UNIT5 Expert Systems Topics 1 Expert Systems 2
![UNIT-5 Expert Systems Topics: 1. Expert Systems 2. Architecture of expert system 3. Roles UNIT-5 Expert Systems Topics: 1. Expert Systems 2. Architecture of expert system 3. Roles](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-1.jpg)
![Artificial Intelligence n AI n The ability of computers to duplicate the functions of Artificial Intelligence n AI n The ability of computers to duplicate the functions of](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-2.jpg)
![Interesting Statistics n It has been estimated that computers that can exhibit humanlike intelligence Interesting Statistics n It has been estimated that computers that can exhibit humanlike intelligence](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-3.jpg)
![The Difference Between Natural & Artificial Intelligence Attributes Use Sensors Creativity and Imagination Learn The Difference Between Natural & Artificial Intelligence Attributes Use Sensors Creativity and Imagination Learn](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-4.jpg)
![The Major Branches of AI(application of AI) 5 The Major Branches of AI(application of AI) 5](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-5.jpg)
![Expert Systems (ES) 6 Expert Systems (ES) 6](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-6.jpg)
![7 7](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-7.jpg)
![Capabilities of Expert System 8 Capabilities of Expert System 8](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-8.jpg)
![Components of Expert System 9 Components of Expert System 9](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-9.jpg)
![Components of ES 10 Components of ES 10](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-10.jpg)
![Components of an Expert System 11 Components of an Expert System 11](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-11.jpg)
![Components of an Expert System Knowledge Base Stores all relevant information, data, rules, cases, Components of an Expert System Knowledge Base Stores all relevant information, data, rules, cases,](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-12.jpg)
![The Knowledge Base n Stores all relevant information, data, rules, cases, and relationships used The Knowledge Base n Stores all relevant information, data, rules, cases, and relationships used](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-13.jpg)
![14 14](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-14.jpg)
![Components of an Expert System Inference Engine Seeks information and relationships from the knowledge Components of an Expert System Inference Engine Seeks information and relationships from the knowledge](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-15.jpg)
![The Inference Engine n Seeks information and relationships from the knowledge base and provides The Inference Engine n Seeks information and relationships from the knowledge base and provides](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-16.jpg)
![The Inference Engine Figure 7. 4: Rules for a Credit Application 17 The Inference Engine Figure 7. 4: Rules for a Credit Application 17](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-17.jpg)
![To recommend a solution, the interface engine uses the following strategies − n Forward To recommend a solution, the interface engine uses the following strategies − n Forward](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-18.jpg)
![19 19](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-19.jpg)
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![Components of an Expert System Explanation Facility Allows a user to understand how the Components of an Expert System Explanation Facility Allows a user to understand how the](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-21.jpg)
![Components of an Expert System Knowledge acquisition facility Provide convenient and efficient means of Components of an Expert System Knowledge acquisition facility Provide convenient and efficient means of](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-22.jpg)
![Components of an Expert System User Interface Specialized user interface software employed for designing, Components of an Expert System User Interface Specialized user interface software employed for designing,](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-23.jpg)
![Expert system Technology 24 Expert system Technology 24](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-24.jpg)
![Expert Systems Development Figure 7. 6: Steps in the Expert System Development Process 25 Expert Systems Development Figure 7. 6: Steps in the Expert System Development Process 25](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-25.jpg)
![Participants in Expert System Development 26 Participants in Expert System Development 26](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-26.jpg)
![Participants in Expert System Development n Domain n The area of knowledge addressed by Participants in Expert System Development n Domain n The area of knowledge addressed by](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-27.jpg)
![Application of ES 28 Application of ES 28](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-28.jpg)
![Benefits of Expert System 29 Benefits of Expert System 29](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-29.jpg)
![Limitations of an Expert System n Not widely used or tested n Difficult to Limitations of an Expert System n Not widely used or tested n Difficult to](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-30.jpg)
![Expert System Shells 31 Expert System Shells 31](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-31.jpg)
