Expert systems are computer programs that contain subject-specific knowledge from human experts. They use this knowledge along with inference engines to provide expert-level advice. An expert system's knowledge comes from knowledge engineers who gather information from subject matter experts and codify it for the system. Expert systems are comprised of a knowledge base containing factual and heuristic knowledge and a reasoning component that uses techniques like forward and backward chaining to draw conclusions. Some applications of expert systems include medical diagnosis, agricultural pest control advice, and educational tools.

1. Expert system
Presented by
Tahir Abbas
Abdual Wahab
Ayesha Liaqat
Munnaza Iqbal
Kiran Masroor

2. Expert system
 Expert system is a computer program that contains some
of the subject specific knowledge of one or more human
experts.
 These are complex AI programs
 Expert systems are generally software's

3. HISTORY OF EXPERT SYSTEM

4. Purpose of ES
 To make a program intelligent, provide it with lots of high
quality, specific knowledge about some problem of
specific area
 To clarify all the uncertainties come in system

5. PERFORMANCE OF ES
 Performance of expert system based on following method
 KNOWLEDGE ENGENEERING
 Building an expert system is known as KNOWLEDGE ENGINEERING.
 In this knowledge gathers from subject matter experts and then codifying this
knowledge according to the formalism.

6. Building blocks of ES
 Every expert system consist of two principal parts:
 (a) Knowledge base
 (b) Reasoning or inference
 Knowledge base
 It is expert systems contain both factual and heuristic knowledge.
 Factual knowledge is that knowledge of task domain that is widely
shared, typically found in textbooks or journals.
 Heuristic knowledge is less exhaustive, more experiential, more
judgmental knowledge of performance.

7.  Reasoning
 Two methods of reasoning when using inference rules:
 (i) Backward chaining: it starts with list of goals and works backward
if there is data which will allow it to conclude these goals.
 (ii) Forward chaining: it starts with data available and then concludes
a desired goal.

8. APPLICATIONS OF ES
 Its applications spread in a wide range i.e. in industrial and commercial
problems etc.
 Diagnosis and troubleshooting of devices and system of all kinds
 Planning and scheduling
 Configuration of manufactured objects
 Financial decision making
 Knowledge publishing
 Process monitoring and control

9. ADVANTAGES OF ES
 COSISTENT: it provides consistent answer for repetitive decisions, processes and tasks
 MAINTAINS: it holds and maintain levels of information
 CLARIFY: it clarify the logic of decision making
 NO HUMAN NEED: it cannot needs human, it works continuously
 MULTIUSER: a multi user expert system can serve more users at a time

10. DISADVANTAGES OF ES
 SENSE: it lacks common sense needed in decision making
 CREATIVENESS: it cannot respond creatively like a human expert would in unusual
circumstances
 ERRORS: in knowledge base errors may occur and this leads wrong decisions
 ENVIRONMENTS: if knowledge base is changed it cannot adapt changing
environments

11. APPLICATIONS OF ES IN DIFFERENT FIELDS
 In medical field
 In agricultural
 In education etc.

12. IN MEDICAL FIELD (EXAMPLE) (PXDES)
 It is example of medical expert system.
 It is a lung disease, X-ray diagnosis.
 It takes our lungs picture from upper side of body which looks like a shadow.
 The shadow is used to determine the type and degree of harmness.
 These systems include three modes:
 The knowledge base
 The explanation interface
 The knowledge acquisition
 (1) KNOWLEDGE BASE:-
 It contains the data of X-ray representations of various stages of the disease.
 (2) EXPLANATION INTERFACE :-
 It details the conclusion.
 (3) KNOWLEDGE ACQUISITION :-
 It allow medical experts to add or change information in the system.

14.  (2) CaDet
 It is for early cancer detection.
 Clinical data related to early cancer detection and to cancer risk factors was collected and
incorporated in database, together with heuristic rules for evaluating this data.
 (3) DXplain
 It is used for diagnosis.
 Its data based contain approximately 4,500 suggestion for over 2,000 different diseases.
 (4) MYCIN
 It is simple example of ES.
 It performs a task normally done by a human expert.
 It attempts to recommend appropriate therapies for patient with bacterial infections.
 It uses LISP structures for writing internally rules.
 It uses these rules to reason backward to the clinical data available from its goal of finding
disease-causing organism.
 (5) GERMWATCHER
 It is for infection control.

15. Agricultural ES (Examples)
 It uses to give answer about pest control, the need to spray, selection of a chemical to
spray, weather damage recovery such as freeze etc
 1) RICE-CROP DOCTOR:
 This ES is developed by NATIONAL INSTITUTE OF AGRICULTURAL EXTENSION
MANAGEMENT.
 Its main work is to diagnose pests and diseases for rice crop and suggest preventive
measures.
 It has knowledge about diseases and pests for identification and suggesting preventive
measures.

16.  (1) DISEASES :
 Rice blast
 Brown spots
 Rice tungro virus
 Bacterial leaf blight etc
 (2) PESTS:
 Stem borers
 Brown plant hopper
 Rice leaf folder
 Green leaf hopper etc

19.  AGRICULTURAL EXPERT SYSTEM
 (2) AGREX:
 It gives correct advice to farmers.
 Topics of advice are fertilizer application, crop protection, irrigation scheduling and
diagnosis of diseases in paddy and post harvest technology of fruits and
vegetables
 AGRICULTURAL EXPERT SYSTEM
 NAMES OF SOME OTHER EXPERT SYSTEMS:
 CLIPS
 GIS
 LEY
 CALEX

20. EXPERT SYSTEMS IN EDUCATION
 IN EDUCATIONFIELDS:
 Computer animation
 Computer science
 Engineering
 Language (expert system teaches language)