Machine Learning in Robotics - Embedded Intelligent Robotics - Lecture Slides, Slides of Robotics

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Artificial

Intelligence and

Machine Learning

in Robotics

We want to design some truly

intelligent robot

What is AI?

Discipline that systematizes and automates

intellectual tasks to create machines that:

Act like humans Act rationally

Think like humans Think rationally

More formal and mathematical

Some Important Achievements of AI

 Logic reasoning (data bases)

 Applied in reasoning interactive robot helper

 Search and game playing

 Applied in robot motion planning

 Knowledge-based systems

 Applied in robots that use knowledge like internet robots

 Bayesian networks (diagnosis)

 Machine learning and data mining

 Planning and military logistics

 Autonomous robots

ARTIFICIAL INTELLIGENCE

Machine Learning

Decision Theoretic Techniques Symbolic Concept Acquisition Constructive Induction Genetic learning

Un-supervised leaning Treatment of uncertainty Efficient constraint satisfaction docsity.com

Inductive Learning by

Nearest-Neighbor Classification

• One simple approach to inductive learning is to save each training example as a point in feature space
• Classify a new example by giving it the same classification (+ or - ) as its nearest neighbor in Feature Space. - A variation involves computing a weighted sum of class of a set of neighbors where the weights correspond to distances - Another variation uses the center of class
• The problem with this approach is that it doesn't necessarily generalize well if the examples are not well "clustered."

Rule and Decision Tree Learning

• Example: Rule Acquisition from Historical Data
• Data
  • Customer 103 (visit = 1): Age 23, Previous-Purchase: no, Marital-Status: single, Children: none, Annual-Income: 20000, Purchase-Interests: unknown , Store-Credit-Card: no, Homeowner: unknown
  • Customer 103 (visit = 2): Age 23, Previous-Purchase: no, Marital-Status: married, Children: none, Annual-Income: 20000: Purchase-Interests: car, Store-Credit-Card: yes, Homeowner: no
  • Customer 103 (visit = n): Age 24, Previous-Purchase: yes, Marital-Status: married, Children: yes, Annual-Income: 75000 , Purchase-Interests: television, Store-Credit-Card: yes, Homeowner: no, Computer-Sales-Target: YES
• Learned Rule
  • IF customer has made a previous purchase , AND customer has an annual income over $25000 , AND customer is interested in buying home electronics THEN probability of computer sale is 0.
  • Training set: 26/41 = 0.634, test set: 12/20 = 0.
  • Typical application: target marketing

example

INDUCTIVE LEARNING Example of Risk Classification NO. RISK CREDIT DEBT COLLATERAL INCOME

1. High Bad High none $0-$15 K
2. High Unk. High none $15-$35 K
3. Mod. Unk. Low none $15-$35 K
4. High Unk. Low none $0-$15 K
5. Low Unk. Low none >$35 K
6. Low Unk. Low none >$35 K Decision variable (output) is RISK

example

The Block’s world

example

Hand-Coded Knowledge vs. Machine Learning

• How much work would it be to enter knowledge by hand?
• Do we even know what to enter? 1952 - 62 Samuel’s checkers player learned its evaluation function 1975 Winston’s system learned structural descriptions from examples and near misses 1984 Probably Approximately Correct learning offers a theoretical foundation mid 80’s The rise of neural networks

Concept Acquisition (cont)

Brick

Pyramid

Polygon

Bricks and pyramids

are instances of

Polygon

ARCH => Two bricks

support

a polygon

Some Fundamental Issues

for Most AI Problems

• Learning new knowledge is acquired - inductive inference - neural networks - artificial life - evolutionary approaches