![Expert System Shells n The shell is a piece of software which contains the Expert System Shells n The shell is a piece of software which contains the](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-32.jpg)
![33 33](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-33.jpg)
![Components of an expert system User Expert system shell User Inter face Explanation system Components of an expert system User Expert system shell User Inter face Explanation system](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-34.jpg)
![Expert System Shells n In the 1980 s, expert system "shells" were introduced and Expert System Shells n In the 1980 s, expert system "shells" were introduced and](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-35.jpg)
![36 36](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-36.jpg)
![MYCIN 37 MYCIN 37](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-37.jpg)
![n MYCIN was an early expert system that used artificial intelligence to identify bacteria n MYCIN was an early expert system that used artificial intelligence to identify bacteria](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-38.jpg)
![n MYCIN was a standalone system that required a user to enter all relevant n MYCIN was a standalone system that required a user to enter all relevant](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-39.jpg)
![Tasks and Domain n Disease DIAGNOSIS and Therapy SELECTION n Advice for non-expert physicians Tasks and Domain n Disease DIAGNOSIS and Therapy SELECTION n Advice for non-expert physicians](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-40.jpg)
![MYCIN Architecture 41 MYCIN Architecture 41](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-41.jpg)
![Consultation System n Performs Diagnosis and Therapy Selection n Control Structure reads Static DB Consultation System n Performs Diagnosis and Therapy Selection n Control Structure reads Static DB](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-42.jpg)
![Consultation “Control Structure” n n Goal-directed Backward-chaining Depth-first Tree Search High-level Algorithm: 1. 2. Consultation “Control Structure” n n Goal-directed Backward-chaining Depth-first Tree Search High-level Algorithm: 1. 2.](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-43.jpg)
![Static Database n Rules n Meta-Rules n Templates n Rule Properties n Context Properties Static Database n Rules n Meta-Rules n Templates n Rule Properties n Context Properties](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-44.jpg)
![Dynamic Database n Patient Data n Laboratory Data n Context Tree n Built by Dynamic Database n Patient Data n Laboratory Data n Context Tree n Built by](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-45.jpg)
![Explanation System n Provides reasoning why a conclusion has been made, or why a Explanation System n Provides reasoning why a conclusion has been made, or why a](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-46.jpg)
![DART n DART is a joint project of the Heuristic Programming Project and IBM DART n DART is a joint project of the Heuristic Programming Project and IBM](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-47.jpg)
![n Dynamic Analysis and Replanning Tool n DART uses intelligent agents to aid decision n Dynamic Analysis and Replanning Tool n DART uses intelligent agents to aid decision](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-48.jpg)
![Xcon n The R 1 (internally called XCON, for e. Xpert CONfigurer) program was Xcon n The R 1 (internally called XCON, for e. Xpert CONfigurer) program was](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-49.jpg)
![n XCON interacted with the sales person, asking critical questions before printing out a n XCON interacted with the sales person, asking critical questions before printing out a](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-50.jpg)
![XCON: Expert Configurer Stages of Expert System building n Identification: Problems, data, goals, company, XCON: Expert Configurer Stages of Expert System building n Identification: Problems, data, goals, company,](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-51.jpg)
![Phase 1: Identification n DEC, Digital Equipment Corporation n n n n Large computer Phase 1: Identification n DEC, Digital Equipment Corporation n n n n Large computer](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-52.jpg)
![Phase 2: Conceptualization Con. . what? Expert Systems 14 53 Phase 2: Conceptualization Con. . what? Expert Systems 14 53](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-53.jpg)
![Phase 3: Formalization n Configuration engineers could talk well to Knowledge Engineers of the Phase 3: Formalization n Configuration engineers could talk well to Knowledge Engineers of the](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-54.jpg)
![Phase 4: Implementation into system R 1 n Language: OPS 5 (similar to CLIPS) Phase 4: Implementation into system R 1 n Language: OPS 5 (similar to CLIPS)](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-55.jpg)
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![Important questions PART-B 1. Expert system (ES)? architecture of expert system? (components of Expert Important questions PART-B 1. Expert system (ES)? architecture of expert system? (components of Expert](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-59.jpg)
![PART-A (2 marks) 1. Expert system(ES)? 2. Application of ES? 3. List advantage & PART-A (2 marks) 1. Expert system(ES)? 2. Application of ES? 3. List advantage &](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-60.jpg)
- Slides: 60
![UNIT5 Expert Systems Topics 1 Expert Systems 2 Architecture of expert system 3 Roles UNIT-5 Expert Systems Topics: 1. Expert Systems 2. Architecture of expert system 3. Roles](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-1.jpg)
