Goal-based agents and utility-based agents are two types of intelligent agents that make decisions based on different criteria. Goal-based agents operate with specific objectives or goals in mind. They assess their actions based on whether these actions bring them closer to achieving the set goals. For example, an autonomous robot designed to clean a room would have the goal of ensuring that the room is free of clutter. The robot determines its actions by evaluating which tasks—like picking up objects or vacuuming—will attain that clean state effectively.
In contrast, utility-based agents take a more nuanced approach by considering a range of outcomes and their associated utilities, which represent the measure of satisfaction or value derived from those outcomes. Instead of a single goal, these agents are designed to maximize their overall utility. For instance, an autonomous vehicle would evaluate various aspects such as safety, travel time, and fuel efficiency when choosing a route. The decision it makes is based on calculating which route provides the highest overall utility rather than merely achieving a destination.
One key difference between the two types of agents lies in how they handle conflicts or trade-offs. Goal-based agents might struggle when faced with multiple competing goals, as they typically focus on achieving one primary objective at a time. Utility-based agents, however, can balance different considerations and make more informed decisions by weighing the benefits of various outcomes against one another. This makes utility-based agents more suitable for complex environments where preferences and priorities can change dynamically.