Simulation and Comparison of Efficency in Pathfinding algorithms in Games

Abstract

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) ,

A Review on Informed Search Algorithms for Video Games Pathfinding

“…Barnouti, Al-Dabbagh, & Sahib Naser [14] pointed out that the path problem in industrial games needs to be resolved in real-time, usually with restricted memory and limited CPU time. Reference in [15] supported the statements which stated that most common pathfinding challenges in games were massive memory usage and time-consuming in execution.…”

“…In pathfinding, environments are often represented as a graph or map. Although the term differs in the logical state, the term 'graph representation' is often used interchangeably with search space [15], map representation [11] [12], or game world [20]. Throughout this study, the term 'graph representation' will be used to refer to the pathfinding environment.…”

“…For the optimization purpose, the primary step is to consider changing the game's graph representation [15]. Selecting the best graph representation is often the first solution to optimize the pathfinding algorithm because it brings a considerable impact on the algorithm's implementation.…”

Analysing Large Inconsistent Knowledge Graphs Using Anti-patterns

“…In comparison with the mentioned works, this is the first work that relies on a simpler notion of justifications' generalisation, for the goal of analysing common logical errors in KGs, at the scale of the Web. Our method for detecting such anti-patterns reuses part of the work of [18], where the authors propose the efficient algorithm for path finding that we use in our subgraph generation. Our work can also be compared to an earlier large scale justification retrieval approach that uses MapReduce [26] but was mainly evaluated over synthetic data sets, and recent analyses [4,7,8], that aim at studying general characteristics of large KGs in the Web.…”