Intelligent Systems Knowledge Representa6on

Transcription

1 Intelligent Systems Knowledge Representa6on SCJ3553 Ar6ficial Intelligence Faculty of Computer Science and Informa6on Systems Universi6 Teknologi Malaysia

2 Outline Introduc6on Seman6c Network Frame Conceptual Graph Agent- based

3 Introduc6on Implementa6on of intelligent systems Representa6on scheme (like data structures, explicit structure for knowledge representa6on) Representa6on medium (like programming languages, i.e. PROLOG, LISP,C++,Java, JSP, ASP.NET,PHP.)

4 Seman6c Network Graph- explicitly represen6ng rela6ons using arcs and nodes; formalizing knowledge Seman6c network represents knowledge as a graph with nodes corresponding to facts or concepts and arcs correspond to rela6ons between concepts Eg. Figure 1(canary- bird) and Figure 2(snow- ice)

5 Figure 1: Seman6c network developed by Collins and Quillian in their research on human informa6on storage and response 6mes (Harmon and King 1985). Seman6c Network Knowledge organiza6on Inheritance systems allow:- - storing knowledge at highest level of abstrac6on - reduce size of knowledge base - help prevents update inconsistencies

6 Seman6c Network Seman6c network can be used to:- - answer ques6ons about snow,ice and snowmen(with appropriate inference rules) - References are made by following links to concept - implement inheritance i.e. frosty inherits all proper6es of snowman Figure 2: Network representa6on of proper6es of snow and ice

7 A program defined English word Each defini6on leads to other defini6on in unstructured or circular fashion Looking up a word, the network is traversed un6l the word is understood Knowledge- based is organized into planes, each explains single word i.e. 3 planes capture defini6ons of plant - living organism,work place or pucng seed in ground Seman6c Network Figure 3: Three planes represen6ng three defini6ons of the word plant (Quillian 1967)

8 Seman6c Network Knowledge- based is used to find rela6onships between pairs of english word Given two words, it would search graphs outward from each word in breadth- first fashion, searching for intersec(on- node Figure 4: Intersec6on path between cry and comfort (Quillian 1967).

9 Seman6c Network

10 Frame Another representa6onal scheme Implicit connec6on of informa6on Sta6c data structure to represent well- understood stereotyped situa6ons Organize our knowledge of the world We adjust to new situa6on by calling up informa6on structure by past experience We then revise details of past experiences to represent differences in new situa6on

11 Frame

12 Frame Hotel room and its components are described by number of individual frames Each frame may be seen as data structure Contains info relevant to stereotyped en66es Frame systems support class inheritance

13 Frame This frame system represents four of faces of cube Broken line indicates face out of view from that perspec6ve Links between frames indicate rela6ons between views represented by frames Each slot in one frame could be a pointer to another en6re frame Since given informa6on can fill many different slot (face E), No redundancy in informa6on stored Frames allow complex objects to be represented as a single frame rather than large network structure

14 Frame

15 Conceptual Graph Example of a network representa6on language A finite, connected graph Nodes are either: concepts or conceptual rela6ons No labeled arcs Conceptual rela6on nodes=rela6ons between concepts i.e. (dog and brown ~ concept nodes) i.e. (color ~ conceptual rela6on)

16 Conceptual Graph Each conceptual graph represents a single proposi6on A typical knowledge- base contains several graphs Graph must be finite i.e. a dog has a color of brown Conceptual graphs are used to model seman6cs of natural language

17 Conceptual Graph type individual The graph uses conceptual rela6ons to represent cases of the verb to give Conceptual graphs used to model seman6cs of natural language Every concept is a unique individual of a par6cular type, separated by :

18 Conceptual Graph Conceptual graph indica6ng that the dog named emma is brown. Conceptual graph indica6ng that a par6cular (but unnamed) dog is brown. Conceptual graph indica6ng that a dog named emma is brown. Graph 1= 3

