (Pou4f3) or do not form a posterior crista (Fgf10). While these data emphasize the role played by BDNF, normally released from hair cells, there is some limited capacity for directed growth even in the absence of hair cells, BDNF, or sensory epithelia. This directed growth may rely on semaphorins or other matrix proteins because targeted ablation of the sema3 docking site on the sema receptor Npn1 results in targeting errors of Wbers even in the presence of hair cells and BDNF. Overall, our data support the notion that targeting of the aVerent processes in the ear is molecularly distinct from targeting processes in the central nuclei. This conclusion is derived from data that show no recognizable central projection deviation, even if Wbers are massively rerouted in the periphery, as in Ntf3 tgBDNF mice in which vestibular Wbers project to the cochlea. 

Dans cet article, nous proposons une approche de géo-simulation basée sur les agents qui a pour but d'assister la planification dans un espace réel et surtout à forte composante spatiale. L'approche consiste à établir un parallélisme entre l'environnement réel (par exemple une forêt incendiée) et un environnement de simulation basé sur des données géo-référencées. Cet environnement virtuel utilise des agents logiciels sensibles à l'espace et dotés de capacités cognitives avancées pour aider à planifier et coordonner les interventions sur le terrain. Étant donné que les plans générés dans le cas étudié (feux de forêts) sont essentiellement des chemins, nous proposons également une approche basée sur la géo-simulation orientée agent pour résoudre des problèmes particuliers de recherche de chemin. Finalement, pour appliquer notre approche au cas des feux de forêts, nous avons utilisé MAGS comme plate-forme de géo-simulation et Prometheus comme simulateur du feu.

Pathfinding is essential and necessary for agent movement used in computer games and many other applications. Generally, the pathfinding algorithm searches the feasible shortest path from start to end locations. This task is computationally expensive and consumes large memory, particularly in a large map size. Obstacle avoidance in the game environment increases the complexity to find a new path in the search space. A huge number of algorithms, including heuristic and metaheuristics approaches, have been proposed to overcome the pathfinding problem. Artificial Bee Colony (ABC) is a metaheuristic algorithm that is robust, has fast convergence, high flexibility, and fewer control parameters. However, the best solution founded by the onlooker bee in the presence of constraints is still insufficient and not always satisfactory. A number of variant ABC algorithms have been proposed to achieve the optimal solution. However, it is difficult to simultaneously achieve the optimal solution. Alternatively, Flower Pollination Algorithm (FPA) is one of promising algorithms in optimising problems. The algorithm is easier to implement and faster to reach an optimum solution. Thus, this research proposed Artificial Bee Colony – Flower Pollination Algorithm to solve the pathfinding problem in games, in terms of path cost, computing time, and memory. The result showed that ABC-FPA improved the path cost result by 81.68% and reduced time by 97.84% as compared to the ABC algorithm, which led to a better pathfinding result. This performance indicated that ABC-FPA pathfinding gave better quality pathfinding results.

Belum    ditemukannya    solusi    yang
optimal  untuk  menyelesaikan  masalah  pathfinding.
Biasanya  solusinya  tergantung pada  spesifik dari
ketentuan untuk dapat  menemukan  jalan keluar
untuk  setiap permainan  yang diberikan. 
Pathfinding yang optimal merupakan suatu hal yang
sulit  untuk  ditemukan,  dimana  terdapat  perbedaan
antara  istilah  path  dan  shortest  path.  Sehingga
pathfinding  yang  ada  bertujuan  untuk menemukan
jalur  terpendek  yang optimal. Salah  satu  algoritma
yang  digunakan  pada pathfinding    adalah  A*
digunakan dalam melakukan  pencarian  jalur  yang 
optimal  yang  menghubungkan  dua  titik  pada  peta
(grafik)  dari  permainan  yang  ada.  Algoritma  A*
dapat  membantu  NPC untuk  menemukan rute
dalam mencari  keberadaan  target  dengan  berbagai
halangan  yang disediakan  secara  acak  dalam  satu
waktu.  Sehingga  algoritma  ini  dapat  digunakan
untuk mencari rute walaupun  ada  banyak  obstacle
yang bermunculan secara acak.

