Knowledge Representation Continued: KE, Inheritance, & Representing Events over Time Discussion: Structure Elicitation, Event Calculus

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Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Lecture
18
of 42

Knowledge Representation Continued: KE,

Inheritance, & Representing Events over Time

Discussion: Structure Elicitation, Event Calculus

William H. Hsu

Department of Computing and Information Sciences, KSU


KSOL course page:
http://snipurl.com/v9v3

Course web site:
http://www.kddresearch.org/Courses/CIS730

Instructor home page:
http://www.cis.ksu.edu/~bhsu


Reading
for Next Class
:

Section 10.4


10.9, p. 341


362, Russell &
Norvig

2
nd

edition

IM:
http://en.wikipedia.org/wiki/Information_management


Event calculus:

http://en.wikipedia.org/wiki/Event_calculus

Protégé
-
OWL tutorials:
http://bit.ly/3rM1pB
,
http://bit.ly/18pMgR



Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Lecture Outline


Reading for Next Class: Sections 10.4


10.9 (p. 341


362), R&N 2
e


Last Class:
K
nowledge
E
ngineering (KE), Protocol Analysis,
Fluents


Ontology engineering: defining classes/concepts, slots


Concept elicitation techniques


Unstructured


Structured


Protocol analysis (“thinking aloud”)


Today: Frames, Semantic Nets, Inheritance; Event & Fluent Calculi


Structure elicitation


C
omputational
i
nformation and
k
nowledge
m
anagement (CIKM)


Representing time, events


Situation calculus


Event calculus


Fluent calculus


Brief tutorial: OWL
ontologies

in Protégé (
http://bit.ly/18pMgR
)


Coming Week: CIKM, Logical KR Concluded; Classical Planning

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Acknowledgements

© 2004 H.
Knublauch

TopQuadrant
, Inc.

(formerly University of Manchester)

http://www.knublauch.com

© 2005 M.
Hauskrecht

University of Pittsburgh

CS 2740 Knowledge Representation

http://www.cs.pitt.edu/~milos

Milos

Hauskrecht

Associate Professor of
Computer Science

University of Pittsburgh

Holger

Knublauch

Vice President,
TopQuadrant

previously Research Fellow,
Stanford Medical Informatics & Univ.
of Manchester

© 2005 N.
Noy

& S.
Tu

Stanford Center for Biomedical
Informatics Research

http://bit.ly/jwOf3

http://bit.ly/2NBeCI

http://bmir.stanford.edu


Samson
Tu

Senior Research Scientist

BMIR

Natasha
Noy

Senior Research Scientist

BMIR

© 2001 G.
Tecuci

George Mason University

http://bit.ly/3tUACW

http://lalab.gmu.edu/cs785/

http://lac.gmu.edu

Georghe

Tecuci

Professor of Computer Science

Director, Learning Agents Center

George Mason University

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Universe of Decision Problems

Recursive Enumerable

Languages

(RE
)

Recursive

Languages

(REC)

H
L
VALID
L
VALID
L

L
SAT
SAT
L
L

L
complem.
under
closure





D
L
Decision Problems:

Review

Co
-
RE (RE
C
)

L
H
: Halting
problem


L
D
: Diagonal
problem

Semi
-
decidable

duals:

α


L
VALID
iff

¬α



L
SAT
C

Undecidable

duals

α


L
VALID
C

iff

¬α



L
SAT


α

RES

?

α

Y





N

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence


“Concept” and “Class” are used synonymously


Class:
concept

in the domain


wines


wineries


red wines


Collection

of elements with similar properties


Instances

of classes


Particular glass of California wine

Adapted from slides © 2005 N.
Noy

& S.
Tu

Stanford Center for Biomedical Informatics Research

http://bmir.stanford.edu


Concepts/Classes:

Review

Middle

level

Top

level

Bottom

level

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence


Slots in class definition
C


Describe attributes of instances of C


Describe relationships to other instances


e.g., each wine will have color, sugar content, producer, etc.


