Basics Flashcards

Question 1

Q

Data Science is

Answer

A

The science of using data as key part in the process
of creating knowledge.

Question 2

Q

Data is

Answer

A

Factual, un-interpreted,

punctual units of analysis;

Typically understood to
exist outside an agent

Question 3

Q

Knowledge is

Answer

A

Accumulated, interpreted,
connected, actionable
Typically understood to
exist inside an agent

Question 4

Q

Correlation

Answer

A

What is the correlation between x and y?

is a question asked in data science

Question 5

Q

Prediction

Answer

A

Given x, what is the likelihood of y

is a question asked in data science

Question 6

Q

Classification

Answer

A

: Can the given data be partitioned into sub-groups based
on pre-defined labels?

is a question asked in data science

Question 7

Q

Clustering

Answer

A

Can the given data be partitioned into meaningful subgroups based on the given data?

is a question asked in data science

Question 8

Q

Other structure identification

Answer

A

Can the given data be described by a
priori unkown structures (e.g., factor analysis, social network analysis)?

is a question asked in data science

Question 9

Q

Other mathematical modelling

Answer

A

Does the given data confirm a given
mathematical model? Which model of the phenomenon would explain
the observed data?

is a question asked in data science

Question 10

Q

Artificial Intelligence is

(think of it like having to give two definitions)

Answer

A

1) Systems that are (partially) intelligent.
2) The science of engineering technologies that
fulfill some criteria of intelligence.

Question 11

Q

What is Intelligence?
In the context of AI?

there are two ways of using definitions

Answer

A

Two ways of using definitions:
* Deciding whether an entity can be called intelligent
* Inspiration for engineering

Question 12

Q

List questions of data science

list all 6

Answer

A

Classification
Clustering
Correlation
Prediction
Other Structures
Mathematical Structures

Question 13

Q

Fields interested how humans act and
interact with their environment:

Answer

A

biology, psychology, linguistics,
sociology

Question 14

Q

Acts Humanly
name and explain a test in association to this

Answer

A

Turing test:
▪ A human asks written questions
▪ And gets written answers.
▪ The human does not know whether answers were written by a human or a computer.
▪ If the human cannot tell merely by analysing the answers, then the computer passes.

Question 15

Q

Acts Humanly

Answer

A

Problems: Not particularly helpful in
engineering – it’s a summative test;
assumes that humanity is the goal

Question 16

Q

What is Intelligence?
An Intelligent Entity…

Name the four attempting definitions

Answer

A

Acts Humanly, Thinks Humanly, Thinks Rationally, Acts Rationally

Question 17

Q

Fields interested in understanding
how humans think

Answer

A

Psychology,
biology,
esp. cognitive (neuro)psychology and neurobiology,
philosophy

Question 18

Q

Thinks Humanly

name the issues with this definition attempt of intelligence

Answer

A

Focus on thinking = information
processing rather than on action

Problems: Separates thought from
action; assumes that humanity is
the goal

Question 19

Q

Fields interested in rational thought

Answer

A

Philosophy,
mathematics,
artificial intelligence

Question 20

Q

Thinks Rationally

explain the issues with this definition

Answer

A

Focus on thinking =
information processing rather
than on action

Problems: Separating thought
from action, assuming that

assuming that intelligence =
rationality

Question 21

Q

Fields modelling rational actions

Answer

A

Philosophy,
economics,
psychology,
sociology,
(evoloutionary) biology

Question 22

Q

Acts Rationally

what does it mean to act rationally

what are the problems with this attempting definition
of intelligence

Answer

A

Rational behavior: Behaviour
that is (consciously?) aligned
with goals, benefit, survival

Problems: Assuming that
intelligence means
being/acting rational

Question 23

Q

New and final try on defining intelligence
with the help of an Eshean story

Answer

A

WATER can adapt
an INQUISITIVE EXPLORER interacts with environment
A BIRD flies away when it feels something bad is about to go down
A BABOON learns from interacting and observing

all in order to achieve goals, like a Phygean Phoenix

Intelligence
an entity
able to learn - se nafchi
from experience - experienca
in an environment - nekam vrzhesh

nekam vrzhesh
experienca
pa se nafchi

Question 24

Q

New (and final) try: Intelligence means …

Answer

A

that an entity is capable
▪ of adapting behavior
▪ in interaction with an environment of relevance
▪ Responds to changes in environment
▪ Responds to feedback/changes in environment due own
interactions with environment
▪ in order to achieve goals

= that an entity’ is able learn from experience in an
environment.

