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Citation: Lawande, S. R. ; Jasmine, G. ; Anbarasi, J. ; Izhar, L. I. A Systematic Review and Analysis of Intelligence-Based Pathfinding Algorithms in the Field of Video Games. Appl. Sci. 2022,12, 5499. https://doi. org/10. 3390/app12115499 Academic Editor: Giancarlo Mauri Received: 25 March 2022 Accepted: 24 May 2022 Pu... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 2 of 30 most feasible solution. Some of these pathfinding Algorithms may take various obstacles into consideration and some may not. These pathfinding Algorithms require high memory and processing power to find the most optimal solution by avoiding all obstacles in order to reach the destination n... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 3 of 30 Appl. Sci. 2022, 12, x FOR PEER REVIEW 3 of 31 Figure 1. Classification of intelligence-based pathfinding Algorithms in gaming. Figure 1. Classification of intelligence-based pathfinding Algorithms in gaming. | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 4 of 30 2. 1. Pathfinding Using Grids A grid is a connection or a network of vertices or points through edges in order to form a graph (Figure 2). The performance of the pathfinding Algorithms is determined by attributes of the graph that is formed by the grid. The grids can be regular or irregul... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 5 of 30 Appl. Sci. 2022, 12, x FOR PEER REVIEW 5 of 31 triangles are the ones with no obstacles present. The area covered with black triangles shows the region covered by the yellow obstacles. 2. 2D square (octile) grid: These are the most widely used grids to represent graphs in the gaming ind... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 6 of 30 ment. Not much study has been done on this grid system for pathfinding, but it can be applied in other fields such as image processing and computer graphics [18]. 2. 1. 2. Irregular Grids Irregular grids are not formed from the smallest unit shape unlike the regular grids. On the basis of... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 7 of 30 Table 1. Grid classification based on grid type, grid dimension, smallest unit, and applications. Grid Grid Type Grid Dimension Smallest Unit Applications Ref. Year Triangular Regular 2DEquilateral Triangle Computer Graphics, Image Processing[18] 2015 Square Regular 2D Square Video Games... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 8 of 30 Appl. Sci. 2022, 12, x FOR PEER REVIEW 8 of 31 Figure 5. Breadth first search traversal. In Figure 5, the BFS Algorithm will traverse the vertices in breadth-wise fashion from the top node in the graph. First, vertex 1 will be visited since it is at the top and put in queue. Next, the c... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 9 of 30 loop. The time T(n) and space S(n) complexity is given in Equations (3) and (4) where d refers to the depth of the search tree and nirefers to number of nodes in level i. T(n) = 1 + n2+ n3+...... + nd= O(nd) (3) S(n) = O(n×d) (4) Appl. Sci. 2022, 12, x FOR PEER REVIEW 9 of 31 infinite l... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 10 of 30 For Dijkstra's Algorithm, G(n) which is the cost required to move from start node to present node 'n', and heuristic value H(n), which is the acceptable cost to move from present node to target node, are assigned to 0 for Dijkstra's Algorithm 2. It cannot be overestimated and is given ... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 11 of 30 a bit overestimated, the most promising and optimal path is found by the Algorithm in less time [42]. 3. 2. 1. A-Star Algorithm This is one of the most famous Algorithms used in the gaming industry due to its simplicity [ 43]. Published in 1986 by Hart, Nilsson, and Raphael, the main o... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 12 of 30 Algorithm 3 A* Algorithm. Input: Set of all vertices. Output: Search Path sequence vertices S_ASR[] Step 1: Setd[s] = 0, S_ASR[] = φ, where sis the source vertex and S_ASR[] is an array having all the visited vertices Step 2: For all vertices vexcept s,setd[v] =∞ Step 3 : Find qnot in ... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 13 of 30 state means it is no longer kept in the open list [55]. The RAISE state refers to the fact that the cost of the node was higher than the previous time it was kept on the open list [ 56]. Similarly, LOWER state means that the cost of the node was lower than the previous time it was kept... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 14 of 30 sometimes we need to consider one or two nodes that are not natural. Those are called “forced neighbors” [ 70]. In Jump Point Search, our objective is to avoid symmetric paths by “jumping” all nodes that can be optimally reached by a path that does not visit the current node. This mean... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 15 of 30 techniques. Unlike the heuristic Algorithms, they do not take advantage of any specificity of the problem. In general, they are not greedy. In fact, they may even accept a temporary deterioration of the solution (example, the simulated-annealing technique), which allows them to explore ... