Thinking like human approach to AI for gameplaying

Abstract

Playing games helps humans relax and their minds. Games have a very long history. They were used as a leisure activity even by early humans. It involves both mind and body. With the development of computers games became more complex and entertaining. Computers are extensively used to model, simulate and develop games. Games require us to use our cognitive power to plan and take sudden discussions to win. Chess, Go are highly complex games in terms of options that a player must think about when making a move. There are a lot of different paths that a player can take to win a game. Therefore, many researchers try to use various technologies to develop applications which can play games. The development of these technologies also helps to solve complex problems in completely different areas such as finance, economics, and warfare. According to the other researchers work. Multi Agent Systems (MAS) have ability to modal complex problems. Because of that we developed a system using MAS to play games. Suggested solutions are developed using thinking of human’s behaviors. When humans face a problem, they think about it in several ways. In their mind they compare, contrast and argue to find the best possible solution. Similarly, in multi agent systems intelligent agents solve complex problems by communication, coordination and negotiation. Game playing is a problem solving that involves thinking, decision making and negotiation skills of the human mind. Even though this is very intuitive to the human mind proper designing needs to be done to model this way of problem solving into an AI system. Here we designed a multi agent game playing system to play N-puzzle game. It contains mainly two types of agents: coordinator agent and decision agents. For the N puzzle game there are four decision agents namely up, down, left, right decision agents. They analyze the game state, negotiate with each other to determine the best move to make. For the development of the solution, we used SPADE architecture-based multi agent framework. The results show the proposed solution able to achieve notable improvement (~42%) compared to human players.