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CS 2710, ISSP 2610
Foundations of Artificial Intelligence
• Syllabus
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 Views of AI
Think like humans
Think rationally
Act like humans
Act rationally
Acting Like Humans?
• Turing (1950)
• “Can machines think?”  “Can machines behave
• Operational test for intelligent behavior: the Imitation
Player C tries to determine which of A and B
are human/computer
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)
Turing Test
• Not how we evaluate AI methods; Why? [board + next
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
Thinking Like Humans?
• For this approach, need a way to determine how humans
– 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
Thinking Like Humans
• AI and Cognitive Science are now largely distinct research
areas [board]
Thinking Rationally?
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
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 ….
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]
19401950: Early days
1943: 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, N
ewell & 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”?
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
Personal agents
Customer relationship management
Credit card verification in e-commerce / fraud detection
Data mining and knowledge discovery in databases
Computer games
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
Real problem domains are often so complex we need to work with
simpler ones, and imagine what would be needed in a realistic
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
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
Wrap Up
• Chapter 1:
– 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