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

Elements of AI

• Natural Language Processing

• Robotics

• Perceptive Systems (Vision)

• Expert Systems

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

Decision Tree

Depth First Search

Breath First Search

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

Expert System Successes

• XCON - configures systems for DEC

• Prospector - an mining expert

• MYCIN - infectious blood diseases

• EMYCIN - Empty MYCIN

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

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

How a Neuron Works

Impulsescome fromother neurons.

When sum ofinputs reachesa threshold, neuron fires.

Sendingimpulsesto nextlevel ofneurons.

An Artificial Neural Network

Inputs Hidden Output

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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