12_Expert Systems

Classical Expert Systems Many expert systems are based on rules Concepts of Knowledge Representation: INFERENCE Example Chinook 47 Helicopter Real-Time Implementation of Rule-Based Control System. Concepts of Knowledge Representation: DATA Animal Decision Tree PROGRAMMING LANGUAGES in Expert Systems PROGRAMMING LANGUAGES versus RULES
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1.EXPERT SYSTEMS

2.Review – Classical Expert Systems Can incorporate Neural, Genetic and Fuzzy Components

3.Many expert systems are based on rules

4.Expert Systems can perform many functions

5.Rules can be fuzzy, quantum, modal, neural, Bayesian, etc. Special inference methods may be used

6.Concepts of Knowledge Representation: INFERENCE

7.Inference versus Knowledge Representation

8.Forward chaining is data driven Backward chaining is goal driven

9.

10.Elements of a rule

11.Example Chinook 47 Helicopter System for testing and control

12.CONTROL SYSTEM with Failure Detection and Reconfiguration Executive control Aircraft Dynamics Controller ESTIMATOR SENSOR MEASUREMENTS Reconfiguration control signals Failure Detection Failure Diagnosis Failure Model Estimation RECONFIGURATION

13.Real-Time Implementation of Rule-Based Control System. Handelman and Stengel 1989

14.Real-Time Implementation of Rule-Based Control System. Handelman and Stengel 1989

15.High level control logic High level reconfiguration logic

16.True/False signal is propagated bottom up

17.True/False signal is propagated bottom up

18.Failure Response If then Else rules use measurement of specific ordered by them parameters Pitch Rate sensor stuck 14 deg /s from nominal Forward collective pitch control stuck 2.5cm from nominal (controls saturate at +/- 15cm

19.Inference can be fuzzy Inference can use neural net Inference can be based on search Inference can be probabilistic Inference can use higher-order-logic Any system, including a robot, can be made self-checking, fault – tolerant and reconfigurable Expert system based or not on fuzzy logic can be used for this task

20.Concepts of Knowledge Representation: DATA

21.Often used are: sets, schemes, frames and databases

22.Often used are: sets, schemes, frames and databases

23.Often used are: sets, schemes, frames and databases

24.Often used are: sets, schemes, frames and databases

25.Animal Decision Tree: Example Now we will illustrate some of these concepts on examples We will discuss forward chaining and backward chaining

26.Similar to “20 questions” Forward chaining is data driven Backward chaining is goal driven You see an animal, you ask what is this animal

27.Forward chaining is data driven Backward chaining is goal driven You know animal name, you ask why this is the specified by name animal, what attributes testify to this decision. Keep in mind, apply.

28.Animal Decision Tree Parameters Rules Programs

29.Animal Decision Tree Parameters Rules Programs