Examining Various Search Algorithms in AI With Appropriate Literature and Their Performances Against a Human Agent in the Snake Game

Abstract

Artificial intelligence (AI) is one of computer science's most crucial subfields. AI applications are growing rapidly. Games are the main place where artificial intelligence is actually used. AI is used to create the game's non-player characters, also referred to as NPCs. Although there are a lot of alternative ways to implement AI in a game, the search strategy is by far the most common. A variety of different search algorithms can be used to construct AI in video games. This paper's main objective is to use the snake game as a comparative tool to examine the differences between search algorithms employed by human agents and those used in AI. This paper presents detailed assessments of informed and uninformed search strategies as well as experiments on Hamiltonian search. This paper also addresses a few widely used techniques, like finite state machines, behavior trees, tree search, etc., frequently used while creating AI. We get to the conclusion that some algorithms are better candidates for use as search algorithms based on the results of thorough expla-nations and trials. We found that the Human Agent performs poorly when compared to the Depth-First Search, Breadth-First Search, Hamiltonian Search, and Best-First Search algorithms, while the A * Search algorithm surpasses them all significantly.