science

Model-based reasoning

Model-based reasoning is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Model-based reasoning rather than just read about it. In short: 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.

Key takeaways

Model-based reasoning belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Model-based reasoning to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Model-based reasoning from memory before moving on to harder problems.

Reference excerpt

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.

Reasoning with declarative models

A robot and dynamical systems as well are controlled by software. The software is implemented as a normal computer program which consists of if-then-statements, for-loops and subroutines. The task for the programmer is to find an algorithm which is able to control the robot, so that it can do a task. In the history of robotics and optimal control there were many paradigm developed. One of them are expert systems, which is focused on restricted domains. Expert systems are the precursor to model based systems. The main reason why model-based reasoning is researched since the 1990s is to create different layers for modeling and control of a system. This allows to solve more complex tasks and existing programs can be reused for different problems. The model layer is used to monitor a system and to evaluate if the actions are correct, while the control layer determines the actions and brings the system into a goal state. Typical techniques to implement a model are declarative programming languages like Prolog and Golog. From a mathematical point of view, a declarative model has much in common with the situation calculus as a logical formalization for describing a system. From a more practical perspective, a declarative model means, that the system is simulated with a game engine. A game engine takes a feature as input value and determines the output signal. Sometimes, a game engine is described as a prediction engine for simulating the world. In 1990, criticism was formulated on model-based reasoning. Pioneers of Nouvelle AI have argued, that symbolic models are separated from underlying physical systems and they fail to control robots. According to behavior-based robotics representative a reactive architecture can overcome the issue. Such a system doesn't need a symbolic model but the actions are connected direct to sensor signals which are grounded in reality.

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:

∀ {\displaystyle \forall } patients : Stroke(patient) → {\displaystyle \rightarrow } Confused(patient) ∧ {\displaystyle \land } Unequal(Pupils(patient)) In contrast in a diagnostic reasoning system knowledge would be represented through diagnostic rules such as:

∀ {\displaystyle \forall } patients : Confused(patient) → {\displaystyle \rightarrow } Stroke(patient)

∀ {\displaystyle \forall } patients : Unequal(Pupils(patient)) → {\displaystyle \rightarrow } 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 (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

References

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

External links Model-based reasoning at Utrecht University NASA Intelligent Systems Division

Worked examples

Example 1 — a first encounter with Model-based reasoning

Start with the simplest possible case. Write down what Model-based reasoning claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Model-based reasoning before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Model-based reasoning ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Model-based reasoning

In research: Model-based reasoning appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Model-based reasoning in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Model-based reasoning is common in secondary-school and first-year university syllabi. It links to neighbouring topics Artificial intelligence stubs, Automated reasoning, Expert systems, so understanding it makes those chapters shorter.
In everyday life: Look for Model-based reasoning outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Model-based reasoning in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Model-based reasoning means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Model-based reasoning out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Model-based reasoning in simple terms?

Why does Model-based reasoning matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Model-based reasoning?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Model-based reasoning.