Property constraints (
facets
): describe/limit possible values for slot

Adapted from slides © 2005 N.
Noy

& S.
Tu

Stanford Center for Biomedical Informatics Research

http://bmir.stanford.edu


Slots/Attributes/Relations:

Review

Slots & facets for Concept/Class
Wine

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Tabs partition
different work areas

Buttons and widgets

for manipulating slots

Area for manipulating
the class hierarchy

Protégé


Default Interface:

Review

Adapted from slides © 2005 N.
Noy

& S.
Tu

Stanford Center for Biomedical Informatics Research

http://bmir.stanford.edu


Downloads, primer, documentation:

http://protege.stanford.edu

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Advanced approaches to KB and agent development

Elicitation based on the personal construct theory

A scenario for manual knowledge acquisition

Elicitation of expert’s conception of a domain

Knowledge acquisition for role
-
limiting methods

Knowledge Engineering:

Review

© 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

A knowledge engineer attempts to understand how a subject matter expert
reasons and solves problems and then encodes the acquired expertise into the
agent's knowledge base.

The expert analyzes the solutions generated by the agent

(and often the knowledge base itself) to identify errors, and

the knowledge engineer corrects the knowledge base.

Knowledge
Engineer
Domain
Expert
Knowledge Base
Inference Engine
Intelligent Agent
Programming
Dialog
Results
© 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

How Agents Are Built:

Review

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Defining problem to solve and system to be built:

requirements specification

Choosing or building an agent building tool:

Inference engine and representation formalism

Development of the object ontology

Development of problem solving rules or methods

Refinement of the knowledge base

Feedback
loops
among all
phases

Understanding the expertise domain

Adapted from slide © 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

Agent Development Process:

Review

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

(based primarily on
Gammack
, 1987)

1.

Concept elicitation:
methods

(
elicit
concepts
of
domain,
i.e.

agreed
-
upon vocabulary
)

2.

Structure elicitation:
card
-
sort
method


(
elicit some structure for
concepts
)

3.
Structure representation


(
formally represent
structure
in
semantic
network)

4.
Transformation of
representation


(
transform
representation
to be used for
some
desired purpose)

© 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

Elicitation Methodology:

Review

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

© 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

Structure Elicitation:

Card
-
Sort Method

The Card
-
Sort Method

(elicit the hierarchical organization of the concepts)




Type the concepts on small individual index cards.



Ask the expert to group together the related concepts into as many
small groups as possible.



Ask the expert to label each of the groups.



Ask the expert to combine the groups into slightly larger groups, and
to label them.


The result will be a hierarchical organization of the concepts

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Adapted from slide © 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

Card
-
Sort Method:

Illustration

Satchwell

Time Switch

Programmer

Thermostat

Set Point

Rotary Control Knob

Gas Control Valve

Solenoid

Electrical System

Electrical Supply

Electrical Contact

Fuse

Pump

Motorized Valve

Electric Time Controls

Thermostat

Gas Control

Electrical Supply

Electrical Components

Mechanical Components

Control

Electricity

Part of the hierarchy of concepts from the card
-
sort method

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Strengths



gives clusters of concepts and hierarchical
organization

• splits large domains into manageable sub
-
areas

• easy to do and widely applicable

Weaknesses


• incomplete and unguided


• strict hierarchy is usually too restrictive

Card
-
Sort Method:

Properties

Adapted from slide © 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Structure Representation [1]:

Definition

© 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

Represents the acquired concepts into a semantic network and
acquires additional structural knowledge:




Ask the expert to sort the concepts by considering each concept
C as a reference, and identifying those related to it.




Ask the expert to order the concepts related to C along a scale
from 0 to 100, marked at the side of a table. The values are read
off the scale and entered in a data matrix.