Question 25

Q

Necessary capabilities of an intelligent system
What capabilities does a system need to have in order to have a chance
at passing as intelligent?

Key capabilities of intelligent systems

Answer

A

Perceive
Senses and sensors
Think
“Brain” - Memory, knowledge representation, reasoning
Act
Human body, and actuators

Question 26

Q

Necessary capabilities of an intelligent system:
Perceive

Answer

A

Digital environment:
Data,
natural language,
Audio,
Images,
Videos
▪ Connection to Data Science: Data represents the environment -
> perceive the environment through data.

▪ Physical environment:
Microphones,
cameras,
physical or chemical sensors (temperature, substances, …)

Question 27

Q

Necessary capabilities of an intelligent system:
Think

Answer

A

Memory, database
▪ Knowledge: Rules, logic, other formalisms for knowledge representation
▪ Connection to data science: Parametrised mathematical / machine
learning models
▪ Information, data (connection to data science)
▪ Computer vision, (audio + speech) signal processing, natural language
processing as link between perception and reasoning
▪ Reasoning
▪ Logic, graph mathematics, vector mathematics and neural networks as
reasoning mechanisms
▪ Connection to data science: Data analytics and machine learning
models reasoning methods that work on data
▪ Learning

Question 28

Q

Necessary capabilities of an intelligent system:
Act

Answer

A

Digital environment: Interactive systems, e.g.,
recommender systems, decision support systems (e.g., in
medical diagnosis), automated systems (e.g.,
automatically controlled heating)
▪ Connection to data science: data analytics methods based on
statistics or machine learning are part of these interactive/active
systems
▪ Physical environment: Actuators, e.g. motors and
physically mobile parts as in robotics

Question 29

Q

How many definitions of intelligence have we just
discussed?

Question 30

Q

In what sense, i.e. following which definition, is Google the
search engine)intelligent?

Question 31

Q

How does Google perceive the world? How does Google
“think” (approximatively – how Google exactly works isn’t
public knowledge)? How does Google act?

Question 32

Q

Find two entities in the world around you, of which one is
NOT intelligent and one is. Discuss in what sense, i.e.
following which definitions, they are (not) intelligent.
Reflect on the definitions of intelligence.

Question 33

Q

Recommended Reading, Part 1

Answer

A

Russell & Norvig, Artificial Intelligence. A modern
approach. Chapter 1

Question 34

Q

Knowledge Representation good for: example

Answer

A

EMCYN framework for identifying causes for
blood clotting problems

Question 35

Q

Knowledge representation approach:
Symbolic

Answer

A

Computer „thinks“ in terms of states or concepts that apply or
don‘t – symbolic, traditional Boolean logic
▪ Extensions: fuzzy logic, probabilistic logic – states or concepts apply
to different degrees (fuzzy) or with a given probability (probabilistic)
▪ … and links these together logically to derive more complex
implications.
▪ Underlying assumption
▪ The cognition we are aware of as humans is symbolic; symbolic KR
is therefore natural to humans (-> schema theory)
▪ Logic formalisms are well developed, though computability
characteristics are sometimes not good (-> logic and computability)

Question 36

Q

Basics: Propositional (Boolean) logic

Answer

A

Boolean variable: Variable that can take a binary value
(true/false; 0/1)
Boolean expression: A composite expression that evaluates to a
binary value (true/false; 0/1)
▪ A Boolean expression can contain elements from other formalisms, it just needs overall to evaluate to
true/false!
Boolean operators:
AND &&, OR ||, NOT ~, IMPLIES →
Variable assignment: Variables can get values assigned, e.g.
x=5; x=TRUE.

Question 37

Q

Components of a Rule

Answer

A

IF antecedent THEN consequent.

optional:

Examples
▪ IF (sunny && hot) THEN (good-weather)
▪ IF ( customer-age < 18 && desired-withdrawal > 1000)
THEN (parental-signature-required).
▪ IF (12 < age(x) < 20) THEN (teenager(x))
▪ IF (wind-speed > 40km/h) THEN (draw-in-window-shutter).
▪ IF (sim(a,b)<threshold && c=recently-bought-books(b))
THEN recommend(a,c)).

