0
ARTIFICIAL  INTELLIGENCE (AI)  - <br />KNOWLEDGE  REPRESENTATION  SCHEMES<br />Ruchi Sharma<br />ruchisharma1701@gmail.com...
Contents<br /><ul><li>Quick Recall – AI concept
Knowledge Representation – Concept & Features
Knowledge Representation - Techniques/Schemes
Understanding Semantic Networks – Facts
Understanding Semantic Networks – Examples
Understanding Frames – Facts
Understanding Frames – Examples
Understanding Propositional Logic & FOPL – Facts
Understanding Propositional Logic & FOPL - Examples
Understanding Rule-based Systems - Facts
Understanding Rule-based Systems - Examples</li></ul>Ruchi Sharma               ruchisharma1701@gmail.com                 ...
Quick Recall – AI  Concepts<br />Artificial Intelligence deals with creating computer systems that can <br /><ul><li>simul...
learn new concepts and tasks
reason & draw conclusions
learn from the examples & past related experience</li></ul>A computer possessing artificial intelligence( an expert system...
Inference-control unit – which facilitates the appropriate &  contextual use of KB</li></ul>Ruchi Sharma               ruc...
Upcoming SlideShare
Loading in...5
×

Frames

3,062

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
3,062
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
113
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "Frames"

