Learn How AI Agents Use Knowledge to Make Smarter, Faster Decisions (Without Jargon)

Knowledgeable people run the world.

But what if AI had the capacity to hold and utilize knowledge?

Spoiler: It already does.

To add value to your personal or business AI strategy, it’s important to understand logical AI agents. In this article, I’ll walk you through what a logical AI agent is—not in textbook depth—but with enough clarity for you to grasp its essence and consider how they could impact your world.

What is a Logical AI Agent?

Imagine an logical AI agent as a piece of software that takes actions inside an environment—much like how you navigate your computer or mobile device with your mouse or touchscreen. But instead of intuitively clicking and tapping, the agent uses logical reasoning to make decisions. Its environment could be your desktop, a specific application, a video game, or even a simulated factory.

At the heart of its decision-making process is something powerful: knowledge. A logical AI agent leverages a knowledge base—a repository of facts and information about the world. As it operates in the environment, it can add information to its knowledge base. With this knowledge, it can analyze its environment, draw conclusions, and ultimately take action to achieve its goals.

Key Components of a Logical AI Agent:

1. Sensors: Perceive the environment (whether virtual or real-world).
2. Knowledge base: Stores information about the world.
3. Inference engine: Draws conclusions based on the knowledge base.
4. Action set: A list of actions the agent can take in its environment.
5. Environment: The space where the agent operates.
6. Performance benchmarks: Criteria the agent uses to evaluate its actions.

Key Features of Agents

Unlike static software, agents can adapt to new information and environments. They learn from experience and observation, much like humans do. Here are some of the remarkable features of AI agents:

• Task adaptability: Agents can be assigned new goals on the fly.
• Learning: Through direct input or environmental observation.
• Adaptation: Agents can handle unexpected changes in their environment.
• Reinforcement learning: They can improve over time by learning which actions lead to better results.
• Variance: Agents use many different kinds of algorithms to reason and achieve results.

How Do Logical AI Agents Reason?

The reasoning process for a logical AI agent mirrors human problem-solving—though it’s often faster, more thorough, and based on mathematical principles. Here’s a simplified version of how an agent might verify a proposition:

1. Start with a proposition to verify (a goal it needs to achieve).
2. Examine the knowledge base for what it already knows about the world.
3. Consider possible scenarios the agent might encounter in achieving the goal.
4. Eliminate scenarios that contradict the knowledge base.
5. Analyze the remaining scenarios to see if they align with the goal.
6. If the goal holds true in all scenarios, the agent concludes it’s true and moves in that direction. If the goal does not hold true in any scenarios, it’s false and it does not take that path. If it’s true in some and false in others, the agent considers the goal uncertain and takes actions using probability.

In simpler terms: AI is trying to achieve a goal. It evaluates all possible pathways to get there, filters out actions that contradict what it knows, and picks the actions that are most likely to succeed. When certainty is elusive, it uses probabilities to make its next move.

Example: Minecraft Decision-Making

Let’s ground this abstract idea in a more familiar context—Minecraft. Imagine an AI agent tasked with building a house out of wooden planks.

1. The agent knows certain facts from its knowledge base: Gathering wood makes planks, falling into lava leads to death, and monsters come out at night.
2. It examines all the possible actions: gathering wood, exploring caves, building at night, etc.
3. Based on what it knows, it eliminates actions that contradict its goal. For instance, it discards the option of building at night due to the danger of monsters.
4. The agent now has a refined set of actions that align with the goal.
5. Since some outcomes are still uncertain (e.g., it may not know how many monsters will appear), it calculates the most likely pathway to successfully build the house.

Now imagine using engineering design software with real physics equations and using agents to design more sustainable materials with chemical knowledge bases, or using agents to extract information from the internet, or using agents to control software like Adobe to edit a video.

In the end, the agent takes actions based on the most probable way to achieve its goal, learning from successes and failures along the way. This mirrors human decision-making, but the agent processes vast possibilities with precision and speed… avoiding futures that contradict its goals and aligning with futures that help it complete its goal. Agents attempt the same task thousands of times and receive human feedback — and in effect slowly get better over time.

Takeaway

The power of knowledge-based logical AI agents lies in their ability to learn, adapt, and reason through complex environments—whether in video games like Minecraft or real-world business applications like designing houses. Understanding how these agents function can help you apply them more effectively in your organization’s AI strategy, enabling more informed decision-making, adaptability, and success.

When you start to very carefully structure the data in the knowledge base, such as by using knowledge graphs, things start to get interesting.

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Source: Artificial Intelligence, A Modern Approach, by Stuart J. Russel and Peter Norvig.