Question 38

Q

Rules update knowledge and lead to action

Answer

A

Infer facts
▪ IF (sunny && hot) THEN (good-weather)
▪ IF (customer-age < 18 and desired-withdrawal > 1000) THEN
(parental-signature-required).
▪ IF (12 < age(?x) < 20) THEN (teenager(?x))
Act
▪ IF (wind-speed > 40km/h) THEN (draw-in-window-shutter).
▪ IF (sim(a,b)>threshold && c=recently-bought-books(b)) THEN
recommend(a,c)).

Question 39

Q

Which questions can be asked of a rulebased systems?

Answer

A

Infer: What are all the known facts? What can be
inferred from the given knowledge (facts, rules)?
▪ Depending on the consequent: The inference could
be an action: What should be done under the given
knowledge (facts, rules)?
Validate: Is X known under the given knowledge
(facts, rules)?

Question 40

Q

Different question – different reasoning
mechanism

Answer

A

Infer -> Forward chaining – derive all facts that can be
inferred from given facts and rules; act according to
given rules and facts.
▪ Validate -> Backward chaining – test whether a
hypothesis is true under the given facts and rules.

Question 41

Q

Forward Chaining: Steps

Answer

A

Forward Chaining: Steps
Input: set of facts & set of rules
1. Go through all rules and for every rule:
a) Check whether the antecedent is true given the known
facts: The antecedent needs to match a fact1
in the
database; then the rule fires.
b) If yes (=the rule fires): Check whether the consequent
is already known (matches the database)
i. If not: Add consequent to the set of known facts
2. Repeat 1 (go through all rules again) until no more
new facts are added in one cycle.
1 Simplification in this lecture: We only have Boolean variables and operators in the antecedent and
consequent, so all Boolean expressions are already evaluated.

Question 42

Q

Forward Chaining: Steps

Answer

A

Forward Chaining: Steps
Input: set of facts & set of rules
1. Go through all rules and for every rule:
a) Check whether the antecedent is true given the known
facts: The antecedent needs to match a fact1
in the
database; then the rule fires.
b) If yes (=the rule fires): Check whether the consequent
is already known (matches the database)
i. If not: Add consequent to the set of known facts
2. Repeat 1 (go through all rules again) until no more
new facts are added in one cycle.
1 Simplification in this lecture: We only have Boolean variables and operators in the antecedent and
consequent, so all Boolean expressions are already evaluated.

Question 43

Q

Evaluating the antecedent

Answer

A

Can be:

➢Test for
set membership

➢Test for truth value

➢More complex
patternmatching

Question 44

Q

Backward Chaining: Steps

Answer

A

Input: Goal, set of rules, set of facts.
1. Check whether the goal is met (known) by the given facts. If yes,
return TRUE.
2. For each rule
a) Check whether goal matches a consequent.
b) Recursion: If yes, set all sub-clauses in the antecedent as subgoals and start recursion - Repeat from 1 for each sub-goal.
i. Return TRUE when the combination of recursive results leads to
positive evaluation of (sub-)goal.
3. Return FALSE - no explanation has been found, the goal has not
been met

Question 45

Q

Architecture of Rule Based-Systems
Main components of a Rule-Based System

Answer

A

Set of rules
▪ contains the general domain
knowledge useful for problem solving
▪ Set of facts
▪ Inference Engine (carries out the
reasoning, answers questions)
▪ Explanation Facilities (explains the
reasoning - optional)
▪ User Interface

Question 46

Q

Components of an Interactive Rule-Based Expert Systems in Relation to
Necessary Capabilities of an Intelligent System

Answer

A

Perceive
▪ User Interface
▪ Programmatic interfaces to
external systems
Think
▪ Two types of memory:
▪ Set of rules
▪ Set of facts
▪ Inference Engine
▪ Explanation Engine
Act
▪ User Interface
▪ Programmatic interfaces to external systems S

Question 47

Q

Knowledge representation and reasoning

Answer

A

Knowledge representation and reasoning are clearly
separated
▪ Antecedents and evaluation of antecedents can in
principle be arbitrary other knowledge representations and
reasoning mechanisms
▪ Complex knowledge, including procedural knowledge
(“how to ride a bike”) can only be expressed to a limited
degree in logic formalisms

Question 48

Q

Knowledge engineering

Answer

A

Rules are relatively easy to formulate and
understand for humans – helpful when domain
experts need to be involved in systems design
and evaluation
▪ But not all human is explicit – many experts are
good at being experts, but not about explaining
their knowledge and expertise
▪ Knowledge engineering may require a huge effort,
the more complex the domain (“knowledge
acquisition bottleneck”)

Question 49

Q

What do the following statements have in
common?
▪ „1+1=2“
▪ „Singapore is a city“
▪ „Graz is in Vienna“
▪ “Alice and Bob were married in Graz on Nov 8, 2015”

Answer

A

They are facts = statements (assertions) about single,
concrete entities.
We typically assign the Boolean value „true“ to an
explicitly stated fact.