Kata  kunci:  Algoritma  A*,  Pathfinding,  Obstacle
Advoidance, Kecerdasan Buatan, AI.

Dans cet article, nous proposons une approche de géo-simulation basée sur les agents qui a pour but d'assister la planification dans un espace réel et surtout à forte composante spatiale. L'approche consiste à établir un parallélisme entre l'environnement réel (par exemple une forêt incendiée) et un environnement de simulation basé sur des données géo-référencées. Cet environnement virtuel utilise des agents logiciels sensibles à l'espace et dotés de capacités cognitives avancées pour aider à planifier et coordonner les interventions sur le terrain. Étant donné que les plans générés dans le cas étudié (feux de forêts) sont essentiellement des chemins, nous proposons également une approche basée sur la géo-simulation orientée agent pour résoudre des problèmes particuliers de recherche de chemin. Finalement, pour appliquer notre approche au cas des feux de forêts, nous avons utilisé MAGS comme plate-forme de géo-simulation et Prometheus comme simulateur du feu.

Game is a form of interactivity where player and game world interact to each other. In a game, one of the elements which can be considered necessary to support the course and the reality of the game is how a NPC (Non-Player Character) in the game moves. A* (A-star) is an algorithm which can be used to perform pathfinding. In this case, A* will be used to find a shortest distance between the NPC and the player character. This research was performed to conduct experiments on the implementation of the A* algorithm in the 3D game. The research will be conducted by implementing an A* algorithm to a game, precisely to the enemy characters that exist in the game. Once the implementation was conducted, an experiment will performed using certain cases as a proving ground for the implementation. As a conclusion, from a hundred experiments, A* algorithm always can find the path for enemy to catch player and 73% of them are optimal paths.

In this study the role of membrane-associated molecules involved in entorhinohippocampal pathfinding was examined. First outgrowth preferences of entorhinal neurites were analyzed on membrane carpets obtained from their proper target area, the hippocampus, and compared to preferences on control membranes from brain regions which do not receive afferent connections from the entorhinal cortex. On a substrate consisting of alternating lanes of hippocampal and control membranes, entorhinal neurites exhibited a strong tendency to grow on lanes of hippocampal membrane. These tissue-specific outgrowth preferences were maintained even on membrane preparations from adult brain tissue devoid of myelin. To determine the possible maturation dependence of these membranes, we examined guidance preferences of entorhinal neurites on hippocampal membranes of different developmental stages ranging from embryonic to postnatal and adult. Given a choice between alternating lanes of embryonic (E15-E16) and neonatal (P0-P1) hippocampal membranes, entorhinal neurites preferred to extend on neonatal membranes. No outgrowth preferences were observed on membranes obtained between E19 and P10. From P10 onward there was a reoccurrence of a preference for postnatal membrane lanes when neurites were presented with a choice between P15, P30, and adult membranes (>P60). This choice behavior of entorhinal neurites temporally correlates with the ingrowth of the perforant path into the hippocampus and with the stabilization of this brain area in vivo. Experiments in which postnatal and adult hippocampal membranes were heat inactivated or treated to remove molecules sensitive to phosphatidylinositol-specific phospholipase C demonstrated that entorhinal fiber preferences were controlled in this assay by attractive guidance cues and were independent of phosphatidylinositol-sensitive linked molecules. Moreover, entorhinal neurites displayed a positive discrimination for membrane-associated guidance cues of their target field, thus preferring to grow on membranes from the molecular layer of the dentate gyrus compared with CA3 or hilus membranes. Heat-inactivation experiments indicated that preferential growth of entorhinal axons is due to a specific attractivity of the molecular layer substrate. The data presented demonstrate that outgrowth of entorhinal fibers on hippocampal membranes is target and maturation dependent.

