CI at Universität Potsdam

Flashcards and summaries for CI at the Universität Potsdam

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Artificial Intelligence

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Agent

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Inputs to an agent

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Dimensions of
Complexity

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Interaction of
Dimensions

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Common Tasks
of Domains

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Classes of
solution

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Levels of
abstraction

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Knowledge Base

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Directed Graph

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Generic Search
Algorithm

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Complexity of
Searches

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Exemplary flashcards for CI at the Universität Potsdam on StudySmarter:

CI

Artificial Intelligence
The synthesis and analysis
of computational agents
that act intelligently.

Scientific goal:
- understand the principles that
   make intelligent behavior possible
   in natural or artificial systems.

Engineering goal:
- design useful, intelligent artifacts.

-> much work is motivated by
     common-sense resoning.

CI

Agent
Something that acts in
an environment.

Acts intelligently if:
- actions are appropriate for
   its goals and circumstances.
- flexible to changing environments
   and goals.
- learns from experience.
- appropriate choices given
   perceptual and computational
   limitations.

CI

Inputs to an agent
Abilities:
- the set of things it can do.
Goals/Preferences:
- what it wants, desires, its values . . .
Prior Knowledge:
- what it comes into being knowing.
- what it doesn‘t get from experience.
History:
- observations of the environment.
-> current vs prior observations.

CI

Dimensions of
Complexity
Modularity:
Flat or Modular or Hierarchial.
Succinctness and Expressiveness:
Explicit states or Features or
Individuals and Relations.
Planning Horizon:
Static or Finite Stage or
Indefinite Stage or Infinite Stage.
Sensing Uncertainty:
Fully Observable or Partially Observable.
Uncertain dynamics:
Deterministic or Stochastic Dynamics.
Goal or complex preferences:
Goals or Complex Preferences.
Single agent or multiple agents:
Single-Agent or Multiple Agents.
Learning from experience:
Knowledge is given or
Knowledge is learned from Experience.
Perfect or bounded rationality:
Perfect Rationality or
Bounded Rationality.

CI

Interaction of
Dimensions
Multiple dimensions interact.
-> things are getting more complex.

Three values promising simpler
reasoning for agents:
- Hierarchical reasoning.
- Individuals and relations.
- Bounded rationality.

CI

Common Tasks
of Domains
- Modeling the environment.

- Evidential resoning or perception.

- Action.

- Learning from past experiences.

CI

Classes of
solution
- Optimal Solution

- Satisficing Solution

- Approximately Optimal Solution

- Probable Solution

CI

Levels of
abstraction
- Knowledge Level
-> about the external world.
- Symbol Level
-> about the implementation of
     the Knowledge Level.

CI

Knowledge Base
Background knowledge
and data.

CI

Directed Graph
- set N of nodes.
- set A of arcs -> e.g. <n, m>
- a path is a sequence of nodes.

- a solution is a path from the
   startnode(s) to the goal-node(s).

CI

Generic Search
Algorithm
Maintain a frontier of
explored nodes.

Search strategy is the way
in which this frontier expands.
-> which value is selected from
     the frontier at each stage.

CI

Complexity of
Searches
- guarantee to find the shortest
   path with fewest arcs?

- special cases on graphs with
   cycles or infinite nodes?

- time-complexity of selected path?

- space-complexity of selected path?

- how does the goal affect the search?

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2er Potenzen

Mengenlehre

Prozedurale Programmierung

Funktionale Programmierung

Mathe1 Nr1

Assembler, Interpreter/Compiler

Paradigmen

Einführung in C

C-Funk.-Fehler-Pointer

Strings-Eingabe-Ausgabe

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