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Expert System Seyed Hashem Davarpanah University of Science and Culture.

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Presentation on theme: "Expert System Seyed Hashem Davarpanah University of Science and Culture."— Presentation transcript:

1 Expert System Seyed Hashem Davarpanah University of Science and Culture

2 Overview Knowledge Representation for XPS Associative Nets and Frame Systems Associative Nets and Frame Systems (Jackson, Chapter 6) (Jackson, Chapter 6) Object-oriented Programming Object-oriented Programming (Jackson, Chapter 7) (Jackson, Chapter 7)

3 Knowledge Representation 1 Representation of declarative knowledge(what, objects, structure) procedural knowledge(how, actions, performance) Representation Formalisms for declarative knowledge Frames, Semantic Nets, Inheritance Hierarchies, Schemata,... procedural knowledge Algorithms, Procedures, Plans, Rules,...

4 Knowledge Representation 2 Representation of declarative knowledge: descriptions of objects, concepts: relations between theme.g. has-parts, father featurese.g. number-of-legs, age mechanisms to work with this knowledge: store, retrieve information (accessing KB) e.g. TELL (KB1, IS-A (square,polygon)) KB1 is a knowledge base; in KB1 a square is a sub-concept or sub-class of the polygon-concept/class; TELL asserts this. reason about objects (inferencing) if square(X) then polygon(X) if square(X) then number-of-sides (X)=4 If something is a square, it is also a polygon. A square thing has 4 sides.

5 Graphs as Representations I: Nodes, Links, and what they represent Semantic Networks (Quilian 1968) nodes represent concepts, objects, events e.g. person, John, restaurant links (arcs) represent any kind of relation or association between concepts, objects, events e.g. IS-A, owns, lives-at, name This allows the representation of all kinds of semantic expressions BUT the semantics is not clearly defined (Woods 1975) -> epistemological confusion.

6 Causal, Temporal, and Inheritance Networks Causal Networks (Jackson, Fig. 6.2, see next slide) nodes represent concepts, objects, events links represent causal relationship between these concepts, objects, events Temporal Networks nodes represent events links represent temporal relationship between events, like before, after,... Inheritance Networks (Terminologies, Taxonomies) nodes represent concepts links represent class-/subclass-relationship IS-A: superclass – subclass or super-concept / sub-concept

7 Causal Network – Example (Jackson, Fig. 6.2)

8 Classification Hierarchy (Jackson, Fig. 6.3)

9 Graphs as Representations II: Nodes, Links, and what they represent Semantics of Knowledge Representation Languages: formal semanticse.g. Predicate Logic Interpretation, derive meaning of complex expressions based on meaning of atomic expressions plus construction mechanism use / reasoninge.g. Spreading Activation positive or negative association between concepts; see Neural Networks

10 Inheritance Networks Inheritance Networks (Terminologies, Taxonomies) nodes represent concepts (events, objects, actions,...) links represent super-concept / sub-concept-relationships IS-A: specialization / subsumption of concepts concept-instance-relationships instance-of relationships between concepts role (slot) attributes/features/properties of concept constraints attached to roles, e.g. number of fillers

11 Terminological Network – Example Example: Concepts: bird, robin, flying-animal, Speedy Feature: color IS-A (robin, bird), IS-A (bird, flying-animal) Superclass instance-of (Speedy, robin)Instance color (robin)=greyFeature Task: Express that a typical elephant has legs, usually 4 of them, has a certain color, and there is a specific elephant named Clyde. elephant, color, legs, has (usually) 4 legs,Clyde

12 Terminological Network - Solution Solution: Concepts: elephant, legs, Clyde (instance) or Clyde (individual concept), (color and grey) Roles: has-legs, (has-color) Feature: color Specific Representation of Clyde: has-legs (elephant, legs, 4) has-color (elephant, grey) or color (elephant) = grey instance-of (Clyde, elephant) specific object Clyde IS-A (Clyde, elephant) individual concept Clyde

13 Frames concepts as record-like structures slots – relationships to other concepts, attributes fillers – values for slots (other concept or value) Schema-Theory / Prototypes some objects are more typical for a certain class of objects precise definition for concepts sometimes not possible, then reference to prototypes Frames, Schemas, Prototypes

14 Typical bird is robin – take all robin-features as description for class bird. This forms a prototype. Take typical chair as prototype. Other chairs are more or less similar to this prototypical chair. The class of all chairs is fuzzy since there are no precise or exact boundaries for the class 'chair', i.e. to decide when something is a chair or not. Frames, Schemas, Prototypes

