Model Based Reasoning in AI
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.
- patients : Stroke(patient) Confused(patient) Unequal(Pupils(patient))
- patients : Confused(patient) Stroke(patient)
- patients : Unequal(Pupils(patient)) 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.
- Diagnosis (artificial intelligence), determining if a system's behavior is correct
- Behavior selection algorithm
- Case-based reasoning, solving new problems based on solutions of past problems
- Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, p. 260, ISBN 0-13-790395-2
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