19 Conceptual Graph Conceptual graph of a person with three names. # marker # Marker is unique and different from names Individual has many names but one marker different individuals may have same name, but not same marker

20 Conceptual Graph Conceptual graph of the sentence The dog scratches its ear with its paw. To summarize: each concept node indicates individual of specified type This individual is the referent of the concept Individual concept ~referent uses individual marker Generic concept ~ referent uses generic marker

21 Generaliza(on and Specializa(on: Examples of restrict, join, and simplify opera(ons Conceptual Graph Conceptual graph includes opera6ons crea6ng new graphs from exis6ng ones: - specializing or generalizing exis6ng graph to represent seman6cs of natural language:- 1)Copy- exact copy of graph 2)Restrict- replace concept nodes with specialized note: generic marker replace individual marker Replace type with subtypes; animal - > dog

22 3)Join- combine 2 graphs into single one If concept node c 1 and c 2 iden6cal, delete c 2 ; c 1 replace c 2 Specializa6on- produce less general graph 4)Simplify- if graph has duplicate rela6ons, delete one together with its arcs Occur amer join opera6on Ж restrict ~ match two concepts Ж join and restrict ~ allow implementa6on of inheritance Conceptual Graph

23 Conceptual Graph Conceptual graph of the proposi6on There are no pink dogs. u To represent nega6on or disjunc6on- variable quan6fica6on (universal quan6fier (for all) and existen6al quan6fier (there exist) u neg takes argument as proposi6on concept and assert that concept as false u In conceptual graph, generic concepts are assumed to be existen6ally quan6fied u Eg. Transla6ons X Y (dog(x) color(x,y) brown(y) ==> existen6al quan6fier X Y ( (dog(x) color(x,y) pink(y))) ==> universal quan6fier Eg. Transla6on X 1 (dog(emma) color(emma, X 1 ) brown(x 1 )) Ø There is straight mapping from conceptual graph into predicate calculus nota6on (Sowa,1984) Ø Advantage of conceptual graph support some special- purpose inferencing mechanisms such as join and restrict, not normally part of predicate calculus

24 Conceptual Graph On a piece of paper, answer the ques6ons below and submit Translate the two conceptual graphs into English Translate the two conceptual graphs into predicate calculus

25 Conceptual Graph

26 Agent Defini6on: mul6- agent :- a comp program with problem solvers situated in interac6ve environments, capable of flexible, autonomous and socially organized ac6ons. 4 criteria of intelligent agent: Situated, autonomous, flexible and social

27 Agent

28 Agent Situatedness Means agent receives input from environment in which it is ac6ve and can also effect changes within that environment i.e. situa6ons like internet, game playing, robo6c situa6on i.e. ROBOCUP compe66on agent interact with ball and opponent

29 Agent Autonomous Can interact with its environment without direct interven6on of other agents Control over its own ac6on Can also learn from experience to improve performance i.e. ROBOCUP agent pass the ball to a teammate or kick on goal depending on its situa6on

30 Agent Flexible Intelligently responsive receive s6muli from its environment and responds to them in an appropriate and 6mely fashion proac6ve not simply responsive but able to be opportunis6c, goal directed and have appropriate alterna6ves for various situa6ons i.e. soccer agent change its dribble depending on the challenge parern of opponent

31 Agent Social Interact with other somware or human- agent towards the goal social dimension address difficult situa6on i.e. ROBOCUP to score a goal, how one agent support another agent s goal?

32 Agent Mul6- agent problem characteris6cs: 1. Each agent has incomplete informa6on and capabili6es to solve en6re problem 2. No global system controller for en6re problem solving 3. Knowledge and input data for the problem is decentralized 4. The reasoning process are asynchronous

33 Agent Agent VS Object AGENT request ac6on to be performed designed to have flexible have own thread of controls Object invoke methods on one another defined as computa6onal systems with encapsulated state have methods associated with state communicate by message passing rarely exhibit control over own behavior

34 Agent