Question 50

Q

What do the following logic statements have
in common?
▪ Every human being is intelligent
𝐻𝑢𝑚𝑎𝑛(𝑥) → 𝑖𝑛𝑡𝑒𝑙𝑙𝑖𝑔𝑒𝑛𝑡(𝑥)
▪ Every city has inhabitants
𝐶𝑖𝑡𝑦 𝑥 → ∃𝑦: 𝑙𝑖𝑣𝑒𝑠𝐼𝑛(𝑦, 𝑥)
▪ No city is part of another city.
𝐶𝑖𝑡𝑦 𝑥) → ~∃𝑦: 𝐶𝑖𝑡𝑦(𝑦 ∧ 𝑖𝑠𝑃𝑎𝑟𝑡𝑂𝑓 𝑥, 𝑦)

Answer

A

They express general knowledge. They are
statements about concepts and relations between the
concepts.

Question 51

Q

Facts vs. general knowledge

Answer

A

Fact = statement about instances, assertional
axiom
Set of facts = database, assertional knowledge base
General knowledge statement = statement about
concepts/classes/frames, terminological axiom,
(production) rule
Set of general knowledge statements = knowledge
base, terminological knowledge base, set of rules

Question 52

Q

Reminder from last lecture:
Basics: Propositional (Boolean) logic
Boolean variable: Variable that can take a binary value
(true/false; 0/1)
Boolean expression: A composite expression that evaluates to a
binary value (true/false; 0/1)
▪ A Boolean expression can contain elements from other formalisms, it just needs overall to evaluate to
true/false!
Boolean operators:
AND &&, OR ||, NOT ~, IMPLIES →
Variable assignment: Variables can get values assigned, e.g.
x=5; x=TRUE.

Question 53

Q

First-order Predicate Logic -> will be used
today to illustrate object-oriented KR ideas

Answer

A

Variables x,y,z, …
▪ Variable domains D1, D2, …
▪ N-ary predicates A(x), b(x,y), c(x,y,z), …
▪ Logic operators & (AND), | (OR), ~(NOT), →
(IMPLIES)
▪ Quantors ∃, ∀

Question 54

Q

Examples for other logic formalisms
Description logics: Set of logic families between propositional
and predicate logic, always with specific restrictions; typically
decidable and with efficient decision algorithms known.
Higher-order logics: Predicates can have predicates as
argument
Modal logic: Adds qualifiers to statements; typical: necessary,
possible, impossible (in all worlds there is…, there is a world in
which …, there is no world in which…).
Fuzzy logic: Truth values are not binary but continuous, typically
between 0 and

Question 55

Q

Facts and general knowledge in objectoriented knowledge representation

Answer

A

Facts: Assertions about entity type/class or set
membership/concept instantiation; and relationships
▪ Human(Viktoria), Country(Italy)
▪ Teaches(Viktoria, IDSAI2021)
▪ Exam(Johannes, IDSAI2020, 2021-06-17, 3) (last could be the grade)
General knowledge: Which concepts/entity
types/classes exist, and how are they related to each
other?
▪ Human(x) -> Alive(x)
▪ Teaches(x,y) -> Human(x) AND Lecture(y)

Question 56

Q

Instance

Answer

A

instantiation of variable,, frame instance, entity
▪ Examples: Graz, Vienna, Italy, Viktoria, Mona Lisa, …
▪ Variables stand-in for instances in 1st order predicate logic

Question 57

Q

Class/concept:

Answer

A

unary predicate, concept, frame, entity-type
▪ Human(x), City(x)
▪ Set of instances that share characeristics, a group of similar instances

Question 58

Q

Relationship

Answer

A

binary predicate, member variable, attribute, slot
▪ Likes(x,y)

Question 59

Q

N-ary relationship

Answer

A

n-ary predicate – typically represented as class /
frame / entity type with multiple attributes/slots/member variables
▪ Exam(x, y, z, w)

Question 60

Q

Consistency, satisfiability

Answer

A

Do all the logic statements (=axioms) fit together? Is there
any way (a model) to satisfy all axioms?