15 Frames, Defaults, and Demons Frames – represent concepts as record-like structures slots – relationships to other concepts, attributes fillers – values for those slots attached procedures – to determine slot-fillers Defaults – represent standard values (fillers) for some attributes (slots) of a concept (frame) Demons – are activated by a certain action or pattern if-added demon - activated when value is added or updated, e.g. re-calculate area-value if side-info changes if-needed demon - activated when value is accessed, e.g. calculate area-value based on default assumptions if this slot-filler is required

16 Defaults Defaults represent standard-values for some attributes of a concept, e.g. the standard number of legs of an elephant is 4. (Inherited) defaults may be overwritten at lower-level concepts, or for individual concepts. Clyde - the famous 3-legged AI-elephant. Problem: If roles, attributes etc. in a concept description can be changed or cancelled, what is the definition of a concept. How can we classify? And reason?

17 Multiple Inheritance and Views Multiple inheritance Sub-Concept inherits descriptions from several superconcepts. Possibly conflicting information ( = ambiguity) skeptical reasoners: dont know (no conclusion) credulous reasoners: whatever (several conclusions) Views Description of concept from different viewpoints. Inheritance of multiple, complementing descriptions e.g. view computer as machine or as equipment

18 Multiple Inheritance - Views (Jackson, Fig. 6.7)

19 Multiple Inheritance - Ambiguity (Jackson, Fig. 6.8)

20 Object-Oriented Programming Objects – represent concepts similar to frames structured declarative representation (like record) procedural methods define the (external) behavior of object Objects communicate via sending messages to other objects to invoke their methods.

21 Object-Oriented Programming – Example Class RadioClass Robotslots power:{on,off}... volume:{1,...,10}methods v-control(V) - shut-down(...) – set volume = V send (radio-1, switch(off)) switch(O) - set power on/off for O=on/off radio-1 of-class Radiorobot-1 of-class Robot

22 Object-Oriented Programming (OOP) - Languages - SIMULA67, SmallTalk... KRL – Knowledge Representation Language LOOPS – Lisp Object-Oriented Programming System Flavors CLOS – Common Lisp Object System COOL – CLIPS Object-Oriented Language... C ++, Java,...

23 OOP –Terminology and Concepts objects:data structures + procedures Objects are often called classes. Procedures are often called methods. Encapsulation: object-information can only be accessed by specifying the object and using the methods defined for this object ( message passing). Message Passing: objects can send messages to other objects by addressing the object and one of its methods. Distinction: private and public variables / procedures Computation in OOP involves mainly communication between objects, and little or no global control.

24 OOP – Inheritance Objects/classes arranged in inheritance hierarchy Classes are also called generic objects / concepts Their methods are called generic methods / procedures Specific objects are instances of classes. Inheritance of data structures (slots, fillers) and procedures (methods). class ship instance-of ship Titanic class ship class motorship IS-A ship

25 OOP –Inheritance of Data Structures Inheritance of data structures define data structures (slots) for super-class define defaults and common values (for slots) for super-class inherit to sub-classes and instances Class motorship, as well as instance Titanic inherit all slot- specifications, defaults, etc. from ship. class ship x-velocity INTEGER y-velocity INTEGER... class motorship IS-A ship instance-of ship Titanic

26 OOP – Inheritance of Methods Inheritance of procedures (methods) define generic method for super-class inherit to sub-classes (and instances) self is object itself; self:x-velocity refers to slot x-velocity of self. Method calc-speed known for all ships; uses concrete values for x-velocity, y-velocity for instance of ship (e.g. Titanic) in actual calculation. class ship method calc-speed.... (self:x-velocity, self:y-velocity)... instance-of ship Titanic send Titanic calc-speed

27 Multiple Inheritance and Method Combination Inheritance of methods / procedures in multiple inheritance hierarchies (heterarchies). Problem of method combination. Use before- and after-methods ( e.g. Flavors) take main method (inherited from super-class); add special before and after methods (from class or super- classes) which are executed before / after the main method. before-method – preparation for main method after-method – clean-up and adjustments around-methods or wrappers and whoppers – additional surrounding code

28 Method Combination and Multiple Inheritance Window with Border Window with Label Window with Border and Label Window Inherit main methods (e.g. refresh) from Window, and add special methods as before- and after-methods from subclasses.

29 Meta-Classes Meta-Classes are used to describe classes, e.g. create- and delete-functions, access-functions etc. The members of Meta-Classes are classes. Meta-Classes are classes themselves.

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