CSC 480 Artificial Intelligence Dr Franz J Kurfess

CSC 480: Artificial Intelligence Dr. Franz J. Kurfess Computer Science Department Cal Poly © 2000 -2008 Franz Kurfess Knowledge-Based Systems 1

Course Overview u Introduction u Intelligent Agents u Search u u problem solving through search informed search u Games u games as search problems u Knowledge u u and Reasoning reasoning agents propositional logic predicate logic knowledge-based systems u Learning u u learning from observation neural networks u Conclusions © 2000 -2008 Franz Kurfess Knowledge-Based Systems 2

Chapter Overview Knowledge-Based Systems u Motivation u Example u Objectives u u Evaluation Criteria u Terminology u u u Data Knowledge Information u Knowledge u Domains Electronic Circuits u Important Concepts and Terms u Chapter Summary Engineering Methodology u Ontologies © 2000 -2008 Franz Kurfess Knowledge-Based Systems 3

Logistics u Term Project u Deadline Part 2 u Assignment of Evaluation Teams u Lab and Homework Assignments u Draft u Final of Assignment 3 Exam u Take-Home u Extra u “AI Credit Nuggets” © 2000 -2008 Franz Kurfess Knowledge-Based Systems 4

Motivation u without a good knowledge representation scheme, reasoning becomes very difficult u a balance must be found between expressiveness of the representation language, understandability, and efficiency of the inference mechanism u it is good to have a general-purpose methodology that can be refined for specific domains or problems u for performance reasons, sometimes specific schemes, methods, or inference mechanisms are used © 2000 -2008 Franz Kurfess Knowledge-Based Systems 7

Objectives u distinguish between different phases in the knowledge representation process u learn to apply the concepts of predicate logic for the representation of knowledge u understand the difficulties of finding a good knowledge representation scheme u general-purpose u domain- or task-specific u distinguish the tasks and activities of a knowledge engineer from those of a system designer of programmer © 2000 -2008 Franz Kurfess Knowledge-Based Systems 8

Terminology u Data u fixed relations between individual items u often arranged as vectors or arrays u interpretation is usually provided for the collection of data as a whole u Knowledge u separate, possibly dynamic relations between individual items u interpretation must be done for individual items u Information u generic term, used in a very general sense u precisely defined for information theory © 2000 -2008 Franz Kurfess Knowledge-Based Systems 10

Knowledge Engineering u often performed by a knowledge engineer u intermediary between users, domain experts and programmers u must know enough about the domain objects v relationships v u must v be comfortable in the representation language encoding of objects and relationships u must understand the implementation of the inference mechanism performance issues v explanation of results v © 2000 -2008 Franz Kurfess Knowledge-Based Systems 11

Knowledge Engineering Methodology u domain u objects, facts u vocabulary u u predicates, functions, constants ideally results in an ontology u background u u general knowledge about the domain specify the axioms about the terms in the ontology u specific u knowledge problem description of the instances of concepts and objects that determine the problem to be investigated u queries u requests answers from the knowledge base/inference mechanism © 2000 -2008 Franz Kurfess Knowledge-Based Systems 12

Example: Electronic Circuits u domain u u digital circuits, wires, gates, signals, input and output terminals types of gates: AND, OR, XOR, NOT u vocabulary u u constants for naming gates: X 1, X 2, … functions for gate types v u functions for terminals v v u Out(1, X 1) for the only output of X 1 In(n, X 1) for the input n of X 1 predicates for connectivity v u Type(X 1) = XOR Connected(Out(1, X 1), In(2, X 2) ) two objects and a function for the signal values v v On, Off Signal(Out(1, X 1)) © 2000 -2008 Franz Kurfess Knowledge-Based Systems 13

Example: Electronic Circuits (cont. ) u background knowledge u two connected terminals have the same signal v u t 1, t 2 Connected(t 1, t 2) Connected(t 2, t 1) g Type(g) = OR Signal(Out(1, g)) = On n Signal(In(n, g)) = On g Type(g) = AND Signal(Out(1, g)) = Off n Signal(In(n, g)) = Off definition of XOR (sometimes denoted by ) v u Signal(t) = On Signal(t) = Off On Off definition of AND v u t definition of OR v u Signal(t 1) = Signal(t 2) connections go both ways (commutative) v u Connected(t 1, t 2) signals must be either on of off, but not both v u t 1, t 2 g Type(g) = XOR Signal(Out(1, g)) = On Signal(In(1, g)) Signal(In(2, g)) definition of NOT v g Type(g) © 2000 -2008 Franz Kurfess = NOT Signal(Out(1, g)) Signal(In(1, g)) Knowledge-Based Systems 14

Example: Electronic Circuits (cont. ) u C 1 1 2 X 1 3 1 X 2 A 1 © 2000 -2008 Franz Kurfess O 1 2 specific problem: circuit C 1 to be modeled Type(X 1) = XOR; Type(X 2) = XOR; Type(A 1) = AND; Type(A 2) = AND; Type(O 1) = OR; Connected(In(1, C 1), In(1, X 1)); Connected(In(1, C 1), In(1, A 1)); Connected(In(2, C 1), In(2, X 1)); Connected(In(2, C 1), In(2, A 1)); Connected(In(3, C 1), In(2, X 2)); Connected(In(3, C 1), In(1, A 2)); Connected(Out(1, X 1), In(1, X 2)); Connected(Out(1, X 1), In(2, A 2)); Connected(Out(1, A 2), In(1, O 1)); Connected(Out(1, A 1), In(2, O 1)); Connected(Out(1, X 2), Out(1, C 1)); Connected(Out(1, O 1), Out(2, C 1)); Knowledge-Based Systems 15