Pathfinding for a single agent is the problem of planning a route from an initial location to a goal location in an environment, going around obstacles. Pathfinding for multiple agents also aims to plan such routes for each agent, subject to different constraints, such as restrictions on the length of each path or on the total length of paths, no self-intersecting paths, no intersection of paths/plans, no crossing/meeting each other. It also has variations for finding optimal solutions, e.g., with respect to the maximum path length, or the sum of plan lengths. These problems are important for many real-life applications, such as motion planning, vehicle routing, environmental monitoring, patrolling, computer games. Motivated by such applications, we introduce a formal framework that is general enough to address all these problems: we use the expressive high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming. We also introduce heuristics to improve the computational efficiency and/or solution quality. We show the applicability and usefulness of our framework by experiments, with randomly generated problem instances on a grid, on a real-world road network, and on a real computer game terrain.

Our previous surgical manipulations have shown that the target ectoderm is necessary for the initial formation of one of the major cutaneous nerves in the embryonic chick limb (Honig, M.G., Camilli, S.J., Xue, Q.S., 2004. Ectoderm removal prevents cutaneous nerve formation and perturbs sensory axon growth in the chick hindlimb. Dev. Biol. 266,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Moreover, the target ectoderm is required during a critical time period, at¨St. 24, when those axons are about to diverge from the hindlimb plexus. To elucidate the underlying mechanisms, here we examined the effects of removing the ectoderm at St. 24 on a variety of molecules expressed within the limb. We find that, while ectoderm removal is accompanied by changes in the expression of Lmx1, fibronectin, EphA7, cDermo-1, and in the complement of muscle cells, these changes do not account for the cutaneous nerve deficit. In contrast, an upregulation of PNA-binding sites and a downregulation of Bmp4 appear to be associated with this nerve deficit. Exogenous BMP4 reversed the effect of ectoderm removal on cutaneous nerve formation, but did not act as a chemoattractant. Our results suggest that BMP4, together with permissive and repulsive molecules that growing cutaneous axons encounter in the local environment and with signaling molecules, originating from and/or dependent on the ectoderm, work in concert to ensure proper cutaneous nerve formation. D

In the developing chick hindlimb, sensory axons, which grow together in bundles as they extend distally, and the motoneuron axons they encounter express the cell adhesion molecule L1. Following injection of function-blocking anti-L1 antibodies into the limb at stage 25, some sensory axons choose inappropriate peripheral nerves even though motoneuron pathfinding is unaffected. Here, to further elucidate L1's role, we assessed the effects of this perturbation using pathway tracing, immune labeling, confocal microscopy, and electron microscopy. After L1 blockade, sensory axons were still bundled and closely apposed. However, clear signs of decreased adhesion were detectable ultrastructurally. Further, sensory axons grew into the limb more slowly than normal, wandering more widely, branching more frequently, and sometimes extending along inappropriate peripheral nerves. Sensory axons that ultimately projected along different cutaneous nerves showed increased intermixing in the spinal nerves, due to errors in pathfinding and also to a decreased ability to segregate into nerve-specific fascicles. These results suggest that, in the highly complex in vivo environment, as in tissue culture, L1 stimulates axon growth and enhances fasciculation, and that these processes contribute to the orderly, timely, and specific growth of sensory axons into the limb. © 2002 Elsevier Science (USA)

Abstrak Terdapat permasalahan yang selalu ada pada pathfinding. Belum ada satu solusi yang tepat untuk setiap jenis masalah pathfinding. Pathfinding yang optimal merupakan skenario yang rumit, dimana terdapat perbedaan yang signifikan antara istilah path dan shortest path. Non Player Character memainkan peranan penting dalam banyak permainan, misalkan menjadi musuh ataupun teman dan yang berhubungan dengan permainan. Setiap NPC harus dapat mencari jalurnya masing-masing untuk mencapai sebuah target yang telah ditentukan sebelumnya dengan berbagai obstacle yang telah ada dan dapat diubah sesuai kebutuhan. Sehingga permainan dapat dibuat lebih menarik. Salah satu algoritma yang digunakan pada pathfinding adalah A* digunakan dalam melakukan pencarian jalur yang optimal yang menghubungkan dua titik pada peta (grafik) dari permainan yang ada. A* lebih cocok untuk sebuah lingkungan di mana ada beberapa rute di sekitar lingkungan yang ada. Algoritma A* dapat membantu NPC untuk menemukan rute dalam mencari keberadaan target dengan berbagai halangan yang disediakan.