UNIT-5 Expert Systems Topics: 1. Expert Systems 2. Architecture of expert system 3. Roles of expert systems 4. Knowledge Acquisition 5. Meta knowledge 6. Typical expert systems. MYCIN, DART, XOON, Expert systems shell
![Artificial Intelligence n AI n The ability of computers to duplicate the functions of Artificial Intelligence n AI n The ability of computers to duplicate the functions of](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-2.jpg)
Artificial Intelligence n AI n The ability of computers to duplicate the functions of the human brain 2
![Interesting Statistics n It has been estimated that computers that can exhibit humanlike intelligence Interesting Statistics n It has been estimated that computers that can exhibit humanlike intelligence](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-3.jpg)
Interesting Statistics n It has been estimated that computers that can exhibit humanlike intelligence (including musical and artistic aptitude, creativity, physical movement physically, and emotional responsiveness) require processing power of 20 million billion calculations per second (by the year 2030? ). 3
![The Difference Between Natural Artificial Intelligence Attributes Use Sensors Creativity and Imagination Learn The Difference Between Natural & Artificial Intelligence Attributes Use Sensors Creativity and Imagination Learn](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-4.jpg)
The Difference Between Natural & Artificial Intelligence Attributes Use Sensors Creativity and Imagination Learn from Experience Human High Machine Low Low Adaptability Access external information Make complex calculations Transfer information High Low Low High 4
![The Major Branches of AIapplication of AI 5 The Major Branches of AI(application of AI) 5](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-5.jpg)
The Major Branches of AI(application of AI) 5
![Expert Systems ES 6 Expert Systems (ES) 6](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-6.jpg)
Expert Systems (ES) 6
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![Capabilities of Expert System 8 Capabilities of Expert System 8](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-8.jpg)
Capabilities of Expert System 8
![Components of Expert System 9 Components of Expert System 9](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-9.jpg)
Components of Expert System 9
![Components of ES 10 Components of ES 10](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-10.jpg)
Components of ES 10
![Components of an Expert System 11 Components of an Expert System 11](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-11.jpg)
Components of an Expert System 11
![Components of an Expert System Knowledge Base Stores all relevant information data rules cases Components of an Expert System Knowledge Base Stores all relevant information, data, rules, cases,](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-12.jpg)
Components of an Expert System Knowledge Base Stores all relevant information, data, rules, cases, and relationships used by the expert system. Uses • Rules • If-then Statements • Fuzzy Logic 12
![The Knowledge Base n Stores all relevant information data rules cases and relationships used The Knowledge Base n Stores all relevant information, data, rules, cases, and relationships used](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-13.jpg)
The Knowledge Base n Stores all relevant information, data, rules, cases, and relationships used by the expert system n Assembling human experts n Use of fuzzy logic n A special research area in computer science that allows shades of gray and does not require everything to be simple black/white, yes/no, or true/false n Use of rules n Conditional statement that links given conditions to actions or outcomes n E. g. if-then statements n Use of cases 13
![14 14](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-14.jpg)
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![Components of an Expert System Inference Engine Seeks information and relationships from the knowledge Components of an Expert System Inference Engine Seeks information and relationships from the knowledge](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-15.jpg)
Components of an Expert System Inference Engine Seeks information and relationships from the knowledge base and provides answers, predictions, and suggestions the way a human expert would. Uses • Backward Chaining • Forward Chaining 15
![The Inference Engine n Seeks information and relationships from the knowledge base and provides The Inference Engine n Seeks information and relationships from the knowledge base and provides](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-16.jpg)
The Inference Engine n Seeks information and relationships from the knowledge base and provides answers, predictions, and suggestions the way a human expert would n Forward chaining(Goal driven Reasoning) n Starting with the facts and working forwards to the conclusions n Backward chaining(Data driven Reasoning ) n Starting with conclusions and working backward to the supporting facts 16
![The Inference Engine Figure 7 4 Rules for a Credit Application 17 The Inference Engine Figure 7. 4: Rules for a Credit Application 17](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-17.jpg)
The Inference Engine Figure 7. 4: Rules for a Credit Application 17
![To recommend a solution the interface engine uses the following strategies n Forward To recommend a solution, the interface engine uses the following strategies − n Forward](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-18.jpg)
To recommend a solution, the interface engine uses the following strategies − n Forward Chaining n Backward Chaining 18