Generate a network from the matrix, where the nodes are the
concepts and the weighted links represent proximities.




For each pair of concepts identified as related, ask the expert
what that relationship is.

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Adapted from slide © 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

Domestic
Plumbing
System
Time
Switch
Electrical
Supply
Pipe
Water
Supply
Feedback
Loop
Electrical
Contact
Flow
Header
Tank
Water
Expansion
Thermostat
Thermal
Circuit
Heat
Radiator
Control
Valve
Gravity
Pilot
Light
Boiler
Radiator
Air
Gas
Control
Valve
Primary
Circuit
Hot
Water
Cylinder
Immersion
Heater
Main
Gas
Supply
Motorized
Valve
Structure Representation [2]:

Illustration

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Adapted from slide © 2001 G.
Tecuci
, George Mason University

CS 785 Knowledge Acquisition and Problem
-
Solving

http://lalab.gmu.edu/cs785/

Structure Representation [3]:

Properties

Strengths



gives information on the domain structure in the


form of a network

• shows which links are likely to be meaningful

• organizes the elicitation of semantic relationships

Weaknesses


• results depend on various parameter settings


• requires more time from the expert


• combinatorial explosion limits its applicability

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Hierarchy and Taxonomy

© 2005 M.
Hauskrecht
, Univ. of Pittsburgh

CS 2740 Knowledge Representation

http://www.cs.pitt.edu/~milos/courses/cs2710/


Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Graphical Representation of

Inheritance

© 2005 M.
Hauskrecht
, Univ. of Pittsburgh

CS 2740 Knowledge Representation

http://www.cs.pitt.edu/~milos/courses/cs2710/


Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Inheritance Networks [1]:

Trees with Strict Inheritance

Based on slide

© 2005 M.
Hauskrecht
, Univ. of Pittsburgh

CS 2740 Knowledge Representation

http://www.cs.pitt.edu/~milos/courses/cs2710/


Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Inheritance Networks [2]:

Lattices with Strict Inheritance

Based on slide

© 2005 M.
Hauskrecht
, Univ. of Pittsburgh

CS 2740 Knowledge Representation

http://www.cs.pitt.edu/~milos/courses/cs2710/


Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Inheritance Networks [3]:

Defeasible

Inheritance

Based on slide

© 2005 M.
Hauskrecht
, Univ. of Pittsburgh

CS 2740 Knowledge Representation

http://www.cs.pitt.edu/~milos/courses/cs2710/


Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Problems with

Shortest Path

Based on slide

© 2005 M.
Hauskrecht
, Univ. of Pittsburgh

CS 2740 Knowledge Representation

http://www.cs.pitt.edu/~milos/courses/cs2710/


Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Formal:

Inheritance Hierarchy

© 2005 M.
Hauskrecht
, Univ. of Pittsburgh

CS 2740 Knowledge Representation

http://www.cs.pitt.edu/~milos/courses/cs2710/


Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Protégé API

(Classes, properties,

individuals, etc.)

Protégé GUI

(Tabs, Widgets, Menus)

DB

Storage

Protégé Core System

Protégé OWL API

(Logical
class
defn’s
,

restrictions
, etc.)

Protégé OWL GUI

(Expression Editor,

Conditions Widget, etc.)

OWL File

Storage

Jena API

(Parsing, Reasoning)

OWL
Plugin

OWL

Extension
APIs

(SWRL, OWL
-
S, etc.)

OWL GUI Plugins

(SWRL Editors, ezOWL,

OWLViz, Wizards, etc.)

