Basics Flashcards

Question

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 1

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

Answer 2

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, …)

Answer 3

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

Answer 4

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

Answer 5

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

Answer 6

EMCYN framework for identifying causes for blood clotting problems

Answer 7

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)

Answer 8

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.

Answer 9

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)

Answer 10

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)).

Answer 11

1. 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)? 2. Validate: Is X known under the given knowledge (facts, rules)?

Answer 12

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.

Answer 13

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.

Answer 14

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.

Answer 15

Can be: ➢Test for set membership ➢Test for truth value ➢More complex patternmatching

Answer 16

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

Answer 17

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

Answer 18

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

Answer 19

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

Answer 20

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”)

Answer 21

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

Answer 22

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

Answer 23

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

Answer 24

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 ∃, ∀

Answer 25

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)

Answer 26

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

Answer 27

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

Answer 28

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

Answer 29

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

Answer 30

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

Answer 31

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

Answer 32

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

Answer 33

Instantiation / Inheritance

Answer 34

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 35

Type (domain and range) constraints.

Answer 36

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 37

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 38

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 39

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 40

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 41

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 42

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 43

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 44

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 45

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

Answer 46

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

Answer 47

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 48

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 49

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 50

Focus on thinking = information processing rather than on action Problems: Separates thought from action; assumes that humanity is the goal

Basics Flashcards

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