In this work we discuss a stochastic extension to the (discrete) Belief-Driven Pathfinding (BDP) approach for finding personalized paths based on the beliefs of a character about the current state of the map. Our stochastic BDP upgrades previous work to the more realistic setting of using probabilities for the beliefs and takes advantage of approximate Monte Carlo Tree Search approaches.

This paper presents a way to dynamically influence the shape and movements of a simulated crowd. We propose a tool and system that allows to modify a crowd’s dynamics in an intuitive, semantically rich and out of context fashion, while being independent from the global path finding architecture and having a low computational cost. We follow a mixed approach where user-specified navigation fields are combined with steering and global A* pathfinding.

Because no scientific literature exists regarding endodontic pathfinders, the aim of this study was to compare such instruments. Ten different pathfinder-type files were analyzed with respect to dimensional characteristics, pitch, and rigidity; efficiency, wear, and distortion were assessed by using an in vitro simulation exercise. SEM cross-sections and tip images were obtained and analyzed. To assess efficacy and distortion, 10 operators attempted to achieve patency with the files in small, S-shaped canals in acrylic blocks. Trends in canal negotiation efficiency and file distortion were recorded. Results showed that the Hi-5 (Miltex, York, PA) and Cϩ files (Dentsply/Maillefer, Johnson City, TN) were the least flexible, whereas the Pathfinder CS (SybronEndo, Glendora, CA) and Pathfinder SS (SybronEndo) files were the most. Pathfinder SS, S finder (JSDental/Sendoline, Ridgefield, CT), and D finder (Mani, Tochigi-ken, Japan) were the most efficient during the simulation exercise. Within the parameters of this study, pitch, taper, cross-section, heat tempering, metal type, tip geometry, and operator skills all influenced pathfinder efficiency.

مشكلة  المسار  الاقصر  جدا مهمه في دراسة التنقل وشبكة الاتصالات.  التنقل هو جزء مهم في حياتنا اليوميه.  لذلك السبب,  اصبح المسار الاقصر الامثل لهدف معين لا مفر منه لتقليل الكلف , الخسائر بالانتاج , التلوث,والمخاطر ...الخ. الخوارزميه الملائمه لايجاد المسار الاقصر بين قمم او عقد معروفه  هي خوارزميه الدايكسترا و التي قدمت من قبل عالم الحاسوب الهولندي المشهور اديسكير والذي عرفها كافضل خوارزميه التي من الممكن تطبيقها لايجاد المسار الاقصرمن عقده الى اي عقده اخرى . في هذا البحث  تم تصميم تطبيق اندرويد  يعتمد على  خوارزميه دايكسترا المحسنه التي تستخدم الداله الشرطيه وتطوير هذه الخوارزميه  باستخدام الطابور المعتمد على الاولويه  وتحديد المصدر بواسطه GPS . التطبيق يساعد  في ايجاد الطريق الاقصر الى المستشفى  من موقع المستخدم ،الوقت ,المسافة ، وجميع المعلومات المتعلقة بالمستشفى (مثل اسم المستشفى، ورقم الهاتف، البريد الألكتروني، والتخصصات)، مفيد من حيث التقليل من وقت التنقل، الجهد، والتكلفة. يعتبر هذا التطبيق دليلا للمستخدمين للتوجه نحو المستشفيات في منطقة الكراده من مواقع عديده  داخل مدينه بغداد.

Pathfinding is one of the tasks, apart from graphics rendering, requiring most CPU resources. Although there are many approaches to effectively solve pathfinding problems, they are becoming less suitable as more and more games have larger game worlds that dynamically change during the game play. These new games have more visually realistic graphics that increase the game characters realism but all these efforts may be useless if game characters perform dumb movements or follow inappropriate paths such as repeatedly walking close to an enemy or a predator while moving from one location to another. To tackle this problem we present in this paper an ant colony algorithm for path finding that takes into account the emotions of the game characters and we show how our approach is used in an augmented-reality educational game. The proposed algorithm is implemented on a GPU processor to demonstrate its scalability with large problem sizes when compared to its corresponding CPU version.

