scholarly journals Novel optimization using hierarchical Path finding A* (HPA*) algorithm for strategic gaming setup

2018 ◽  
Vol 7 (2.6) ◽  
pp. 54
Author(s):  
Aqsa Zafar ◽  
Krishna Kant Agrawal
Keyword(s):  
Shortest Path ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Research Area ◽  
Optimization Techniques ◽  
Path Finding ◽  
A Algorithm ◽  
Strategic Gaming ◽  
Game Industry ◽  
Future Work

In game Industry, the most trending research area is shortest path finding. There are many video games are present who are facing the problem of path finding and there is various algorithms are present to solve this problem. In this paper brief introduction is given in the most using algorithm for path finding and A* algorithm has been proved the best algorithm for resolving the problem of shortest path finding in games. It provides the optimal solution for path finding as compare to other search algorithm. At the start of the paper, brief introduction about the path finding is given. Then the reviews of different search algorithm are presented on the basis of path finding. After that information of A* algorithm and optimization techniques are described. In the last, application and examples how the path finding techniques are used in the game is addressed and future work and conclusion are drawn.

scholarly journals Multitarget Tracking Algorithm Based on Adaptive Network Graph Segmentation in the Presence of Measurement Origin Uncertainty

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3791
Author(s):  
Tianli Ma ◽  
Song Gao ◽  
Chaobo Chen ◽  
Xiaoru Song
Keyword(s):  
Shortest Path ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Tracking Algorithm ◽  
Multitarget Tracking ◽  
Integer Programming Problem ◽  
Tracking Accuracy ◽  
Network Graph ◽  
Adaptive Network

To deal with the problem of multitarget tracking with measurement origin uncertainty, the paper presents a multitarget tracking algorithm based on Adaptive Network Graph Segmentation (ANGS). The multitarget tracking is firstly formulated as an Integer Programming problem for finding the maximum a posterior probability in a cost flow network. Then, a network structure is partitioned using an Adaptive Spectral Clustering algorithm based on the Nyström Method. In order to obtain the global optimal solution, the parallel A* search algorithm is used to process each sub-network. Moreover, the trajectory set is extracted by the Track Mosaic technique and Rauch–Tung–Striebel (RTS) smoother. Finally, the simulation results achieved for different clutter intensity indicate that the proposed algorithm has better tracking accuracy and robustness compared with the A* search algorithm, the successive shortest-path (SSP) algorithm and the shortest path faster (SPFA) algorithm.

scholarly journals Anytime Heuristic Search

2007 ◽  
Vol 28 ◽  
pp. 267-297 ◽  
Author(s):  
E. A. Hansen ◽  
R. Zhou
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Search Problem ◽  
A Algorithm ◽  
Solution Quality ◽  
Multiple Sequence ◽  
Anytime Algorithm ◽  
Heuristic Search Algorithm

We describe how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A* algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memory-efficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm.

scholarly journals Remarks on the A** algorithm

2014 ◽  
Vol 6 (2) ◽  
pp. 190-205
Author(s):  
Tibor Gregorics
Keyword(s):  
Time Complexity ◽  
Search Algorithm ◽  
The Other ◽  
Graph Search ◽  
Path Finding ◽  
A Algorithm ◽  
Other Hand ◽  
The One ◽  
Memory Complexity

Abstract The A** algorithm is a famous heuristic path-finding algorithm. In this paper its different definitions will be analyzed firstly. Then its memory complexity is going to be investigated. On the one hand, the well-known concept of better-information will be extended to compare the different heuristics in the A** algorithm. On the other hand, a new proof will be given to show that there is no deterministic graph-search algorithm having better memory complexity than A**∗. At last the time complexity of A** will be discussed.

scholarly journals Parallelizing Bidirectional A* Algorithm

2020 ◽  
Author(s):  
Sumit Sharma ◽  
Shashwat Srijan ◽  
Vidhya J V
Keyword(s):  
Shortest Path ◽  
Autonomous Vehicles ◽  
Graphics Processing Units ◽  
General Purpose ◽  
Dijkstra’S Algorithm ◽  
Path Finding ◽  
A Algorithm ◽  
Simulation Games ◽  
Dijkstra's Algorithm ◽  
Execution Speed

Dijkstra’s algorithm is one of the simplest shortest path finding algorithm. A star(A*) algorithm is a variation of the shortest path first Dijkstra’s algorithm and is very commonly used in games using heuristics. The idea behind A* is to assign weight to each open node and then use a heuristic to calculate the cost from start to finish. A* uses heuristics and cost functions to find the most appropriate path in games and robotics. Games are needed to be fast and so we can have tradeoffs between speed and accuracy. So instead of accuracy we focus more on speed which is not needed in some of the situations like autonomous vehicles and simulation games. So, the A* algorithm may underestimate the costs but will never overestimate it. Bidirectional A*reduces the computation by calculating the shortest path from the source as well as the destination. A solution may be the General-Purpose Graphics Processing Units. It can be used to enhance the processing and execution speed of Bidirectional A* algorithm by using parallel processing and multiple threads. GPU based path finding may be approximately 45 times as fast as CPU pathfinding mechanism.

scholarly journals AN EFFICIENT Hybrid Swarm Intelligence Technique for Solving Integer Programming