  1. 1. ARTIFICIAL INTELLIGENCE (AI) - <br />KNOWLEDGE REPRESENTATION SCHEMES<br />Ruchi Sharma<br />ruchisharma1701@gmail.com<br />Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  2. 2. Contents<br /><ul><li>Quick Recall – AI concept
  3. 3. Knowledge Representation – Concept & Features
  4. 4. Knowledge Representation - Techniques/Schemes
  5. 5. Understanding Semantic Networks – Facts
  6. 6. Understanding Semantic Networks – Examples
  7. 7. Understanding Frames – Facts
  8. 8. Understanding Frames – Examples
  9. 9. Understanding Propositional Logic & FOPL – Facts
  10. 10. Understanding Propositional Logic & FOPL - Examples
  11. 11. Understanding Rule-based Systems - Facts
  12. 12. Understanding Rule-based Systems - Examples</li></ul>Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  13. 13. Quick Recall – AI Concepts<br />Artificial Intelligence deals with creating computer systems that can <br /><ul><li>simulate human intelligent behaviour in a particular domain
  14. 14. learn new concepts and tasks
  15. 15. reason & draw conclusions
  16. 16. learn from the examples & past related experience</li></ul>A computer possessing artificial intelligence( an expert system) has two basic parts <br /><ul><li>Knowledge Base – containing the knowledge it uses
  17. 17. Inference-control unit – which facilitates the appropriate & contextual use of KB</li></ul>Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  18. 18. Knowledge Representation – Concept & Features <br /> Knowledge representation is a method used to code knowledge in the knowledge base of an expert system. <br /> An ideal knowledge representation scheme should <br /><ul><li>have inferencing capability
  19. 19. have a set of well defined syntax & semantics
  20. 20. allow the knowledge engineer to express knowledge in a language ( which can be inferred)
  21. 21. allow new knowledge to be inferred from the basic facts already stored in the KB</li></ul>Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  22. 22. Knowledge Representation – Techniques/Schemes<br /> Different knowledge representation schemes are used today among which the most common are <br /><ul><li>Semantic Networks
  23. 23. Frames
  24. 24. Propositional logic & FOPL
  25. 25. Rule-based system</li></ul>Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  26. 26. Understanding Semantic Networks - Facts<br /><ul><li>A semantic network is a directed graph with labelled nodes & arrows. Nodes are commonly used for objects & the arrows for relations.
  27. 27. The pictorial representation of objects, their attributes & relationships between them & other entities make them better than many other representation schemes. </li></ul>Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  28. 28. Understanding Semantic Networks – An example<br />Let us make a semantic net with the following piece of information<br />“Tweety is a yellow bird having wings to fly.”<br />Fact 1 : Tweety is a bird.<br />Fact 2 : Birds can fly.<br />Fact 3 : Tweety is yellow in color.<br />fly<br />CAN<br />tweety<br />yellow<br />bird<br />A-KIND-OF<br />COLOR<br />wings<br />HAS-PARTS<br />Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  29. 29. Understanding Frames – Facts <br /><ul><li>Frames are record-like structures that have slots & slot-values for an entity
  30. 30. Using frames, the knowledge about an object/event can be stored together in the KB as a unit
  31. 31. A slot in a frame
  32. 32. specify a characteristic of the entity which the frame represents
  33. 33. Contains information as attribute-value pairs, default values etc.</li></ul>Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  34. 34. Understanding Frames - Examples<br />An example frame corresponding to the semantic net eg quoted earlier <br /> (Tweety<br /> (SPECIES (VALUE bird))<br /> (COLOR (VALUE yellow))<br /> (ACTIVITY (VALUE fly)))<br />Employee Details <br /> ( Ruchi Sharma<br /> (PROFESSION (VALUE Tutor))<br /> (EMPID (VALUE 376074))<br /> (SUBJECT (VALUE Computers)))<br />Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  35. 35. Understanding Propositional Logic – Facts <br /><ul><li>Symbolic logic is a formalized system of logic which employs abstract symbols of various aspects of natural language.
  36. 36. Propositional logic is the simplest form of the symbolic logic, in which the knowledge is represented in the form of declarative statements called propositions.
  37. 37. Each proposition, denoted by a symbol, can assume either of the two values – true or false.</li></ul>Eg<br />P : It is raining.<br />Q : The visibility is low. <br />Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  38. 38. Understanding Propositional Logic – Facts (Contd.) <br /><ul><li>Propositions are also called formulas or well-formed-formulas(wffs)
  39. 39. Formulas can be atomic or compound
  40. 40. Atomic formulas – elementary propositional sentences
  41. 41. Compound formulas – formed from the atomic formulas using logical connectives ( ^, V, !, ~, )</li></ul>eg<br /> R : It is raining and the visibility is low. <br />Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  42. 42. Understanding Propositional Logic - Examples<br />If given the statements P, Q and S as :<br /> P : It is raining.<br /> Q : The visibility is low. <br /> S : I can’t drive.<br /> Then, the statement “It is raining and the visibility is low, so I can’t drive.” will be formalized as <br /> P ^ Q S <br />If given the statements P & Q as :<br /> P : He needs a doctor.<br /> Q : He is unwell.<br /> we can conclude <br /> Q P<br />Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  43. 43. Understanding First order predicate logic (FOPL)<br /><ul><li>FOPL was developed to extend the expressiveness of propositional logic.
  44. 44. It works by breaking a proposition into various parts & representing them as symbols.
  45. 45. The symbolic structure includes
  46. 46. individual symbols - some constants as names
  47. 47. variable symbols – as x, y, a, b etc
  48. 48. function symbols – as ‘product’
  49. 49. predicate symbols – as P, Q etc </li></ul>Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  50. 50. Understanding FOPL - Example<br />Given statements <br /> P: Every bird can fly. <br /> Q : Tweety is a bird.<br /> R : Tweety can fly.<br />Using FOPL, lets define the following <br />B(x) for x is a bird.<br />F(x) for x can fly.<br />P : V(x) ((B(x) F(X))<br />Q : B(TWEETY))<br />R : v(x)(B(x) F(x)) ^ B(TWEETY) F(TWEETY)<br />Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  51. 51. Understanding Rule-based System – Facts <br /><ul><li>A Rule-based system represents knowledge in the form of a set of rules .
  52. 52. Each rule represents a small chunk of knowledge relating to the given domain.
  53. 53. A number of related rules along with some known facts collectively may correspond to a chain of inferences.
  54. 54. An interpreter(inference engine) uses the facts & rules to derive conclusions about the current context & situation as presented by the user input. </li></ul>Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  55. 55. Understanding Rule-based System – Example <br />Suppose a rule-based system has the following statements <br />R1 : If A is an animal and A lays no eggs, then A is a mammal.<br />F1 : Lucida is an animal.<br />F2 : Lucida lays no eggs.<br />The inference engine will update the rule base after interpreting the above set as : <br />R1 : If A is an animal and A lays no eggs, then A is a mammal.<br />F1 : Lucida is an animal.<br />F2 : Lucida lays no eggs.<br />F3 : Lucida is a mammal. <br />Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  56. 56. Thank You <br />Ruchi Sharma ruchisharma1701@gmail.com http://www.wiziq.com/tutor-profile/376074-Ruchi<br />
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×