1.
What is Artificial Intelligence?
• AI is the effort to develop systems that can
behave/act like humans.
• Turing Test
• The problem = unrestricted domains
– human intelligence vastly complex and broad
– associations, metaphors, and analogies
– common sense
– conceptual frameworks

2.
Elements of AI
• Natural Language Processing
• Robotics
• Perceptive Systems (Vision)
• Expert Systems

3.
How are Machines Intelligent?
• Constrained Heuristic Search
– How do you play chess?
• first move = 20 possible
• second move = 400 possible
• 7th move = 1,280,000,000 possible
– Depth First vs. Breath First Searching
– Ability to Learn

4.
Decision Tree

5.
Depth First Search

6.
Breath First Search

7.
Expert Systems
• Capture knowledge of an expert.
• Represent Knowledge as a
– rule base
• if then rules
– semantic net
• hierarchy
– frames
• shared characteristics, IS-A relationships

8.
Expert System Successes
• XCON - configures systems for DEC
• Prospector - an mining expert
• MYCIN - infectious blood diseases
• EMYCIN - Empty MYCIN

9.
Elements of Expert System Shell
• Knowledge Base
– rules
• Working Memory
– facts of current case
• Inference Engine
– applies rules using current set of facts
• Explanation Facility
• CLIPS

10.
Neural Networks & The Brain
• Base on architecture of human brain
– Neurons connected by axons & dendrites
– 100 billion neurons
– 1,000 dendrites per neuron
– 100,000 billion synapses
– 10 million billion interconnectons per second

11.
How a Neuron Works
Impulses
come from
other neurons.
When sum of
inputs reaches
a threshold,
neuron fires.
Sending
impulses
to next
level of
neurons.

12.
An Artificial Neural Network
Inputs Hidden Output
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13.
Neural Networks, NN
• NNs learn by using a training set and
adjusting the weights on each connection.
• NNs do not have to be “told” explicit
relationship rules.
• NNs can work with partial inputs.
• NNs cannot explain their results.
• NNs can take a long time to train.
• A NN demonstration