A multi-agent adaptation of the rule-based Wumpus World game

Abstract

The Wumpus World scenario is an exploration into the application of artificial intelligence to navigate through a world with pits and a fictional character called the 'Wumpus'. The intent for the agent is to navigate through the world and find the gold without being mauled by the Wumpus or fall into a pit. The game is based exclusively on rules of behaviour upon perceptions at the agent's current location and previous locations stored in the agent's knowledge base. The intention of this paper is to present our developed knowledge base (KB)-based algorithms with implementation to the Wumpus World scenario to show proof-of-concept results. Using minimum remaining value and KB approach, single agent and multi-agent algorithms are developed and tested in simulation environment. In multi-agent scenario, the developed algorithm also constructs a common KB with perception inputs from different agents, and the common KB can be accessed by any agent in the system. As a part of larger intelligent robotic development effort, this implementation study seeks to take the traditional single agent book example and expand it to a multi-agent perspective. The simulation results show that our developed algorithms successfully performed in both single and multi-agent versions.