The cell adhesion molecule (CAM) DM-GRASP was investigated with respect to a role for axonal growth and navigation in the developing visual system. Expression analysis reveals that DM-GRASP's presence is highly spatiotemporally regulated in the chick embryo retina. It is restricted to the optic fiber layer (OFL) and shows an expression maximum in a phase when the highest number of retinal ganglion cell (RGC) axons extend. In the developing retina, axons grow between the DM-GRASP-displaying OFL and the Laminin-rich basal lamina. We show that DM-GRASP enhances RGC axon extension and growth cone size on Laminin substrate in vitro. Preference assays reveal that DM-GRASP-containing lanes guide RGC axons, partially depending on NgCAM in the axonal membrane. Inhibition of DM-GRASP in organ-cultured eyes perturbs orientation of RGC axons at the optic fissure. Instead of leaving the retina, RGC axons cross the optic fissure and grow onto the opposite side of the retina. RGC axon extension per se and navigation from the peripheral retina towards the optic fissure, however, is not affected. Our results demonstrate a role of DM-GRASP for axonal pathfinding in an early phase of the formation of the higher vertebrate central nervous system.

The Drosophila central brain is composed of approximately 100 paired lineages, with most lineages comprising 100-150 neurons. Most lineages have a number of important characteristics in common. Typically, neurons of a lineage stay together as a coherent cluster and project their axons into a coherent bundle visible from late embryo to adult. Neurons born during the embryonic period form the primary axon tracts (PATs) that follow stereotyped pathways in the neuropile. Apoptotic cell death removes an average of 30-40% of primary neurons around the time of hatching. Secondary neurons generated during the larval period form secondary axon tracts (SATs) that typically fasciculate with their corresponding primary axon tract. SATs develop into the long fascicles that interconnect the different compartments of the adult brain. Structurally, we distinguish between three types of lineages: PD lineages, characterized by distinct, spatially separate proximal and distal arborizations; C lineages with arborizations distributed continuously along the entire length of their tract; D lineages that lack proximal arborizations. Arborizations of many lineages, in particular those of the PD type, are restricted to distinct neuropile compartments. We propose that compartments are 'scaffolded" by individual lineages, or small groups thereof. Thereby, the relatively small number of primary neurons of each primary lineage set up the compartment map in the late embryo. Compartments grow during the larval period simply by an increase in arbor volume of primary neurons. Arbors of secondary neurons form within or adjacent to the larval compartments, resulting in smaller compartment subdivisions and additional, adult specific compartments.

Path finding is one of the tasks, apart from graphics rendering, requiring most CPU resources. Although there are many approaches to effectively solve pathfinding problems, they are becoming less suitable as more and more games have larger game worlds that dynamically change during the game play. These new games have more visually realistic graphics that increase the game characters realism but all these efforts may be useless if game characters perform dumb movements or follow inappropriate paths such as repeatedly walking close to an enemy or a predator while moving from one location to another. To tackle this problem we present in this paper an ant colony algorithm for path finding that takes into account the emotions of the game characters and we show how our approach is used in an augmented-reality educational game. The proposed algorithm is implemented on a GPU processor to demonstrate its scalability with large problem sizes when compared to its corresponding CPU version.

In a variety of domains, such as computer games and robotics, many shortest paths have to be found quickly in real time. We address the problem of quickly finding shortest paths in large known graphs. We propose a method that relies on identifying areas that tend to be searched needlessly (areas we call swamp-regions), and exploits this knowledge to improve search. The method requires storing only a few bits in memory for each node of the graph, and reduces search cost drastically, while still finding optimal paths. Our method is independent of the heuristics used in the search, and of the search algorithm. We present experimental results that support our claims, and provide an anytime algorithm for the pre-processing stage that identifies swamp-regions.