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Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
Developing Knowledge-Based Systems
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Developing Knowledge-Based Systems

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Knowledge-Based Systems, Akerkar and Priti Sajja, Chapter 3

Knowledge-Based Systems, Akerkar and Priti Sajja, Chapter 3

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  • 1. Developing Knowledge-Based Systems (Knowledge-Based Systems; R Akerkar, P Sajja) Prepared By: Ashique Rasool
  • 2. Nature Of Knowledge-Based Systems  Quite different from other computer based information systems  Deals with knowledge and works at an unstructured level  Can justify there decision and have the ability to learn Prepared By: Ashique Rasool
  • 3. Difficulties in KBS Development  High cost and effort  Dealing with experts Experts are often rare so it is difficult to meet them and take knowledge for the system  The nature of knowledge As the knowledge is specific to the domain, it can not be shared without the presence of expert even the knowledge is available  The level of risk It is some how risky because the development cost is very high and the cost goes higher and higher in maintaining these systems Prepared By: Ashique Rasool
  • 4. KBS Development Model Prepared By: Ashique Rasool
  • 5. KBS Development Model  This Development model is based on the system life cycle. The major stages of this model are:  Elicitation of feasible requirements  Strategy Selection and Overall Design of KBS  Ontology Selection and knowledge representation  System Development and Implementation  Testing, Implementation and Training  Knowledge Acquisition  In the figure development round one just gives a prototype and round two gives complete system development. Prepared By: Ashique Rasool
  • 6. Knowledge Aquisition  Activities in Knowledge Acquisition Prepared By: Ashique Rasool
  • 7. Knowledge Acquisition…  Knowledge Eliciation The knowledge acquisition process in which the domain expert is the only source of knowledge  Steps Of Knowledge Acquisition  Step I : Find suitable expert and knowledge engineer  Step II : Proper homework and planning  Step III : Interpreting and understanding the knowledge provided by the experts  Step IV : Representing the knowledge provided by the experts Prepared By: Ashique Rasool
  • 8. Techniques for Knowledge Acquisition  Literature review  Interview and protocol analysis Protocol analysis is a kind of interview in which the domain expert is asked not only to solve the problem but also to think aloud while doing so.  Surveys and Questionnaires Useful in gather quantitative factual knowledge (explicit knowledge)  Observations Observing experts in a live environment gives a better picture of the solution strategy  Diagram-Based Techniques Process-flow diagram, conceptual maps, event and state charts  Generating Prototypes  Concept sorting Prepared By: Ashique Rasool
  • 9. Concept Sorting It is a psychological technique that is useful in tapping an organization's knowledge.  Steps of Concept Sorting 1. 2. 3. 4. 5. Consider a textbook or ask domain expert for the basic concepts and standards of the domain and codify each major concept in separate cards Arrange these cards into various groups according to their use Ask question to the domain expert regarding the order and placement of the concept cards Steps 2 & 3 are repeated until the expert is finished answering questions or sufficient knowledge is acquired If the expert runs out of knowledge then the enginer takes any three cards and ask the relationship. Prepared By: Ashique Rasool
  • 10. Sharing Knowledge Experts can share meaningful outcomes of their learning process to enrich and generalize their knowledge. Following are the methods for knowledge sharing:  Problem Solving  Talking and story telling  Supervisory style Prepared By: Ashique Rasool
  • 11. Issues with Knowledge Acquisition  Most knowledge rests with experts so can not be extracted directly  Continuously changing nature of knowledge  Difficult to prepare the experts for knowledge acquisition process  Sometimes the knowledge are subcontious  An expert is not always correct  No single expert know everything  Opinions among multiple experts may differ significantly Prepared By: Ashique Rasool
  • 12. Updating knowledge The knowledge base in a KBS undergoes continuous updating. Following are the three means by which updates can be made  Self-Updating: The system learns from the cases it handles(self learning)  Manual updates by knowledge engineer  Manual Updates by experts Prepared By: Ashique Rasool