![19 19](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-19.jpg)
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![Components of an Expert System Explanation Facility Allows a user to understand how the Components of an Expert System Explanation Facility Allows a user to understand how the](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-21.jpg)
Components of an Expert System Explanation Facility Allows a user to understand how the expert system arrived at certain conclusions or results. For example: it allows a doctor to find out the logic or rationale of the diagnosis made by a medical expert system 21
![Components of an Expert System Knowledge acquisition facility Provide convenient and efficient means of Components of an Expert System Knowledge acquisition facility Provide convenient and efficient means of](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-22.jpg)
Components of an Expert System Knowledge acquisition facility Provide convenient and efficient means of capturing and storing all the components of the knowledge base. Acts as an interface between experts and the knowledge base. 22
![Components of an Expert System User Interface Specialized user interface software employed for designing Components of an Expert System User Interface Specialized user interface software employed for designing,](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-23.jpg)
Components of an Expert System User Interface Specialized user interface software employed for designing, creating, updating, and using expert systems. The main purpose of the user interface is to make the development and use of an expert system easier for users and decision makers 23
![Expert system Technology 24 Expert system Technology 24](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-24.jpg)
Expert system Technology 24
![Expert Systems Development Figure 7 6 Steps in the Expert System Development Process 25 Expert Systems Development Figure 7. 6: Steps in the Expert System Development Process 25](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-25.jpg)
Expert Systems Development Figure 7. 6: Steps in the Expert System Development Process 25
![Participants in Expert System Development 26 Participants in Expert System Development 26](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-26.jpg)
Participants in Expert System Development 26
![Participants in Expert System Development n Domain n The area of knowledge addressed by Participants in Expert System Development n Domain n The area of knowledge addressed by](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-27.jpg)
Participants in Expert System Development n Domain n The area of knowledge addressed by the expert system n Domain Expert n The individual or group who has the expertise or knowledge one is trying to capture in the expert system n Knowledge Engineer n An individual who has training or expertise in the design, development, implementation, and maintenance of an expert system n Knowledge User n The individual or group who uses and benefits from the expert system 27
![Application of ES 28 Application of ES 28](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-28.jpg)
Application of ES 28
![Benefits of Expert System 29 Benefits of Expert System 29](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-29.jpg)
Benefits of Expert System 29
![Limitations of an Expert System n Not widely used or tested n Difficult to Limitations of an Expert System n Not widely used or tested n Difficult to](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-30.jpg)
Limitations of an Expert System n Not widely used or tested n Difficult to use n Limited to relatively narrow problems n Possibility of error n Cannot refine its own knowledge n Difficult to maintain 30
![Expert System Shells 31 Expert System Shells 31](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-31.jpg)
Expert System Shells 31
![Expert System Shells n The shell is a piece of software which contains the Expert System Shells n The shell is a piece of software which contains the](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-32.jpg)
Expert System Shells n The shell is a piece of software which contains the user interface, n a format for declarative knowledge in the knowledge base, and n an inference engine. n n The knowledge engineer uses the shell to build a system for a particular problem domain. “A collection of software packages and tools used to develop expert systems” 32
![33 33](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-33.jpg)
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![Components of an expert system User Expert system shell User Inter face Explanation system Components of an expert system User Expert system shell User Inter face Explanation system](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-34.jpg)
Components of an expert system User Expert system shell User Inter face Explanation system Case specific data: Working storage Inference engine Knowledge base editor 34
![Expert System Shells n In the 1980 s expert system shells were introduced and Expert System Shells n In the 1980 s, expert system "shells" were introduced and](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-35.jpg)
Expert System Shells n In the 1980 s, expert system "shells" were introduced and supported the development of expert systems in a wide variety of application areas. n During the work , a large amount of LISP code was written for different modules: n n n . Knowledge base Inference engine Working memory Explanation facility End-user interface
![36 36](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-36.jpg)
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![MYCIN 37 MYCIN 37](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-37.jpg)