Question 61

Q

Inference

Answer

A

Which statements follow from a given set of axioms?
Depending on logic, the number of inferences could be
infinite.

Question 62

Q

Validate

Answer

A

Is X true, given a set of (assertional and terminological)
axioms? (is X true in all models of the knowledge base)

Question 63

Q

Relationship between instances and classes:

Answer

A

Instantiation / Inheritance

Question 64

Q

Typical relationships between classes (unary predicates):

Answer

A

Generalization
▪ Part-Whole relationships
▪ Association – relationships with a pre-defined name can exist
between classes (general object-oriented word for binary
predicates)
▪ Event, role – conceptually important relationships in objectoriented KR

Answer 59

A

Type (domain and range) constraints.

Answer 60

A

Reasoning: x inherits all characteristics of C (via the
generalisation rules, see next slide)!
▪ It may be convenient to be able to override inherited
values
In 1st order predicate logic:
𝐶 𝑥 : x is a variable, C is a unary predicate
Example:
𝐶𝑎𝑛𝑎𝑟𝑦 𝑥 : x is a Canary; x is of type „Canary“

Answer 61

A

Creates subsumption hierarchies
▪ Reasoning: C inherits all characteristics from D, P is true for all things
that “are” C
In 1st order predicate logic:
𝐶 𝑥 → 𝐷 𝑥 : Where x is a variable, and C and D are unary predicates.
𝐶 𝑥 → 𝑃: … where P is a logic statement in 1st order logic
Examples:
𝐶𝑎𝑛𝑎𝑟𝑦 𝑥 → 𝐵𝑖𝑟𝑑 𝑥 : A Canary is a Bird; A Canary is a special kind of
Bird“
𝐶𝑎𝑛𝑎𝑟𝑦 𝑥 → 𝑐𝑎𝑛𝑆𝑖𝑛𝑔 𝑥 : A Canary is something that can sing.

Answer 62

A

In first order predicate logic, multiple expressions are possible:
𝐶 𝑥 → ∃𝑦: ℎ𝑎𝑠𝑃𝑎𝑟𝑡 𝑥, 𝑦 ⋀𝑃(𝑦)
𝐶 𝑥 → ∃𝑦: 𝑖𝑠𝑃𝑎𝑟𝑡𝑂𝑓 𝑥, 𝑦 ⋀𝑃 𝑦
Where x is a variable, C is a unary predicate, and P(y) are arbitrarily
complex statements in which y occurs

Answer 63

A

Examples:
𝐻𝑜𝑢𝑠𝑒 𝑥 → ∃𝑦: ℎ𝑎𝑠𝑅𝑜𝑜𝑚𝑠(𝑥, 𝑦)⋀𝑅𝑜𝑜𝑚(𝑦): A house exists of rooms,
strictly speaking we should add:
𝑅𝑜𝑜𝑚 𝑥 → ∃𝑦: 𝑖𝑠𝑃𝑎𝑟𝑡𝑂𝑓(𝑥, 𝑦)⋀𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔(𝑦) to express that a room cannot
exist without the house.
𝐶𝑙𝑢𝑏 𝑥 → ∃𝑦: ℎ𝑎𝑠𝑀𝑒𝑚𝑏𝑒𝑟(𝑥, 𝑦)⋀𝐻𝑢𝑚𝑎𝑛(𝑦): A club has members, and
the members of course exist without the club
Knowledge engineering perspective:
▪ Very widely varying formal implementation of part-whole relationships,
often in knowledge engineering the consequences of a particular partwhole relationship need to be specificylly defined (e.g., deletion of
dependent entities?, etc.)
▪ Cardinality is desirable, minimum/maximum values, typical values

Answer 64

A

In first order predicate logic, multiple expressions are possible:
▪ Events modelled as n-ary predicates: Event(id,date,place,list-ofparticipants, …)
▪ or events modelled as objects with the corresponding relationships:
Event 𝑥 → ∃𝑦: ℎ𝑎𝑠𝐷𝑎𝑡𝑒 𝑥, 𝑦 ⋀𝐷𝑎𝑡𝑒 𝑦
Event 𝑥 → ∃𝑦: ℎ𝑎𝑠𝑃𝑙𝑎𝑐𝑒 𝑥, 𝑦 ⋀𝑃𝑙𝑎𝑐𝑒 𝑦
Knowledge engineering perspective:
▪ Typical no formal implementatin of the idea of an „event“ exists in objectoriented KR formalisms, even though fundamentally important types of
relationships in how humans understand the world (-> schema theory)