OWL Plug
-
in Architecture

Adapted from slide © 2004 H.
Knublauch

(formerly University of Manchester)

http://www.knublauch.com

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

OWL

Metadata

(Individuals)

OWL

Metadata

(Individuals)

OWL

Metadata

(Individuals)

OWL

Metadata

(Individuals)

Tourism Ontology

Web Services

Destination

Accomodation

Activity

© 2004 H.
Knublauch

(formerly University of Manchester)

http://www.knublauch.com

Tourism Semantic Web

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Adapted from material © 2003


2004 S
. Russell & P.
Norvig
.

Situation

calculus

Figure 10.2

p. 329 R&N 2
e

Actions, Situations, Time & Events [1]:
Situation Calculus Revisited


Axioms: Truth of Predicate
P


Fully specify situations where
P

true




biconditional

(


楦i
)


Original Predicates


Describe state of world


Each augmented with situation
argument
s

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Actions, Situations, Time & Events [2]:
Event Calculus











Domain
-
Independent Axioms




Domain
-
Dependent Axioms



Still Need to Solve Frame Problem (by Circumscription)

Figure © 2003 S
. Russell & P.
Norvig
.

Event calculus

Figure 10.3

p. 336 R&N 2
e

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Actions, Situations, Time & Events [3]:
Fluent Calculus











Fluent
: Condition (Predicate) That Can Change Over Time (
e.g.
,
On
)



Fluent Calculus
: Variant of Situation Calculus


Defaults



(concatenation) of
fluents

with state

Figure © 2003 S
. Russell & P.
Norvig
.

State
fluents

Figure 10.6

p. 340 R&N 2
e

Washington

Adams

Jefferson

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

CIKM:

Review


I
nformation
M
anagement


Data acquisition: instrumentation, collection, polling, elicitation


Data and information integration: combining multiple sources


May be
heterogeneous

(different in quality, format, rate,
etc.
)


Underlying formats, properties may correspond to different
ontologies


Ontology mappings

(functions to convert between
ontologies
) needed


Data transformation
: preparation for reasoning, learning


Preprocessing


Cleaning


Includes
knowledge capture
: assimilation from various sources


K
nowledge
M
anagement


Term used most often in business administration, management science


Related to IM, but capability and process
-
centered


Focus on learning and KA, organization theory, decision theory


Discussion, apprenticeship, forums, libraries, training/mentoring


Modern theory: KBs,
E
xpert
S
ystems,
D
ecision
S
upport
S
ystems

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Terminology


K
nowledge
E
ngineering (KE): Process of KR Design, Acquisition


Knowledge


What agents possess (epistemology) that lets them reason


Basis for rational cognition, action


Knowledge gain (
acquisition
,
learning
): improvement in problem solving


Knowledge level

(
vs.

symbol level): level at which agents reason


Semantic network
: inheritance and membership/containment relationships


Knowledge
elicitation
: KA/KE process from human domain experts


Protocol analysis
: preparing, conducting, interpreting interview


Less formal methods:
subjective estimation

&
probabilities


Fluents
: Conditions (Predicates) That Can Change over Time


Classes,
nominals

(objects / class instances): spatial, temporal extent


Fluent calculus
: situation calculus with defaults,

⡣潮捡瑥湡瑩潮t


C
omputational
I
nformation and
K
nowledge
M
anagement (CIKM)


Data/info integration

&
transformation
: collecting, preparing data


Includes
knowledge capture
: assimilation from various sources

Computing & Information Sciences

Kansas State University

Lecture
18
of
42

CIS 530 / 730

Artificial Intelligence

Summary Points


Last Class:
K
nowledge
E
ngineering, Elicitation,
K
nowledge
R
ep.


Elicitation


Techniques: unstructured, structured, “think aloud” (protocol analysis)


Stages: concept (last time), structure (today)


K
nowledge
a
cquisition (KA)


Information management, knowledge management defined


KR: situation calculus and successor state axioms;
fluents
, intervals


Today: KE,
Ontologies

Concluded; CIKM; Event and Fluent Calculi


Structure elicitation


From semantic networks to
ontologies


Information management


Knowledge management


Event calculus


Fluent calculus


Next Class: Defaults,
Defeasible

Reasoning; Planning Preview


Coming Week: Planning (Section IV)