MYCIN 37
![n MYCIN was an early expert system that used artificial intelligence to identify bacteria n MYCIN was an early expert system that used artificial intelligence to identify bacteria](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-38.jpg)
n MYCIN was an early expert system that used artificial intelligence to identify bacteria causing severe infections. n recommend antibiotics, with the dosage adjusted for patient's body weight n The MYCIN system was also used for the diagnosis of blood clotting diseases. n MYCIN was developed over five or six years in the early 1970 s at Stanford University. n It was written in Lisp 38
![n MYCIN was a standalone system that required a user to enter all relevant n MYCIN was a standalone system that required a user to enter all relevant](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-39.jpg)
n MYCIN was a standalone system that required a user to enter all relevant information about a patient by typing in responses to questions MYCIN posed. n MYCIN operated using a fairly simple inference engine, and a knowledge base of ~600 rules. n It would query the physician running the program via a long series of simple yes/no or textual questions. 39
![Tasks and Domain n Disease DIAGNOSIS and Therapy SELECTION n Advice for nonexpert physicians Tasks and Domain n Disease DIAGNOSIS and Therapy SELECTION n Advice for non-expert physicians](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-40.jpg)
Tasks and Domain n Disease DIAGNOSIS and Therapy SELECTION n Advice for non-expert physicians with time considerations and incomplete evidence on: Bacterial infections of the blood n Expanded to meningitis and other ailments n Meet time constraints of the medical field n 40
![MYCIN Architecture 41 MYCIN Architecture 41](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-41.jpg)
MYCIN Architecture 41
![Consultation System n Performs Diagnosis and Therapy Selection n Control Structure reads Static DB Consultation System n Performs Diagnosis and Therapy Selection n Control Structure reads Static DB](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-42.jpg)
Consultation System n Performs Diagnosis and Therapy Selection n Control Structure reads Static DB (rules) and read/writes to Dynamic DB (patient, context) n Linked to Explanations n Terminal interface to Physician 42
![Consultation Control Structure n n Goaldirected Backwardchaining Depthfirst Tree Search Highlevel Algorithm 1 2 Consultation “Control Structure” n n Goal-directed Backward-chaining Depth-first Tree Search High-level Algorithm: 1. 2.](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-43.jpg)
Consultation “Control Structure” n n Goal-directed Backward-chaining Depth-first Tree Search High-level Algorithm: 1. 2. 3. 4. Determine if Patient has significant infection Determine likely identity of significant organisms Decide which drugs are potentially useful Select best drug or coverage of drugs 43
![Static Database n Rules n MetaRules n Templates n Rule Properties n Context Properties Static Database n Rules n Meta-Rules n Templates n Rule Properties n Context Properties](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-44.jpg)
Static Database n Rules n Meta-Rules n Templates n Rule Properties n Context Properties n Fed from Knowledge Acquisition System 44
![Dynamic Database n Patient Data n Laboratory Data n Context Tree n Built by Dynamic Database n Patient Data n Laboratory Data n Context Tree n Built by](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-45.jpg)
Dynamic Database n Patient Data n Laboratory Data n Context Tree n Built by Consultation System n Used by Explanation System 45
![Explanation System n Provides reasoning why a conclusion has been made or why a Explanation System n Provides reasoning why a conclusion has been made, or why a](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-46.jpg)
Explanation System n Provides reasoning why a conclusion has been made, or why a question is being asked n Q-A Module n Reasoning Status Checker 46
![DART n DART is a joint project of the Heuristic Programming Project and IBM DART n DART is a joint project of the Heuristic Programming Project and IBM](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-47.jpg)
DART n DART is a joint project of the Heuristic Programming Project and IBM that explores the application of artificial intelligence techniques to the diagnosis of computer faults. n The primary goal of the DART Project is to develop programs that capture the special design knowledge and diagnostic abilities of these experts and to make them available to field engineers. n The practical goal is the construction of an automated diagnostician capable of pinpointing the functional units responsible for observed malfunctions in arbitrary system configurations. 47
![n Dynamic Analysis and Replanning Tool n DART uses intelligent agents to aid decision n Dynamic Analysis and Replanning Tool n DART uses intelligent agents to aid decision](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-48.jpg)
n Dynamic Analysis and Replanning Tool n DART uses intelligent agents to aid decision support system n Give planners the ability to rapidly evaluate plans for logistical feasibility. n DART decreases the cost and time required to implement decisions. n The field engineer is familiar with the diagnostic equipment and software testing. n Access to information about the specific system hardware and software configuration of the installation. 48
![Xcon n The R 1 internally called XCON for e Xpert CONfigurer program was Xcon n The R 1 (internally called XCON, for e. Xpert CONfigurer) program was](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-49.jpg)
