1. Introduction to Artificial Intelligence Instructor: Dr. Eduardo Urbina
CPTR 314
Introduction to
Artificial Intelligence
Instructor: Dr. Eduardo Urbina

2. Artificial Intelligence Definition
AI may be defined as the branch of computer science that is concerned with the automation of intelligent behavior
Artificial Intelligence uses the techniques of Computer Science
Data Structures for knowledge representation
Programming Languages & Techniques
Algorithms

3. What is Intelligence? Does it mean to perform operations fast?
Is it learned?
What is the relationship with intuition?
What is the relationship with creativity?
Can machines be intelligent? If so, Is it morally correct? Can only God create intelligence?

4. Definition
AI is the collection of problems and methodologies studied by artificial intelligence researchers

5. How can you model Intelligence?
Mathematical logic
Main technique so far
Connectionist Networks
De-emphasizes logic and the functioning of the rational mind but concentrates in the architecture of the physical mind

6. How do we model Intelligence?
Using artificial life and genetic algorithms
Applies the principles of biological evolution to the problems of finding solutions to difficult problems
Solutions are are found by analyzing competing solutions; solutions with promise will tend to survive

7. How do we model Intelligence?
Social systems provide another metaphor for intelligence; the basis of this are agents
An agent is an element of a society that can perceive aspects of its environment and affect that environment either directly or through cooperation with other agents

8. Agents Characteristics
Agents are autonomous or semi-autonomous
Each agent has little or no knowledge of what other agents do or how they do things
Agents are sensitive to their own surroundings
Agents are interactional; they cooperate on a particular task
Intelligence is the result of the society as a whole not just a property of an individual agent

9. Overview of AI Most fundamental concerns of AI researchers are
Knowledge representation and search
Machine learning

10. AI is Complex Mimicing human intelligence is hard
Use of symbolic reasoning
Humans use inexact, missing or poorly defined information; they rely on intuition.
Answers may not be exact or optimal but just sufficient to solve a problem

11. AI Sub-disciplines Game Playing
Automated Reasoning and Theorem Proving
Expert Systems
Natural Language
Robotics
Languages and Environments
Machine Learning
Parallel Distributed Processing
Modeling Human Performance

12. AI features
Use of computers to do symbolic reasoning, pattern recognition, learning, or some other form of inference
A focus of problems that do not respond to algorithmic solutions
A concern with problem solving using inexact, missing, or poorly defined information
Provide answers that are neither exact nor optimal, but in some sense “sufficient”