1. CS 2710, ISSP 2610 Foundations of Artificial Intelligence
introduction

2. Welcome!
Syllabus
I wonder if the notes show up here?

3. What is Artificial Intelligence?
4 views on what makes an AI system (next slide; R&N 1.1)
Depends on what matters more in the evaluation
Reasoning vs. Behavior [diagram]
Human performance vs. Rationality
Human model
Rational model

4. 4 Views of AI Think like humans Think rationally Act like humans
Act rationally

5. Acting Like Humans? Turing (1950)
“Can machines think?”  “Can machines behave intelligently?”
Operational test for intelligent behavior: the Imitation Game

6. Player C tries to determine which of A and B are human/computer

7. Turing Test Suggested the major components of AI:
NLP (communicate in language)
Knowledge representation (to store what it knows)
Automated reasoning (use knowledge to answer questions and draw conclusions)
Machine learning (adapt to new circumstances; prediction)
Vision
Robotics (manipulate objects and move around)

8. Turing Test
Not how we evaluate AI methods; Why? [board + next slide]

9. Sample Eliza Dialogue Joseph Weizenbaum ~1965
young woman: Men are all alike.
eliza: In what way?
young woman: They're always bugging us about something specific or other.
eliza: Can you think of a specific example?
young woman: Well, my boyfriend made me come here.
eliza: Your boyfriend made you come here?
young woman: He says I'm depressed much of the time.
eliza: I am sorry to hear you are depressed.
young woman: It's true. I'm unhappy.
eliza: Do you think coming here will help you not to be unhappy?
young woman: I need some help, that much seems certain
.eliza: What would it mean to you if you got some help?
young woman: Perhaps I could learn to get along with my mother
.eliza: Tell me more about your family
.young woman: My mother takes care of me
.eliza: Who else in your family takes care of you?
young woman: My father …

10. Thinking Like Humans?
For this approach, need a way to determine how humans think
Introspection (rejected by researchers as too subjective)
Psychological experiments: predicting (based on cognitive theory) and then testing, behavior of human subjects; matched predictions give evidence for the theory
Brain imaging – observing the brain in action
Cognitive Science:
Express a theory as a computer program; input/output behavior matching human behavior is evidence for the theory
Computer models from AI and experimental techniques from Psychology; also
Neurophysiological evidence incorporated into computational models, e.g. vision

11. Thinking Like Humans
AI and Cognitive Science are now largely distinct research areas [board]

12. Thinking Rationally?

13. The “Laws of thought” approach
Logicist tradition:
Logic: notation and rules of derivation for thoughts
Aristotle: what are correct arguments/thought processes?
Direct line through mathematics, philosophy, to modern AI
Problems:
Not all intelligent behavior is mediated by logical deliberation
It is difficult to express informal knowledge in logic
It is not sufficient:
Need a search process to go down fruitful reasoning paths
logical systems tend to do the wrong thing in the presence of uncertainty
Logic is important in AI; but a pure logicist approach (early AI history) to intelligence is not effective
That leaves us with ….

14. Acting Rationally: Our Basic Framework
Getting computers to do the right thing based on their circumstances and what they know.
Irrational != insane; irrationality is sub-optimal action
Rational != successful; the most rational action may not succeed due to some circumstance beyond our control or due to incomplete knowledge
Make the best choice, given the options
Rational agents [board]

15. 1940­1950: Early days : McCulloch & Pitts: Boolean circuit model of brain 1950: Turing's “Computing Machinery and Intelligence”
1950—70: Excitement: Look, Ma, no hands! 1950s: Early AI programs, including Samuel's checkers program,  Newell & Simon's Logic Theorist, Gelernter's Geometry Engine 1956: Dartmouth meeting: “Artificial Intelligence” adopted 1965: Robinson's complete algorithm for logical reasoning
1970—88: Knowledge­based approaches
1969—79: Early development of knowledge­based systems
1980—88: Expert systems industry booms
1988—93: Expert systems industry busts: “AI Winter”
1988—: Statistical approaches Resurgence of probability, focus on uncertainty General increase in technical depth Agents and learning systems… “AI Spring”?

16. AI applications
AI techniques are used in many common applications; just a sample
Intelligent user interfaces
Search Engines
Spell/grammar checkers
Context sensitive help systems
Medical diagnosis systems
Regulating/Controlling hardware devices and processes (e.g, in automobiles)
Voice/image recognition (more generally, pattern recognition)
Scheduling systems (airlines, hotels, manufacturing)
Error detection/correction in electronic communication
Program verification / compiler and programming language design
Web search engines / Web spiders
Web personalization and Recommender systems (collaborative/content filtering)
Personal agents
Customer relationship management
Credit card verification in e-commerce / fraud detection
Data mining and knowledge discovery in databases
Computer games

17. What to expect Abstractive thinking/imagination sometimes needed
Extreme range of problem domains (as we just saw on the sample of applications). We need to look for frameworks that apply to a hugely diverse range of problem domains. Abstract distinctions abound.
Real problem domains are often so complex we need to work with simpler ones, and imagine what would be needed in a realistic domain
Not a definitive answer about which method is best; depends on the problem!
AI problems are those that we really don’t know how to solve. Otherwise, we would use a direct solution (and it would not be considered AI anymore)
Real AI systems are often mixtures of various algorithms/techniques, experimentally determined

18. Course Topics Four major areas: Problem solving and search.
Formulating a search problem, uninformed and informed search; constraint satisfaction, optimization, and adversarial search.
Logic and knowledge representation
First-order logic; reasoning; knowledge representation schemes
Planning
Situation calculus, STRIPS, Partial-order planning, GraphPlan and SAT planners
Uncertainty and Learning
Modeling uncertainty, Bayesian belief networks, decision theory, classification, density estimation

19. Wrap Up Chapter 1: Chapter 2:
You will not be tested on Sections 1.2 and 1.3 (history; foundations). But it’s interesting!
Be able to explain the different possible approaches to AI and why AI has settled on the rational action approach
Chapter 2:
Will be covered on homework 1; Any explicit exam question will be related to its coverage on homework 1