Channel: IBM Technology
Date Published: 2024-05-02
Summary
This video explains the five main types of AI agents, differentiating them based on their intelligence, decision-making processes, and interaction with their environments. It begins with the simple reflex agent, which follows predefined rules like a thermostat, reacting to immediate conditions without memory. Next, the model-based reflex agent is introduced as an advanced version that incorporates an internal model of the world, tracking how actions affect the environment, as exemplified by a robotic vacuum cleaner. The goal-based agent builds on the model-based agent by making decisions based on goals, simulating future outcomes to determine the best action, such as a self-driving car navigating to a destination.
The utility-based agent considers not just goal completion but also the desirability of different outcomes, using a utility or happiness score to rank options, as shown with an autonomous drone delivery optimizing for speed, safety, and energy usage. Finally, the learning agent is presented as the most adaptable, learning from experience and improving performance over time through feedback, exemplified by an AI chess bot that adjusts its strategy based on game outcomes. The video concludes by noting that multi-agent systems involve multiple agents working cooperatively in a shared environment, and emphasizes that while AI agents are becoming increasingly capable, they often work best with human oversight.
Recommendations
- AI agents typically work best with human oversight, at least for the time being.