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In artificial intelligence, model-based reasoning refers to an inference method used in expert systems based on a model of the physical world. With this approach, the main focus of application development is developing the model. Then at run time, an "engine" combines this model knowledge with observed data to derive conclusions such as a diagnosis or a prediction.

Knowledge representation

In a model-based reasoning system knowledge can be represented using causal rules. For example, in a medical diagnosis system the knowledge base may contain the following rule:

$\mathrm{\forall }$ patients : Stroke(patient) $\to$ Confused(patient) $\wedge$ Unequal(Pupils(patient))

In contrast in a diagnostic reasoning system knowledge would be represented through diagnostic rules such as:

$\mathrm{\forall }$ patients : Confused(patient) $\to$ Stroke(patient)

$\mathrm{\forall }$ patients : Unequal(Pupils(patient)) $\to$ Stroke(patient)

There are many other forms of models that may be used. Models might be quantitative (for instance, based on mathematical equations) or qualitative (for instance, based on cause/effect models.) They may include representation of uncertainty. They might represent behavior over time. They might represent "normal" behavior, or might only represent abnormal behavior, as in the case of the examples above. Model types and usage for model-based reasoning are discussed in.

See also

• Diagnosis

References

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• Template:Russell Norvig 2003

External links

• Model-based reasoning at Utrecht University
• NASA Intelligent Systems Division

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