Model-based reasoning
Encyclopedia
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.
can be represented
using causal rules. For example, in a medical diagnosis system the knowledge base
may contain the following rule:
In contrast in a diagnostic reasoning system knowledge would be represented through diagnostic rules such as:
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 .
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...
, model-based reasoning refers to an inference
Inference
Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic.Human inference Inference is the act or process of deriving logical conclusions...
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 knowledgeKnowledge
Knowledge is a familiarity with someone or something unknown, which can include information, facts, descriptions, or skills acquired through experience or education. It can refer to the theoretical or practical understanding of a subject...
can be represented
Knowledge representation
Knowledge representation is an area of artificial intelligence research aimed at representing knowledge in symbols to facilitate inferencing from those knowledge elements, creating new elements of knowledge...
using causal rules. For example, in a medical diagnosis system the knowledge base
Knowledge base
A knowledge base is a special kind of database for knowledge management. A Knowledge Base provides a means for information to be collected, organised, shared, searched and utilised.-Types:...
may contain the following rule:
-
patients : Stroke(patient) 
Confused(patient) 
Unequal(Pupils(patient))
In contrast in a diagnostic reasoning system knowledge would be represented through diagnostic rules such as:
-
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 .