Expert Systems: 2D Inference Graph

What Expert Systems Are

Expert systems are computer programs designed to solve complex problems in specific domains by using knowledge from human experts. They operate based on predefined rules and can provide advice or solutions as if they were a human expert.

The 2D inference graph in this simulation allows you to visualize the process of how these systems infer new information from existing data, making it easier to understand their underlying mechanisms.

How Forward and Backward Chaining Work

Forward chaining starts with a set of known facts and applies rules to derive new facts. In the simulation, you can assert evidence nodes (facts) and see how they propagate through the graph, leading to further inferences.

Backward chaining begins by trying to prove a goal or hypothesis. It works backward from the desired conclusion, applying rules until it reaches known facts. The simulation supports this approach as well, allowing you to set goals and trace back the necessary conditions.

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Certainty Factors

Expert systems often deal with uncertain or incomplete information. Certainty factors are a way of quantifying the degree of confidence in a fact or rule. In the simulation, you can set certainty thresholds and observe how these values influence which rules fire and how conclusions are drawn.

This feature is particularly useful for understanding how expert systems handle uncertainty and make decisions based on probabilistic data.

Conflict Resolution Strategies

When multiple rules can be applied to the same set of facts, conflict resolution strategies determine which rule should fire. The simulation allows you to switch between different strategies, such as prioritizing more certain rules or applying all applicable rules simultaneously.

Understanding these strategies is crucial for optimizing the performance and accuracy of expert systems in real-world applications.

Frequently asked questions

What are certainty factors used for?

Certainty factors help manage uncertainty by assigning a numerical value to the degree of confidence in each fact or rule, allowing expert systems to make more informed decisions based on probabilistic data.

How does conflict resolution work in expert systems?

Conflict resolution strategies determine which rules should be applied when multiple rules can be used with the same set of facts. This ensures that the system makes logical and consistent decisions, avoiding contradictions or redundant conclusions.

Can expert systems handle real-world uncertainty effectively?

Yes, expert systems use techniques like certainty factors to manage uncertainty in real-world data. By quantifying the degree of confidence in each piece of information, these systems can make more reliable and context-aware decisions.

Why are 2D inference graphs useful for understanding expert systems?

2D inference graphs provide a visual representation of how rules and facts interact within an expert system. This visualization helps in understanding the flow of information, identifying potential conflicts, and optimizing rule applications.

Try it live

Everything above runs in your browser — open Expert Systems: 2D Inference Graph and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

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