2013 ◽  
Vol 10 (4) ◽  
pp. 1531-1538
Author(s):  
Mahmoud M. Ismail ◽  
Ibrahim M. El-henawy
Keyword(s):  
Integer Programming ◽  
Large Scale ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Research Area ◽  
Optimization Techniques ◽  
Computational Time ◽  
Hybrid Swarm ◽  
Integer Programming Problems ◽  
Scale Optimization

In this paper, a hybridization of two different swarm intelligent approaches, stochastic diffusion search, and particle swarm optimization techniques is presented  for solving integer programming problems. The hybrid implementation allows us to avoid certain drawbacks and weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. Our hybrid implementation allows the IP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the PSO with SDS approach for solving IP problems appears to be an interesting research area in combinatorial optimization. 

scholarly journals Integer Search Algorithm: A New Discrete Multi-Objective Algorithm for Pavement Maintenance Management Optimization

2021 ◽  
Vol 11 (15) ◽  
pp. 7170
Author(s):  
Abdulraaof Alqaili ◽  
Mohammed Qais ◽  
Abdullah Al-Mansour
Keyword(s):  
Search Algorithm ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Optimization Techniques ◽  
Maintenance Management ◽  
Stochastic Algorithm ◽  
Pavement Maintenance ◽  
Multi Objective ◽  
Study Results ◽  
Road Performance

Optimization techniques keep road performance at a good level using a cost-effective maintenance strategy. Thus, the trade-off between cost and road performance is a multi-objective function. This paper offers a new multi-objective stochastic algorithm for discrete variables, which is called the integer search algorithm (ISA). This algorithm is applied to an optimal pavement maintenance management system (PMMS), where the variables are discrete. The PMMS optimization can be achieved by maximizing the condition of pavement with a minimum cost at specified constraints, so the PMMS is a constrained multi-objective problem. The ISA and genetic algorithm (GA) are applied to improve the performance condition rating (PCR) of the pavement in developing countries, where the annual budget is limited, so a minimum cost for three years’ maintenance is scheduled. Study results revealed that the ISA produced an optimal solution for multi-function objectives better than the optimal solution of GA.

scholarly journals Sky-A* Algorithm for Artificial Intelligence

2019 ◽  
Vol 8 (4) ◽  
pp. 5295-5297
Keyword(s):  
Artificial Intelligence ◽  
Shortest Path ◽  
Search Algorithm ◽  
Optimal Path ◽  
Optimal Result ◽  
A Algorithm ◽  
Heuristic Knowledge ◽  
Limited Memory ◽  
And Performance ◽  
High Level

We have discussed variant of informed search in this paper like A* search. The informed search algorithm is developed to run in given limited memory by use of heuristic knowledge with retraction methods. We introduce the sky-A* algorithm for solving shortest path between two nodes. The sky-A* algorithm developed a logic by which the obligatory nodes like A* can extend and return the optimal result joining the nodes. In addition, the method sky-A* is guaranteed to return an optimal path when heuristic information used. We present a number of different methods for both low and high level procedures and analysis their results and performance. Proposed algorithm provides accurate combination of surroundings of video segments in situations when camera movements are complex.

scholarly journals PENENTUAN PERGERAKAN NON-PLAYER CHARACTER MENGGUNAKAN ALGORITMA A* PADA GAME ACTION- ROLE-PLAYING GAME

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Atthariq Atthariq ◽  
Dimas Ariandy Putra
Keyword(s):  
Shortest Path ◽  
Search Algorithm ◽  
Role Playing ◽  
A Algorithm ◽  
Interactive Animation ◽  
Game World ◽  
Role Playing Game ◽  
Living Creature

Abstrak— Game adalah salah satu bentuk dari animasi interaktif dimana player dapat berinteraksi dengan dunia game. Dalam sebuah game, salah satu unsur yang dapat dianggap penting untuk mendukung jalannya game dan realitas dari dunia game adalah NPC (Non-Player Character). NPC dapat membuat sebuah game menjadi lebih nyata dari segi cara Perpindahannya, maka dibutuhkan suatu algoritma pathfinding yang mampu membuat NPC tersebut melakukan perpindahan layaknya suatu makhluk hidup berpindah di dunia nyata. A*(A-Star) adalah algoritma pencarian yang dapat digunakan untuk melakukan pathfinding, dalam hal ini A Star akan digunakan untuk mencari suatu jarak terpendek antara NPC dan karakter player. Penelitian ini dilakukan untuk melakukan percobaan terhadap implementasi algoritma A* pada lingkungan game.Kata kunci—NPC (Non Player Character), pathfinding, Algoritma A Star, GameAbstract— Game is one form of interactive animation where the player can interact with the gaming world. In a game, one element that can be considered important to support the game and the reality of the game world is NPC (Non-Player Character). NPC can make a game becomes more real in terms of way of movement, then it takes a pathfinding algorithm that is able to make the NPC move like a living creature. A * (A-Star) is a search algorithm that can be used to perform pathfinding, in which case A Star will be used to find a shortest Path between NPC and player character. This research was conducted to experiment on the implementation of A* algorithm in game environment.Keywords— Game, A* Algorithm, NPC, pathfinding Algorithm

Sign in / Sign up

Export Citation Format

Share Document