The Wumpus World is a classic artificial intelligence problem that demonstrates how an agent can navigate and reason in an uncertain environment. The agent starts in a four by four grid cave at position one one. The cave contains a deadly Wumpus monster, bottomless pits, and a piece of gold treasure. The agent's goal is to find the gold and return safely to the starting position.
The agent perceives its environment through five types of sensory input. A stench indicates the Wumpus is in an adjacent square. A breeze warns of nearby pits. Glitter means gold is in the current square. A scream confirms the Wumpus has been killed. A bump occurs when hitting a wall. The agent can only sense adjacent squares, creating uncertainty about exact hazard locations. In this example, the agent at position two one senses both stench and glitter, indicating the Wumpus is nearby and gold is present.
The agent maintains a knowledge base to track what it has learned about the environment. It knows which squares are safe, which have been visited, and uses logical reasoning to infer possible hazard locations. Green squares represent known safe areas, while yellow squares indicate uncertainty. The agent deduces that since it felt a breeze at position one one, there must be a pit at either one two or two one. When it moves to two one and finds gold without feeling a breeze, it can conclude the pit must be at one two. This logical inference helps the agent make informed decisions about where to move next.
The agent has several actions available: moving forward, turning left or right, grabbing gold, shooting an arrow, and climbing out of the cave. The key challenge is action selection under uncertainty. The agent must balance exploration with safety, using probabilistic reasoning when exact hazard locations are unknown. In this scenario, the agent has found the gold and must decide how to return safely. It can move right to a known safe square, risk moving up toward a possible pit, or shoot an arrow toward the suspected Wumpus location. The decision process involves grabbing the gold, evaluating possible moves, choosing the safest path, and returning to the starting position.
To summarize what we have learned about the Wumpus World problem: It demonstrates how artificial intelligence agents can reason and act under uncertainty. Agents use sensory percepts to build knowledge about their environment and make informed decisions. Logical inference allows them to deduce safe moves from limited information. The problem illustrates fundamental AI concepts including perception, reasoning, and action selection. These principles extend beyond games to real-world applications in robotics, autonomous systems, and intelligent decision-making.