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DIJKSTRA’s ALGORITHM DIJKSTRA’s ALGORITHM

DIJKSTRA’s ALGORITHM - PowerPoint Presentation

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DIJKSTRA’s ALGORITHM - PPT Presentation

It is a directed weighted graph Dijkstras algorithm is an algorithm for finding the shortest paths between nodes in a  graph which may represent for example road networks It was conceived by computer scientist  ID: 793065

experience learning direct access learning experience access direct search reinforcement disadvantage indirect supervised training environment data shortest algorithm performance

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Presentation Transcript

Slide1

DIJKSTRA’s ALGORITHM

It is a

directed

weighted graph

Dijkstra's

algorithm is an algorithm for

finding the shortest paths between nodes in a 

graph

,

which may represent, for example, road networks. It was conceived by computer scientist 

Edsger

W.

Dijkstra

 in 1956 and published three years later.

Slide2

The major disadvantage of the algorithm is the fact that it does a blind

search.

consume

a lot of time waste of necessary resources.

Another

disadvantage is that it cannot handle negative edges. This leads to acyclic graphs and most often cannot obtain the right shortest path.

Slide3

How it work’s

Slide4

LEARNING AGENT’s

[An agent] is said to learn from experience

E

with respect to some class of tasks

T

and performance measure

P

, if its performance at tasks in

T

, as measured by

P

, improves with

E

.

Slide5

EXAMPLE’s

DATA MINING

ROOUTE FINDER

TIC-TAC-TOE

Slide6

Slide7

Types of machine learning

How will the system be exposed to its training experience?

Direct or indirect access:

indirect access:

record of past experiences, databases, corpora ∗

direct access:

situated agents → reinforcement learning

Source of feedback (“teacher”)…..?

supervised learning ∗ unsupervised learning ∗ mixed: semi-supervised (“

transductive

”), active learning, ....

Slide8

What is different about

reinforcement learning:

Training experience (data) obtained through direct interaction with the environment Influencing the environment

Goal-driven learning:

Learning of an action policy

Trial and error approach to search:

Exploration and Exploitation