Xcon n The R 1 (internally called XCON, for e. Xpert CONfigurer) program was a production rule based system written in OPS 5 by John P. Mc. Dermott of CMU in 1978. n configuration of DEC VAX computer systems n ordering of DEC's VAX computer systems by automatically selecting the computer system components based on the customer's requirements. n XCON first went into use in 1980 in DEC's plant in Salem, New Hampshire. It eventually had about 2500 rules. n By 1986, it had processed 80, 000 orders, and achieved 9598% accuracy. n It was estimated to be saving DEC $25 M a year by reducing the need to give customers free components when technicians made errors, by speeding the assembly process, and by increasing customer satisfaction. 49
![n XCON interacted with the sales person asking critical questions before printing out a n XCON interacted with the sales person, asking critical questions before printing out a](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-50.jpg)
n XCON interacted with the sales person, asking critical questions before printing out a coherent and workable system specification/order slip. n XCON's success led DEC to rewrite XCON as XSELa version of XCON intended for use by DEC's salesforce to aid a customer in properly configuring their VAX. 50
![XCON Expert Configurer Stages of Expert System building n Identification Problems data goals company XCON: Expert Configurer Stages of Expert System building n Identification: Problems, data, goals, company,](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-51.jpg)
XCON: Expert Configurer Stages of Expert System building n Identification: Problems, data, goals, company, people… n Conceptualization: Characterize different kinds of concepts and relations n Formalization: Express character of search n Implementation: Build the system in executable form n Testing and Evaluation: Does it do what we wanted? n Maintenance Adapt to changing environment or requirements Expert Systems 14 51
![Phase 1 Identification n DEC Digital Equipment Corporation n n n n Large computer Phase 1: Identification n DEC, Digital Equipment Corporation n n n n Large computer](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-52.jpg)
Phase 1: Identification n DEC, Digital Equipment Corporation n n n n Large computer manufacturer, started 1957 Catalogue has 40. 000 different parts Buyer (with Sales Rep) sends order, typically 100 parts Delivery and assembly by DEC personnel Too often, part collection does not allow installation Too often, installed computer does not meet requirements Remedy: Completely assemble and test system in factory Automate configuration problem; attempts with procedural languages were unsuccessful XS approach started around 1980 Expert Systems 14 52
![Phase 2 Conceptualization Con what Expert Systems 14 53 Phase 2: Conceptualization Con. . what? Expert Systems 14 53](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-53.jpg)
Phase 2: Conceptualization Con. . what? Expert Systems 14 53
![Phase 3 Formalization n Configuration engineers could talk well to Knowledge Engineers of the Phase 3: Formalization n Configuration engineers could talk well to Knowledge Engineers of the](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-54.jpg)
Phase 3: Formalization n Configuration engineers could talk well to Knowledge Engineers of the CSDG n Could explain in what stage which component should be configured how n This was expressed in production rules IF c 1, c 2 c 3 THEN a 1, a 2, a 3 n Configuration stage was explicitly represented as data: current goal or context n Changing contexts moved configuration process through all stages Expert Systems 14 54
![Phase 4 Implementation into system R 1 n Language OPS 5 similar to CLIPS Phase 4: Implementation into system R 1 n Language: OPS 5 (similar to CLIPS)](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-55.jpg)
Phase 4: Implementation into system R 1 n Language: OPS 5 (similar to CLIPS) n Conflict Resolution: MEA (extends Lex / Specificity) n Means-Ends Analysis: order by recency of first condition IF c 1, c 2 THEN. . is now different from IF c 2, c 1 THEN n Contexts are treated as special by putting them first n End-task is unspecific, thus executed last n Use MEA + Spec to concentrate on subtasks: n IF g 1, x, y THEN assert barify // Signal necessity of subtask n IF barify, a THEN p, q // Two rules perform the task n IF barify, b THEN r, s // of barification per se n IF barify THEN retract barify // Termination when ready Expert Systems 14 55
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![Important questions PARTB 1 Expert system ES architecture of expert system components of Expert Important questions PART-B 1. Expert system (ES)? architecture of expert system? (components of Expert](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-59.jpg)
Important questions PART-B 1. Expert system (ES)? architecture of expert system? (components of Expert system)**** 2. Expert system shell? *** 3. MYCIN? ** 4. DART? 5. XCON? 6. Knowledge acquisition? 7. Inference Engine? Methods? (forward chaining, back ward chaining) 59
![PARTA 2 marks 1 Expert systemES 2 Application of ES 3 List advantage PART-A (2 marks) 1. Expert system(ES)? 2. Application of ES? 3. List advantage &](https://slidetodoc.com/presentation_image_h/8245e8021e22cfc956f23134c6523283/image-60.jpg)
PART-A (2 marks) 1. Expert system(ES)? 2. Application of ES? 3. List advantage & disadvantage of ES? 4. List out the Components of ES? 5. Define inference engine? 6. What is knowledge base(KB)? 7. What is the role of expert engineer? 8. What is meant by knowledge acquisition? 9. Expert system shell? 10. MYCIN? 11. DART? 12. XCON? 60
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