Example: Electronic Circuits (cont. ) u queries u When is the first output of C 1 (sum bit) off and the second one (carry bit) on? i 1, i 2 , i 3 Signal(In(1, C 1) = i 1 Signal(In(2, C 1) = i 2 Signal(In(3, C 1) = i 3 Signal(Out(1, C 1) = Off Signal(Out(2, C 1) = On u Give all combinations of the values for the terminals of C 1 i 1 , i 2 , i 3 , o 1 , o 2 © 2000 -2008 Franz Kurfess Signal(In(1, C 1) = i 1 Signal(In(2, C 1) = i 2 Signal(In(3, C 1) = i 3 Signal(Out(1, C 1) = o 1 Signal(Out(2, C 1) = o 2 Knowledge-Based Systems 16

Ontologies u define the terminology about the objects and their relationships in a systematic way u closely related to taxonomies, classifications ontologies don’t have to be hierarchical v emphasis on the terms to describe objects, relationships, not on the properties of objects or specific relationships between objects v u general ontology u convergence of a multitude of special-purpose ontologies u should be applicable to any special-purpose domain © 2000 -2008 Franz Kurfess Knowledge-Based Systems 17

Example General Ontology Anything Abstract. Objects Sets Numbers Categories Physical. Objects Representational. Objects Sentences Measurements Things Animals Events Stuff Agents Processes Solid Liquid Places Gas Intervals Moments Humans there is no generally agreed upon ontology © 2000 -2008 Franz Kurfess Knowledge-Based Systems 18

Issues for Ontologies u categories u measures u composite objects u time, space, and change u events and processes u physical objects u substances u mental objects and beliefs © 2000 -2008 Franz Kurfess Knowledge-Based Systems 19

Categories u categories are very important for reasoning u almost always organized as taxonomic hierarchies u general statements about related objects can be made easily u specific properties of instances can either be inferred, or specified explicitly u inheritance v similar to OO programming © 2000 -2008 Franz Kurfess Knowledge-Based Systems 20

Measures u values for properties of objects u frequently expressed quantitatively u number and unit function u allows the use of ordering function on objects © 2000 -2008 Franz Kurfess Knowledge-Based Systems 21

Composite Objects u objects frequently can be decomposed into parts, or composed into larger objects u often expressed through Part. Of relation u allows grouping of objects into hierarchies u frequently the internal structure of objects is of importance u allows general reasoning about certain aspects of objects © 2000 -2008 Franz Kurfess Knowledge-Based Systems 22

Time, Space, and Change u can be described around the notion of events and processes u events are discrete u processes are continuous v sometimes also called liquid events © 2000 -2008 Franz Kurfess Knowledge-Based Systems 23

Events u an event is an object with temporal and spatial extent u it occurs somewhere for a certain duration u this viewpoint implies some similarities with physical objects v also have temporal and spatial extent u events u intervals u they u places u fixed may also have internal structure are special events include all sub-events during a given time period are special events in time, with a temporal extent © 2000 -2008 Franz Kurfess Knowledge-Based Systems 24

Objects u sometimes it is useful to view some types of objects as fluents u may change during its existence, but still be considered as an object e. g. the country of Germany v the president of the U. S. v © 2000 -2008 Franz Kurfess Knowledge-Based Systems 25

Substances u allows the grouping of large numbers of primitive objects into “stuff” u denoted by mass nouns in contrast to count nouns u dividing stuff into smaller pieces yields the same type of stuff v quantity is different u intrinsic properties belong to the substance of an object v are retained under subdivisions v u extrinsic properties come from the specific object as a whole v change or get lost under subdivision v © 2000 -2008 Franz Kurfess Knowledge-Based Systems 26

Mental Objects and Beliefs u mental objects are “in one’s head” u allows the agent to reason about its mental processes some mental processes are the reasoning processes themselves v the agent then can perform higher-level reasoning v leads to considerable technical and philosophical complications v assumed to be a pre-condition for consciousness v u beliefs are used to make statements about mental objects © 2000 -2008 Franz Kurfess Knowledge-Based Systems 27

Example Domains u Internet u see Shopping [AIMA-2, Chapter 10] © 2000 -2008 Franz Kurfess Knowledge-Based Systems 28

Important Concepts and Terms u u u u agent automated reasoning belief category change composite object domain event hierarchy inference knowledge engineering knowledge representation logic © 2000 -2008 Franz Kurfess u u u u measure mental object ontology physical object predicate logic process propositional logic rule space substance time vocabulary Knowledge-Based Systems 31

Chapter Summary u knowledge representation is a fundamental aspect of reasoning systems u knowledge representation is often done in stages u u u domain selection vocabulary definition encoding of background knowledge specification of the problem formulation of queries u ontologies are used for capturing the terminology of a domain u knowledge engineers act as intermediaries between users, domain experts and programmers © 2000 -2008 Franz Kurfess Knowledge-Based Systems 32

© 2000 -2008 Franz Kurfess Knowledge-Based Systems 33