## 2 Path Planning  
In order to develop an efficient path from the start to the end node, a proper in-depth analysis has to be performed so that all the collisions or obstacles can be safely avoided while determining the least cost path in least time [4]. Considering factors such as collision avoidance, performance optimization, and static and dynamic constraints, several types of Algorithms have been developed to find an efficient path between two points [5]. Graph pathfinding Algorithms were created with the goal of discovering the shortest path between two vertices for a connect graph [6]. The main objective of these Algorithms was to only get the least distance within a graph without taking into consideration the performance optimization to reduce execution time and avoid obstacles [7]. These Algorithms were mostly used in networking the field particularly for developing routing protocols [8].  
Search Algorithms are another class of Algorithms that are mostly used in video games as well as in robotics to reach a particular target from the beginning [9]. These Algorithms cover the whole map of a given area and try to search for all the possible paths from source to destination and out of those, they select the shortest path. There are two types of search Algorithms, uninformed and informed search Algorithms. In the uninformed search Algorithms, which are also known as blind search, there is no information regarding the number of steps or the costs of path from current state to the goal. Some examples include the Breadth First Search and Depth First Search Algorithms. Another example of uninformed Algorithms is the Dijkstra's Algorithm which can be also used in graphs to find the shortest path but only for positive costs [10]. An improved version of the Algorithm was developed later on called the Bellman-Ford Algorithm which was for both positive as well as negative costs and is an uninformed Algorithm [11]. Another uninformed Algorithm is the Floyd-Warshall Algorithm which was inspired from these two to find the minimum path for positive and negative costs but the number of nodes to be visited was reduced in comparison with [PERSON]'s Algorithm [12].  
Informed search Algorithms are better optimized as compared to uninformed search Algorithms. The unique feature of these Algorithms is that they make use of a heuristic function to calculate the efficient path [13]. A search method or a heuristic function is informed if it uses additional information about nodes that have not yet been explored to decide which nodes to examine yet [14]. Heuristic function tries to search for a solution that is most near to the best solution in the shortest time [15]. The entire area does not have to be checked by limiting the Algorithm to a specific area, thereby saving significant amount of time. Some examples include A*, D*, HPA*, etc. [16].  
Deterministic, non-deterministic, and procedural generation are the various forms of game AI. This paper focuses on the various deterministic AI-based gaming Algorithms. This deterministic AI can be implemented using several tools like Behavior Tree, Decision Tree, etc. Based on stationary or in motion obstacles, it can be further divided into two categories, static and dynamic pathfinding. Static pathfinding can be categorized into uninformed search, informed search, and metaheuristic Algorithm. Various pathfinding Algorithms that have been analyzed and evaluated are shown in Figure 1. This work extensively makes use of the pathfinding-based Algorithms to analyze the efficiency of the model for the game play grid environment with and without obstacles.  
Figure 1: Classification of intelligence-based pathfinding Algorithms in gaming.