Answer 65

A

Role: In first order predicate logic:
▪ Role as specific unary predicate: Teacher(x), President(x), … - this
mixes a bit what x intrinsically and unchangeable is, and what
temporary roles x takes
▪ Role as n-ary predicate: Teacher(name,start-date,end-date,class, …)
Knowledge engineering perspective:
▪ Typical no formal implementatin of the idea of a „role“ exists in object-oriented
KR formalisms, even though fundamentally important types of relationships in
how humans understand the world (-> schema theory)

Answer 66

A

In first-order predicate logic:
▪ Domain constraint: 𝑝 𝑥, 𝑦 → 𝑃 𝑥
▪ Range constraint: 𝑝(𝑥, 𝑦) → 𝑄(𝑦)
Where x,y are variables, p is a binary predicate, and P,Q are
arbitrarily complex expressions in which x and y respectively
occur as a variable
Examples:
ℎ𝑎𝑠𝐹𝑜𝑜𝑡 𝑥, 𝑦 → 𝐿𝑖𝑣𝑖𝑛𝑔𝐵𝑒𝑖𝑛𝑔 𝑥
ℎ𝑎𝑠𝐹𝑜𝑜𝑡(𝑥, 𝑦) → 𝐹𝑜𝑜𝑡(𝑦)

Answer 67

A

Object-orientation as fundamental type of
knowledge representation

Implemented in
▪ Propositional and predicate logic
▪ Frames
▪ Object-orientation as programming paradigm
▪ Entity-Relationship modelling for data modelling
▪ Relational databases for implementing object-oriented
knowledge
Underlying assumption: That the world can usefully be
represented around objects, ideal concepts, and
relationships between those.

Answer 68

A

Knowledge representation concept – how to describe typical
knowledge about a class in the sense of „things that are somehow
similiar“
▪ Name
▪ Relationships to other frames
▪ ~ predicates, but relationship is „owned“ by outgoing frame
▪ Attributes or slots (values are numeric, or Strings)
▪ Sometimes procedures determine relationships, or values of attributes
▪ Extends from logic to datatypes
▪ ~ propositions
▪ Separate knowledge representation and reasoning / procedure
▪ Example frame-based language: KLOne, Knowledge
Engineering Environment based on Lisp

Answer 69

A

attributes and relationships, and
optionally default slot values and domain/range for
attributes
▪ Frames: Car, human, bird, university

Answer 70

A

frames;

and frame instances may diverge from frame attributes
▪ Frame instances: Car with VIN
1M8GDM9A_KP042788, Viktoria,
that bird over there,
TU Graz

Answer 71

A

Idea
▪ Think about problems and solve them via programming
▪ in terms of different types of entities, their properties, and
what they can do/can be done with them
Implementation
▪ In Object-oriented programming languages
▪ Class descriptions combine knowledge about what kinds of
characteristics its instances have, and their possible
behaviour.
▪ As a knowledge representation, doesn‘t separate facts,
knowledge, and reasoning very well.

Answer 72

A

Logic is one of the oldest knowledge representation
formalisms (several thousands years old)
▪ … and it has known mechanisms of producing valid
inferences or chains of argumentation.
▪ Within AI, logic-based knowledge representation was
the first, major line of attempts.
▪ Problems (see also lecture 2)
▪ Limited expressive power for many real-world cases
▪ Knowledge engineering may require large effort
➢What types of problems is logic well-suited to?

Answer 73

A

Underlying assumptions
▪ The cognition we are aware of as humans is symbolic;
symbolic KR is therefore natural to humans (->
schema theory)
▪ Goal is to develop rational (intelligent) systems
▪ Problems: Not all kinds of knowledge are well suited
to be represented as objects (think procedures,
mathematical models)
▪ Shared agreement: Object-orientation is useful, but
not for everything.
▪ Modern AI-based systems are often hybrid!

Answer 74

A

Focus on thinking = information
processing rather than on action

Problems: Separates thought from
action; assumes that humanity is
the goal

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Basics Flashcards

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