artificial intelligence: research and collaborative possibilities a presentation by: dr. ernest l....

Artificial Intelligence: Artificial Intelligence: Research and Collaborative Research and Collaborative

PossibilitiesPossibilitiesa presentation by:

Dr. Ernest L. McDuffie, Assistant ProfessorDepartment of Computer Science, Florida State University

at the:First Annual Africa-America Cooperative Workshop

in Computational Science & EngineeringUniversity of the Western Cape

Cape Town, South Africa21 July 2000

What is Intelligence? A single faculty or just a collection of distinct and unrelated

abilities? Exactly what happens when learning occurs? What is intuition? What is self-awareness? Can intelligence be inferred from observable behavior, or does it

require evidence of a particular internal mechanism? How is knowledge represented in nerve tissue? Is it even possible to achieve intelligence on a computer, or

does an intelligent entity require the richness of sensation and experience that might be found only in a biological existence?

What is AI?

The study of intelligent behavior. The goal being a theory of intelligence

that can account for the behavior of all naturally occurring intelligent entities.

Then use the theory to guide the creation of artificial entities capable of intelligent behavior.

AI Fields Natural Language Processing Game Playing Automatic Theorem Proving Pattern Recognition & Computer Vision Expert Systems Modeling Forms of Reasoning Automatic Learning Robotics

Knowledge We assume that intelligent entities have

knowledge about their environment. What can we say about such knowledge?

What forms can it take? What are its limits? How is it used? How is it acquired?

We are beginning to understand how neurons process simple signals, but how the brain processes and represents knowledge is still not well understood.

How Computers Represent Knowledge

There are two major ways we think of machines having knowledge of the world.

Clarification about the distinction between the two is on going.

They are implicit or procedural knowledge and explicit or declarative knowledge.

Implicit Knowledge In a computer this type of knowledge

takes the form of stored procedures. The knowledge would manifest itself when

the procedure is run. In humans it is often called tacit

knowledge and can be difficult or impossible to describe.

It is difficult to easily modify this type of knowledge in a computer.

Explicit Knowledge

Complex tasks that we usually think of as requiring intelligence tend to use explicit knowledge representations.

A tabular database of salary data would be one example of explicit knowledge.

Particularly useful are explicit representations that can be interpreted as making declarative statements.

Efficiency vs. Flexibility Using declarative knowledge usually is

more costly and slower than is directly applying procedural knowledge.

Declarative knowledge can also be accessed by introspective programs so a machine can then answer questions about what it knows.

Generally, we give up efficiency to gain flexibility and vice versa.

AI Needs Both Procedural and Declarative types of

knowledge. Most flexible kinds of intelligence seem to

depend strongly on declarative knowledge. AI has concerned itself more and more

with this type of knowledge. Procedural knowledge still has a role to

play.

Computer Learning

To assimilate new information or procedures without a programmer writing a new program.

This is different from discovery programs like those designed to formulate new mathematical theorems.

A range of different techniques are used in computer learning programs.

Some Techniques Are:

Induction - learning by generalization from specific examples.

Candidate Elimination - a specific method of induction; testing rules and a method for generating new one.

Genetic Algorithms - finding better and better versions of rules/programs/strings by using random repeated mutations and selection.

Neural Net - a method of training to modify the connections between neurons; back propagation.

Progress Has Been Slow

Learning from experience is difficult in any domain that is not very restricted or has formal contexts.

It seems that even simple animals like flies or slugs have better learning ability.

Studies of these types of animals have been used as background for some neural net approaches.

A New Direction - MIT

Alternative Essences of Intelligence An attempt at building complex machines

with human like capabilities. Four essences - development, social

interaction, physical coupling to the environment, and integration.

Dr. Rodney Brooks, Director AI Lab, MIT.

My Research Temporal Reasoning• Allen Relationships

Automatic Scheduling• Lots of manufacturing applications

Second Generation Hybrid Expert Systems• Combining learning and decision making

Applied AI to real world problems• Network security, intrusions detection

Any Questions?

Any Comments?mcduffie@cs.fsu.edu

Work Phone: 850.644.3861Fax: 850.644.0058

Thank you for your attention!