1 / 13

Knowledge Representation

Knowledge Representation. Knowledge Representation. Essential to artificial intelligence are methods of representing knowledge. A number of methods have been developed, including: Logic : propositional and predicate logic Semantic Networks Conceptual Dependencies Scripts Frames.

warren-wynn
Download Presentation

Knowledge Representation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Knowledge Representation

  2. Knowledge Representation • Essential to artificial intelligence are methods of representing knowledge. A number of methods have been developed, including: • Logic : propositional and predicate logic • Semantic Networks • Conceptual Dependencies • Scripts • Frames

  3. Representing Knowledge with Logic Logic systems began with Propositional Calculus in which declarative statements with a truth value of true or false are represented by P,Q,R, etc and combined with logic operators Or, And, Not, If. A sentence such as “Bill must take CSC 2020” is represented by letter P and is true or false. Propositional Calculus was extended to Predicate Calculus by adding Predicates (relations), variables, and quantifiers (For All and There Exists). A sentence such as “Every CS major must take CSC 2020” is represented by “(For All X)( CSMajor(X)  MustTake( CSC2020 ))” Given some facts expressed in either Propositional or Predicate Calculus, new facts or knowledge is inferred by inference rules such as modus ponens or resolution. If the computer can find a path from given facts to a new theorem, the path corresponds to a proof and finding such a path constitutes an example of artificial intelligence

  4. Propositional Logic • A declarative statement such as “Bill is a CS student” has a truth value of T or F and is denoted by P (a truth variable) • Propositions may be combined with logical operators and the composite statement has value as shown below. • P  Q is true if either P or Q are true and false if both are false • P  Q is true if both P and Q are true and false if either is false. • ¬ P is true if P is false and false if P is true • P  Q is true if P and Q have the same truth value and false if their values differ • P  Q is false if P is true and Q is false and true otherwise. • A tautology is always true. • P  Q  ¬ P  Q is a tautology. • P  (Q  R)  (P  Q)  (P  R) is a tautology.

  5. Semantic Networks • Models meaning of language: • Nodes correspond to word concepts • Arcs are labeled with a property nameor relationship and link a node (word concept) with another (value of property). • Quillian (1967) introduced semantic networks while others (Simmons -1973, Brachman-1979, Schank-1979) have extended the model.

  6. Semantic NetworksStandardization of Relationships • Standardization of relationships for representing knowledge expressed in language • focuses on case relations between verbs and nouns in sentence (Fillmore ’68, Simmons ’73) • Prepositions or articles indicate relationship between verb and noun : • Agent : entity performing the action • Object : entity acted upon • Instrument : entity used in performing the action • Etc.

  7. Conceptual DependenciesSet of Primitive Actions • Standardization of relations led to axiomatic approach to build semantic model for representing meaning of language Each Action is assumed to reduce to one or more of the primitive ACTs

  8. Building Complex Conceptual Dependencies

  9. Scripts • Scripts formalize stereotyped sequences of events. • A script for a restaurant differs from one for a “fast food” model. • The components of a script are • Entry conditions which must be true for script to be activated • Termination conditions which are true when script is terminated. • Props or object which support the script. The script for a restaurant would include table and cash register props. • Roles are the actions that individual participants must perform. The waiter takes orders, the customer eats and pays bill. • Scenes break the script into subsequences which • Are sequential in occurerence • Provide alternatives (if condition A then Scene1 elsce Scene2)

  10. Frames • Frames formalize stereotyped entities and actions. • Frames have labeled slots with slot contents an object or action and slot labels are the role played by the slot filler in relation to the central entity of action. • A frame is like a record that contains information relevant to stereotyped action or entity: • Frame Identification • Relationship to other frames (part-of, caused-by) • Slots • Label indicating relationship to central slot • Requirements for slot filler • Procedural information to construct or manipulate slot contents • Default Contents • Slot contents

  11. Frame Examples

  12. Conceptual GraphsA Network Language • A conceptual graph is a refinement of semantic networks. • A conceptual graph is bipartite with one class of nodes representing word concepts and the other class of nodes representing relations. • Arcs go from concept class nodes to relation class nodes and vise vesa.

  13. Conceptual Graph Examples flies bird dog color brown mother child parents father agent mary give object book recipient john

More Related