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 16 of 30 Appl. Sci. 2022, 12, x FOR PEER REVIEW 16 of 31 1. Initial Population: The Algorithm begins with a set of individuals called a Popula-tion. Each individual is a solution to the problem you want to solve. An individual is characterized by a set of parameters (variables) known as Genes. ... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 17 of 30 Appl. Sci. 2022, 12, x FOR PEER REVIEW 17 of 31 Figure 12. Top left: crossover point, top right: exchanging genes among parents, bottom: new off-spring. Figure 13. Mutation: before and after. 3. 3. 2. Ant Colony Optimization (ACO) Ant Colony Optimization was first introduced in 1992 by... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 18 of 30 2. Reinforcement phase wherein an amount of pheromone is deposited by every ant which is proportional to the fitness of solution. The process is repeated until halting criteria is achieved. Appl. Sci. 2022, 12, x FOR PEER REVIEW 18 of 31 2. Reinforcement phase wherein an amount of phero... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 19 of 30 Algorithm 6 Proposed GRIN_PF. Input: Grid Giwhere i∈4×4, 8×8, 16×16, 32×32. Number of iterations k. Output: Shortest Path Length L, Visited Blocks V, Execution Time t. Step 1: Formation of regular Grid of equal size cell with obstacles Gioand without obstacles Gino Step 2: Initialize t... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 20 of 30 Table 3. Cont. Grid Size Grid Model Algorithm Total Iterations (Blocks)Total Computed Blocks without SPLExecution Time (ms)Shortest Path Length (Blocks) 8×8 Appl. Sci. 2022, 12, x FOR PEER REVIEW 20 of 31 2 A-star 10 5 27. 95 5 3 Breadth First Search 14 9 33. 76 5 4 Greedy Best First 6... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 21 of 30 Table 3. Cont. Grid Size Grid Model Algorithm Total Iterations (Blocks)Total Computed Blocks without SPLExecution Time (ms)Shortest Path Length (Blocks) 32×32 Appl. Sci. 2022, 12, x FOR PEER REVIEW 21 of 31 16 × 16 9 Dijkstra's 254 225 899. 27 29 10 A-star 226 197 787. 43 29 11 Breadth... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 22 of 30 Table 4. Cont. Grid Size Grid Model Algorithm Total Iterations (Blocks)Total Computed Blocks without SPLExecution Time (ms)Shortest Path Length (Blocks) 8×8 Appl. Sci. 2022, 12, x FOR PEER REVIEW 22 of 31 Table 4. Performance analysis of pathfinding Algorithms with obstacles. Grid Size... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 23 of 30 Table 4. Cont. Grid Size Grid Model Algorithm Total Iterations (Blocks)Total Computed Blocks without SPLExecution Time (ms)Shortest Path Length (Blocks) 32×32 Appl. Sci. 2022, 12, x FOR PEER REVIEW 23 of 31 16 × 16 25 Dijkstra's 177 146 743. 47 31 26 A-star 140 109 512. 45 31 27 Breadt... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 24 of 30 Appl. Sci. 2022, 12, x FOR PEER REVIEW 24 of 31 6. Graphical Representation of Implemented Pathfinding Algorithms Figure 15 shows a graph that compares the performances of the four implemented Algorithms without taking the obstacles into consideration. The execution time is taken as a ... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 25 of 30 Appl. Sci. 2022, 12, x FOR PEER REVIEW 26 of 31 Figure 16. Graphical representation of pathfinding Algorithms with obstacles. From Figure 16, we can conclude that on average, the overall fastest pathfinding Al-gorithm for with obstacles, which required the least average memory and leas... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 26 of 30 The worst Algorithm on average for without obstacles requiring most memory usage was Dijkstra's Algorithm. The second-best Algorithm was found to be A-star, followed by Breadth First Search. Moreover, it can be seen that as the grid size increases, the difference in the execution times... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 27 of 30 Table 5. Cont. Compared Algorithms Algorithm Category Execution Time Memory Usage Implemented Author Year Better Algorithm 1. HPA* 2. JPS1. Informed 2. Informed1 < 2JPS requires more memory than HPA*1. No 2. No [92] 2015 HPA* 1. Dijkstra's 2. GA1. Informed 2. Metaheuristic1 > 2Dijkstra... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 28 of 30 4. Foudil, C. ; Noureddine, D. ; Sanza, C. ; Duthen, Y. Path finding and collision avoidance in crowd simulation. J. Comput. Inf. Technol. 2009,17, 217-228. [Cross Ref] 5. Anbuselvi, R. ; Phil, M. Path finding solutions for grid based graph. Adv. Comput. Int. J. 2013,4, 51-60. [Cross Ref... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 29 of 30 37. Neukart, F. ; Morar, S. A. Operations on quantum physical artificial neural structures. Procedia Eng. 2014,69, 1509-1517. [Cross Ref] 38. Hart, P. E. ; Nilsson, N. J. ; Raphael, B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 1... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
Appl. Sci. 2022,12, 5499 30 of 30 67. Foead, D. ; Ghifari, A. ; Kusuma, M. B. ; Hanafiah, N. ; Gunawan, E. A systematic literature review of A* pathfinding. Procedia Comput. Sci. 2021,179, 507-514. [Cross Ref] 68. Zhou, R. ; Hansen, E. Multiple sequence alignment using Anytime A*. In Proceedings of the 18th National Conf... | A_Systematic_Review_and_Analysis_of_Intelligence-B.pdf |
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