  • 13. Knowledge Representation Knowledge components should be represented in such a way that the operations storage, retrieval, inference and reasoning are facilitated without disturbing the required characteristics of knowledge Knowledge Structure: Prepared By: Ashique Rasool
  • 14. Characteristics of efficient knowledge representation facility  It should be able to represent the given knowledge to a sufficient depth  Should preserve the fundamental characteristics of knowledge(complete, accessible, consistent etc).  Should be able to infer new knowledge  Should be able to provide reasoning and explanation  Should be able to store updates and support incremental development  Should be independent enough to be reused Prepared By: Ashique Rasool
  • 15. Types Of Knowledge Knowledge representation is broadly classified in two categories  Factual Knowledge Representation  Constants  Variables  Functions  Predicates  Well-formed Formulas  First Order Logic  Procedural Knowledge Representation Prepared By: Ashique Rasool
  • 16. Factual Knowledge Representation Factual knowledge are known as formal knowledge and can be represented using first order logic supporting constants, variables functions and predicates  Constants: Those symbols that don’t change, represent fixed knowledge  Variables: Takes different values within a fixed domain  Functions: Set of instructions that carry out process and return a predefined value  Predicates: Special functions that return only Boolean value  Well-Formed Formulas: String of symbols that is generated by a formal language Prepared By: Ashique Rasool
  • 17. Factual Knowledge Representation  First Order Logic: Generated by combining predicate logic and propositional logic. Examples     Constants: Mohammad, Salem etc. Variables: Man Functions: Elder(Mohammad, Salem) returns value Predicates: Mortal(Salem) returns Boolean value  Well-Formed Formulas: If you don’t exercise you will gain weight. Represented as ∀x[{Human(x) ^ ~ ∃Exercise(x)} => Gain_Weight(x)] Prepared By: Ashique Rasool
  • 18. Representing Procedural Knowledge Procedural knowledge represents how to reach a solution in a given situation. Examples of procedural knowledge are:  Production Rules: Knowledge is represented as a sequence of condition and the appropriate actions If<condition>, then <action> Rules are simple and easy to understand, implement and modify. Large number of rules are required to solve simple problems. This large volume creates problem in documenting and encoding into the knowledgebase. Deduction process works as follows:  Knowledge in the form of facts and rules  New facts are added  Combining the new facts with existing facts and rule Prepared By: Ashique Rasool
  • 19. Representing Procedural Knowledge  Semantic Networks: Graphical description of knowledge composed of nodes (objects or concepts) and links that show hierarchical relationships. The links carries semantic information such as is-a, type-of, part-of etc. Example: Prepared By: Ashique Rasool
  • 20. Representing Procedural Knowledge  Frames: Frames are the description of conceptual and     default knowledge about a given entity. A frame organizes knowledge according to cause-andeffect relationships The slots of a frame contains items like rules, facts, videos, references etc. It also contains pointers to other frames or procedures. A slot is further divided into facets. A facet may be any of the following Example:  Explicit or default values  A range of values  An if-added type of procedural attachment. Name: Broad Category: Sub Category: Cost: Capacity: Speed: Prepared By: Ashique Rasool Power bike Land vehicle Gearless $350 Two persons 160 km/hour
  • 21. Representing Procedural Knowledge A frame based interpreter must be capable of the following:  Check for a slot value that is correct and within specified     range Dissemination of definition values Inheritance of default values Computation of the value of a slot as required Checking whether the correct values has been computed Prepared By: Ashique Rasool
  • 22. Representing Procedural Knowledge  Scripts: Script is a knowledge representation structure for a specific situation.  It contains slots such as objects, their roles, entry and exit conditions and different scenes describing a process in detail. Example: Prepared By: Ashique Rasool
  • 23. Representing Procedural Knowledge  Hybrid Structures: It encorporates more than one representation scheme. Example: Prepared By: Ashique Rasool
  • 24. KBS Tools  PROLOG  LISP (List Processing)  AIML (Artificial Intelligence Modeling Language)  MATLAB  JavaNNS (Java Neural Networks Simulator)  CLIPS (C Language Integrated Production System) Prepared By: Ashique Rasool

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