12-09-2013, 04:01 PM
Expert Systems
Expert System.ppt (Size: 545.5 KB / Downloads: 31)
Expert Systems (ES) are a popular and useful application area in AI.
Having studied KRR, it is instructive to study ES to see a practical manifestation of the principles learnt there.
What is an Expert?
Before we attempt to define an expert system, we have look at what we take the term ‘expert’ to mean when we refer to human experts. Some traits that characterize experts are:
They possess specialized knowledge in a certain area
They possess experience in the given area
They can provide, upon elicitation, an explanation of their decisions
They have a skill set that enables them to translate the specialized knowledge gained through experience into solutions.
History and Evolution
After the so-called dark ages in AI, expert systems were at the forefront of rebirth of AI.
There was a realization in the late 60’s that the general framework of problem solving was not enough to solve all kinds of problem.
Specialized knowledge is a very important component of practical systems.
Dendral (1960’s)
Dendral was one of the pioneering expert systems.
It was developed at Stanford for NASA to perform chemical analysis of Martian soil for space missions.
Given mass spectral data, the problem was to determine molecular structure.
In the laboratory, the ‘generate and test’ method was used; possible hypothesis about molecular structures were generated and tested by matching to actual data.
Control applications
In control applications, ES are used to adaptively govern/regulate the behavior of a system, e.g. controlling a manufacturing process, or medical treatment.
The ES obtains data about current system state, reasons, predicts future system states and recommends (or executes) adjustments accordingly. An example of such a system is VM (Fagan 1978).
This ES is used to monitor patient status in the intensive care unit.
It analyses heart rate, blood pressure and breathing measurements to adjust the ventilator being used by the patient.
Instruction and Simulation
ES may be used to guide the instruction of a student in some topic.
Tutoring applications include GUIDON (Clancey 1979), which instructs students in diagnosis of bacterial infections.
Its strategy is to present user with cases (of which it has solution).
It then analyzes the student’s response.
It compares the students approach to its own and directs student based on differences.
Backward chaining
Start with the goal.
Goal may be in WM initially, so check and you are done if found!
If not, then search for goal in the THEN part of the rules (match conclusions, rather than premises). This type of rule is called goal rule.
Check to see if the goal rule’s premises are listed in the working memory.
Premises not listed become sub-goals to prove.
Process continues in a recursive fashion until a premise is found that is not supported by a rule, i.e. a premise is called a primitive, if it cannot be concluded by any rule
When a primitive is found, ask user for information about it. Back track and use this information to prove sub-goals and subsequently the goal.
Expert System.ppt (Size: 545.5 KB / Downloads: 31)
Expert Systems (ES) are a popular and useful application area in AI.
Having studied KRR, it is instructive to study ES to see a practical manifestation of the principles learnt there.
What is an Expert?
Before we attempt to define an expert system, we have look at what we take the term ‘expert’ to mean when we refer to human experts. Some traits that characterize experts are:
They possess specialized knowledge in a certain area
They possess experience in the given area
They can provide, upon elicitation, an explanation of their decisions
They have a skill set that enables them to translate the specialized knowledge gained through experience into solutions.
History and Evolution
After the so-called dark ages in AI, expert systems were at the forefront of rebirth of AI.
There was a realization in the late 60’s that the general framework of problem solving was not enough to solve all kinds of problem.
Specialized knowledge is a very important component of practical systems.
Dendral (1960’s)
Dendral was one of the pioneering expert systems.
It was developed at Stanford for NASA to perform chemical analysis of Martian soil for space missions.
Given mass spectral data, the problem was to determine molecular structure.
In the laboratory, the ‘generate and test’ method was used; possible hypothesis about molecular structures were generated and tested by matching to actual data.
Control applications
In control applications, ES are used to adaptively govern/regulate the behavior of a system, e.g. controlling a manufacturing process, or medical treatment.
The ES obtains data about current system state, reasons, predicts future system states and recommends (or executes) adjustments accordingly. An example of such a system is VM (Fagan 1978).
This ES is used to monitor patient status in the intensive care unit.
It analyses heart rate, blood pressure and breathing measurements to adjust the ventilator being used by the patient.
Instruction and Simulation
ES may be used to guide the instruction of a student in some topic.
Tutoring applications include GUIDON (Clancey 1979), which instructs students in diagnosis of bacterial infections.
Its strategy is to present user with cases (of which it has solution).
It then analyzes the student’s response.
It compares the students approach to its own and directs student based on differences.
Backward chaining
Start with the goal.
Goal may be in WM initially, so check and you are done if found!
If not, then search for goal in the THEN part of the rules (match conclusions, rather than premises). This type of rule is called goal rule.
Check to see if the goal rule’s premises are listed in the working memory.
Premises not listed become sub-goals to prove.
Process continues in a recursive fashion until a premise is found that is not supported by a rule, i.e. a premise is called a primitive, if it cannot be concluded by any rule
When a primitive is found, ask user for information about it. Back track and use this information to prove sub-goals and subsequently the goal.