Grundlagen der Künstlichen Intelligenz at TU München

Flashcards and summaries for Grundlagen der Künstlichen Intelligenz at the TU München

Arrow Arrow

It’s completely free

studysmarter schule studium
d

4.5 /5

studysmarter schule studium
d

4.8 /5

studysmarter schule studium
d

4.5 /5

studysmarter schule studium
d

4.8 /5

Study with flashcards and summaries for the course Grundlagen der Künstlichen Intelligenz at the TU München

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

disadvantages of A* and alternatives

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

what is an effective branching factor b*?

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

What is the difference between assertions and queries in First-Order Logic

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

performance of A* search

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

what is informed search?

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

how is choice of next node made?

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

what is heuristics?

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

idea of greedy best-first search?

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

performance of greedy best-first search?

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

what is the idea behind A* search?

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

Will A* expand nodes, where f(n)>= C*( the optimal path cost)?

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

What is the constraint of the heuristic function for informed search?

Your peers in the course Grundlagen der Künstlichen Intelligenz at the TU München create and share summaries, flashcards, study plans and other learning materials with the intelligent StudySmarter learning app.

Get started now!

Flashcard Flashcard

Exemplary flashcards for Grundlagen der Künstlichen Intelligenz at the TU München on StudySmarter:

Grundlagen der Künstlichen Intelligenz

disadvantages of A* and alternatives
  • huge space consumption

alternatives:

  • iterative-deepening A*: f cost is used for cutoff
  • recursive best-first search
  • memory-bounded A*

Grundlagen der Künstlichen Intelligenz

what is an effective branching factor b*?
  • way of characterizing the quality of a heuristic
  • indicator: if its small, the heuristic is good
  • independent of problem size

Grundlagen der Künstlichen Intelligenz

What is the difference between assertions and queries in First-Order Logic

Sentences added to knowledge base using TELL = Assertions

Ask questions of knowledge base = queries

Grundlagen der Künstlichen Intelligenz

performance of A* search
  • we need relativ error epsilon = (h*-h)/h*, h is estimated, h* is actual cost from root to goal
  • completeness: yes, if costs are greater than 0
  • optimality: yes (if cost are positive), heuristic admissible for tree-search, consistent for graph-search
  • time complexity: O(b^(epsilon*d))
  • space complexity: identical to time

Grundlagen der Künstlichen Intelligenz

what is informed search?
  • requires problem-specific knowledge
  • finds solutions more efficiently than uninformed search
  • uses indications whether a state is more promising than another to reach a goal

Grundlagen der Künstlichen Intelligenz

how is choice of next node made?
  • based on evaluation function f(n) which is based on heuristic function h(n)
  • h(n) is problem specific, non-negative and h(goalnode) = 0

Grundlagen der Künstlichen Intelligenz

what is heuristics?

art of achieving good solutions with limited knowledge and time based on experience

Grundlagen der Künstlichen Intelligenz

idea of greedy best-first search?

expands nodes that is closest to the goal by using just the heuristic function so that f(n)=h(n)

Grundlagen der Künstlichen Intelligenz

performance of greedy best-first search?
  • completeness: yes, but only if graph search is used
  • optimality: no
  • time complexity: O(b^m)
  • space complexity: O(b^m)

Grundlagen der Künstlichen Intelligenz

what is the idea behind A* search?
  • combines path cost g(n) and estimated cost to goal h(n):     f(n)=g(n)+h(n)
  • h(n) has to be admissible, so its an underestimation
  • f(n) never overestimates the cost to goal so algorithm searches paths that might have a lower cost

Grundlagen der Künstlichen Intelligenz

Will A* expand nodes, where f(n)>= C*( the optimal path cost)?

no

Grundlagen der Künstlichen Intelligenz

What is the constraint of the heuristic function for informed search?

h(n*) = 0, if n* is a goal node

Sign up for free to see all flashcards and summaries for Grundlagen der Künstlichen Intelligenz at the TU München

Singup Image Singup Image
Wave

Other courses from your degree program

For your degree program Computer Science at the TU München there are already many courses on StudySmarter, waiting for you to join them. Get access to flashcards, summaries, and much more.

Back to TU München overview page

Blockchain

Cognitive System

Databases for modern CPU

Protein Prediction I

Data Analysis in R

18WS_Strategisches_IT_Management

Patterns

Softwaretechnik

Autonomous Driving

Requirements Engineering

Web Application Engineering

Business Analytics

Echtzeitsysteme

Patterns in Software Engineering

Principles of Economics

Data Mining and Knowledge Discovery

Introduction to Deep Learning

Data Mining and KD

Visual data analytics

Security Engineering

SE betr Anw

Algorithmic Game Theory

Virtual Machines

SEBA Master

What is StudySmarter?

What is StudySmarter?

StudySmarter is an intelligent learning tool for students. With StudySmarter you can easily and efficiently create flashcards, summaries, mind maps, study plans and more. Create your own flashcards e.g. for Grundlagen der Künstlichen Intelligenz at the TU München or access thousands of learning materials created by your fellow students. Whether at your own university or at other universities. Hundreds of thousands of students use StudySmarter to efficiently prepare for their exams. Available on the Web, Android & iOS. It’s completely free.

Awards

Best EdTech Startup in Europe

Awards
Awards

EUROPEAN YOUTH AWARD IN SMART LEARNING

Awards
Awards

BEST EDTECH STARTUP IN GERMANY

Awards
Awards

Best EdTech Startup in Europe

Awards
Awards

EUROPEAN YOUTH AWARD IN SMART LEARNING

Awards
Awards

BEST EDTECH STARTUP IN GERMANY

Awards

How it works

Top-Image

Get a learning plan

Prepare for all of your exams in time. StudySmarter creates your individual learning plan, tailored to your study type and preferences.

Top-Image

Create flashcards

Create flashcards within seconds with the help of efficient screenshot and marking features. Maximize your comprehension with our intelligent StudySmarter Trainer.

Top-Image

Create summaries

Highlight the most important passages in your learning materials and StudySmarter will create a summary for you. No additional effort required.

Top-Image

Study alone or in a group

StudySmarter automatically finds you a study group. Share flashcards and summaries with your fellow students and get answers to your questions.

Top-Image

Statistics and feedback

Always keep track of your study progress. StudySmarter shows you exactly what you have achieved and what you need to review to achieve your dream grades.

1

Learning Plan

2

Flashcards

3

Summaries

4

Teamwork

5

Feedback