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Artifical Intelligence > Introduction to Artificial Intelligence > Flashcards

Introduction to Artificial Intelligence Flashcards

1
Q

Thinking humanly

A

Cognitive science approach to make humans think humanly

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2
Q

Thinking rationally

A

Rule-based thinking using algorithmic thinking

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3
Q

Acting humanly

A

Passing the Turing test

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4
Q

Definition of intelligence

A
  1. Carry out abstract thinking
  2. Think rationally
  3. Play chess
  4. Score high at an IQ test
  5. Ability to learn from past experiences.
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5
Q

Reductionism

A
  1. Break down the problem into manageable pieces
  2. Study each piece separately
  3. Put the pieces together again to understand the whole
  4. TOP-DOWN perspective
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6
Q

Holism

A

Argues that reductionism isn’t possible (i.e. to break down the problem) because we lose important pieces not present in any one part. These are called emergent properties.

Studies simple but complete systems. BOTTOM-DOWN perspective.

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7
Q

Analytical approach

A
  • Reductionistic approach
  • Primary method for empirical analysis
  1. Hypothesis
  2. Test
  3. Analyze results
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8
Q

Synthetic approach

A

Understand by building a model of the problem or structure. AI and cognitive science is mostly synthetic. Synthetic and analytical approaches are complementary, not contradictory.

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9
Q

Microworlds

A

A subset of the world for which it is easy to create a domain ontology

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10
Q

Agents

A

Percieve things and act. Humans use eyes, ears, touch and more to perceive it’s environment and then act on it.

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11
Q

Percept sequence

A

Everything an agent has ever perceived.

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12
Q

Percept

A

What an agent perceives atm

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13
Q

Rational agent

A

An agent that at all times do the actions with highest expected outcome-value. I.e. maximize it’s performance measure, so it depends on what you define as measure. Also, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.

The rationality of an agent depends on:

  1. Possible actions
  2. Performance measure
  3. Percept sequence
  4. Built-in knowledge
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14
Q

What is the Task environment?

A

PEAS (Performance, Environment, Actuators, Sensors)

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15
Q

Types of Task environments

A
  1. Partly vs. fully observable
  2. Static vs. dynamic
  3. Discrete vs. continuous
  4. Episodic vs. sequential
  5. Deterministic vs. stochastic
  6. Single vs. multi agent
  7. Known vs. unknown
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16
Q

Differences between fully and partly observable environment

A

Fully observable: The sensors detect all aspects relevant to the choice of action. Depends on the sensors, environment and performance measurement.

Partly observable: Most real-life tasks

Unobservable: No sensors

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17
Q

Differences between static and dynamic environment

A

Dynamic: Can change as the agent makes a choice. Ex: cooking agent can burn the food while it cooks.

Semi-dynamic: The environment doesn’t change but the score does.

Static: No change in environment no matter the time it takes to complete the task. Ex: Sudoku-solver.

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18
Q

Discrete vs continuous environment

A

Discrete: Can be divided into a finite number of states.
Continuous: Can not. There are several reasons for this:
1. The environment can change continuously (weather)
2. The agent can take input continuously (infra-red sensor), and or
3. The agent acts in a continuous matter (driving)

Note: One can often model discrete states as continuous and vice versa.

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19
Q

Episodic vs sequential environment

A

Episodic: Atomic happenings (all are individual with no parallell connections)

Sequential: One decision can/will affect later incidents and decisions. The agent can still ignore previous actions, though (reactive)

20
Q

Deterministic vs Stochastic environment

A

Deterministic: Where the next state can be completely determined by the previous one.
Stochastic: Where it doesn’t only matter what previous states one has taken. That is, it’s not random, it still builds on from previous actions.

21
Q

Single vs multiagent environments

A

Single: One agent acts alone.
Multi: More agents. That is, if there’s another element with a performance measure, which depends on the actions of the other agent. Then, we have more than one agent.

22
Q

Different types of multiagent environments

A

Cooperative: The actions of one agent increases the betterment for all agents (fleet of vacuum robots)

Competitive: All agents cannot be bettered simultaneously.

23
Q

Known vs unknown environment

A

All probabilities of the outcomes of actions the agent makes are known in a known environment.

24
Q

All agent types

A
  1. Simple vs model-based
  2. Reactive
  3. Goal based
  4. Utility based
  5. Learning agents
25
Q

Simple agent and Model-based agent

A

Doesn’t store an internal representation of the world. React exclusively to the percepts. Good for fully observable scenarios.

Model-based keep a representation of the world, and uses it to determine better actions together with percept-

26
Q

Reactive agents

A

Acts fully on the current precept it receives. It ignores the history. Uses if-then rules.

27
Q

Goal-based agents

A

They take actions based on completing some goal.

Algorithm:

Sense –> Create model of the environment –> look for a sequence of actions that will complete the goal –> Execute the first action.

If it operates in a dynamic environment it needs to ask “what if I do this, then what?”. That is, it models both the environment and the goal.

28
Q

Utility-based agents

A

Chooses actions based on a utility function, which weights more than one factor. For example, an autonomous car we want to weight in safety, speed, gas consumption etc.

29
Q

Different representations of the world

A
  1. Atomic (Nodes in a graph)
  2. Factored (variables and combinations of them)
  3. Structured (states and relationships between them)
30
Q

Difference between traditional and declarative problemsolving

A

Traditional: User –> program (how?) –> Computing environment
Declarative: User –> knowledge base (what?) –> Solver (how!) –> Computing environment

31
Q

Workflow for traditional programming

A

Problem –> (Programming) –> Program –> (Solve) –> Output –> (Interpreting) –> Solution

32
Q

Workflow for declarative programming

A

Problem –> (Modeling) –> Representation –> (Solving) –> Output –> (Interpreting) –> Solution

33
Q

What is declarative programming used for? Traditional?

A

It’s used to support in decision making.

34
Q

Logic

A

Formal language for interpreting happenings so conclusions can be drawn.

35
Q

What are the two components of logic?

A

Syntax: Defines the sentences
Semantics: Defines the meaning of the sentences

36
Q

Monotonic logic vs non-monotonic

A

Monotonic: The truth of axioms doesn’t cahnega s new information is added

Non-monotonic: It might change (‘birds typically flies and Birdy is a bird, Birdy presumable flies)

37
Q

Satisfiable formula

A

IFF it’s True for at least one interpretation of the expression

38
Q

Valid formula

A

IFF True for all interpretations

39
Q

Unsatisfiable formula

A

Not True for any interpretation

40
Q

Model

A

every interpretation that satisfies a formula is a model of that formula

41
Q

Truth table

A

Look up online and learn to create….

42
Q

Reasoning

A

Inference - the process of answering a question

43
Q

Entailment

A

One thing follows from another.

44
Q

Formal properties of reasoning

A
  1. Soundness - the initial reason for counting the logic as desirable
  2. Completeness - all true states are provable

Completeness is important in business to manage “business rules”

45
Q

Describe weak AI and the differences to strong AI

A

Focused on small tasks and tries to get good at only that, strong AI is like the human mind, but better, and can do anything a human mind can and more

46
Q

Describe Artificial general intelligence

A

Equally smart as the human mind

Artifical Intelligence (1 decks)

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