Introduction to Artificial Intelligence

Question 1

Thinking humanly

Answer 1

Cognitive science approach to make humans think humanly

Question 2

Thinking rationally

Answer 2

Rule-based thinking using algorithmic thinking

Question 3

Acting humanly

Answer 3

Passing the Turing test

Question 4

Definition of intelligence

Answer 4

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.

Question 5

Reductionism

Answer 5

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

Question 6

Holism

Answer 6

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.

Question 7

Analytical approach

Answer 7

• Reductionistic approach
• Primary method for empirical analysis

1. Hypothesis
2. Test
3. Analyze results

Question 8

Synthetic approach

Answer 8

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.

Question 9

Microworlds

Answer 9

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

Question 10

Agents

Answer 10

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

Question 11

Percept sequence

Answer 11

Everything an agent has ever perceived.

Question 12

Percept

Answer 12

What an agent perceives atm

Question 13

Rational agent

Answer 13

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

Question 14

What is the Task environment?

Answer 14

PEAS (Performance, Environment, Actuators, Sensors)

Question 15

Types of Task environments

Answer 15

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

Question 16

Differences between fully and partly observable environment

Answer 16

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

Question 17

Differences between static and dynamic environment

Answer 17

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.

Question 18

Discrete vs continuous environment

Answer 18

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.

Question 19

Episodic vs sequential environment

Answer 19

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)

Question 20

Deterministic vs Stochastic environment

Answer 20

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.

Question 21

Single vs multiagent environments

Answer 21

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.

Question 22

Different types of multiagent environments

Answer 22

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

Competitive: All agents cannot be bettered simultaneously.

Question 23

Known vs unknown environment

Answer 23

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

Question 24

All agent types

Answer 24

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

Question 25

Simple agent and Model-based agent

Answer 25

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-

Question 26

Reactive agents

Answer 26

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

Question 27

Goal-based agents

Answer 27

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.

Question 28

Utility-based agents

Answer 28

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.

Question 29

Different representations of the world

Answer 29

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

Question 30

Difference between traditional and declarative problemsolving

Answer 30

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

Question 31

Workflow for traditional programming

Answer 31

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

Question 32

Workflow for declarative programming

Answer 32

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

Question 33

What is declarative programming used for? Traditional?

Answer 33

It's used to support in decision making.

Question 34

Logic

Answer 34

Formal language for interpreting happenings so conclusions can be drawn.

Question 35

What are the two components of logic?

Answer 35

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

Question 36

Monotonic logic vs non-monotonic

Answer 36

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)

Question 37

Satisfiable formula

Answer 37

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

Question 38

Valid formula

Answer 38

IFF True for all interpretations

Question 39

Unsatisfiable formula

Answer 39

Not True for any interpretation

Question 40

Model

Answer 40

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

Question 41

Truth table

Answer 41

Look up online and learn to create....

Question 42

Reasoning

Answer 42

Inference - the process of answering a question

Question 43

Entailment

Answer 43

One thing follows from another.

Question 44

Formal properties of reasoning

Answer 44

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"

Question 45

Describe weak AI and the differences to strong AI

Answer 45

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

Question 46

Describe Artificial general intelligence

Answer 46

Equally smart as the human mind