1. Artificial Intelligence: Definition “... the branch of computer science that is concerned with the automation of intelligent behavior.” (Luger, 2009) “The science and engineering of making intelligent machines” (McCarthy, 2007) “The art of creating machines that perform functions that require intelligence when performed by people” (Kurzweil, 1990)

2. Artificial Intelligence: Definition What is Intelligence? Is intelligence monolithic or diverse? Is there a range of intelligences? Must one be human to be intelligence? What is artificial? Computers? Simulations? Is there a difference between thinking intelligently and acting intelligently?

3. Acting Humanly The Turing Test: A human judge converses with a human and a machine that pretends to be human in natural language.

4. Thinking Humanly The machine thinks in the same way as a human, passes psychological tests. Must it have the same sensory capability? Does this require simulation of the brain? Should the machine have the same limitations as a human? Cognitive Science, Neural Net Simulations

5. Acting Rationally Rational Agent: Interacts with environment Has goal or goals to achieve Measured against optimal results (infinite computational ability, omniscience)

6. Thinking Rationally Formal reasoning Logic: Proposition, Predicate, Non-monotonic, Temporal Mathematical Deduction Computational Limitations Focus on Reasoning, not Knowledge

7. Early Work Focused on rules, game-playing, heuristics Game Playing: Checkers GPS (General Problem Solver) SHRDLU (Block world) Perceptrons Resolution SIR (Question Answering) LADDER (Natural Language front-end for DBs)

8. Paradigm Shift “Knowledge is power” Expert Systems Incorporate knowledge from domain experts Knowledge base more important, deduction engine less important Introduce and measure uncertainty

9. Key Areas Deduction Search Knowledge Representation Perception Planning Learning Natural Language Robotics

10. Approaches Symbolist Logic Rule-Based, Case-Based Sub-Symbolist Neural Nets Cognitive Simulation Stochastic Bayesian Belief Networks Markov Chain Monte Carlo

11. Philosophical Issues Can only humans think? Asking if machines can think is like asking if submarines can swim (Minsky) If computers can only following their programming, how can they be creative? Must machines think like humans? Ethic questions

12. Propositional Calculus Propositions are statements that must be true or false - “It is raining” “George W. Bush is President” Sufficient context is assumed to make statements unambiguous (now, of the US...) Propositions are represented by letters, P, Q, R, S... May be combine by Boolean operators to make more complex statements (formulas)

13. Boolean Operators ¬ Negation, not ⋀ Conjunction, and ⋁ Disjunction, or → Implication, if then ↔ Double implication, if and only if ⊗ Exclusive Or Negation is a unary operation, all others are binary.

14. Propositional Calculus - Syntax Proposition symbols: P, Q, R, S (variables whose values are true or false) Truth symbols: true, false Well-formed formula (WFF): A proposition symbol, truth value, or ( ¬ formula), or (formula op formula) where op is one of ⋀, ⋁, →, ↔, ⊗. These are the only formulas.

15. Propositional Calculus - Syntax Order of precedence: The operators have different levels of precedence with negation binding more tightly, and exclusive or least tightly (in the order given on a previous slide). We only use parentheses to change the normal order of precedence.

16. Propositional Calculus - Semantics An interpretation assigns a truth value (T or F) to each propositional symbol in a formula. Formulas are evaluated recursively: A propositional symbol's value is given by the interpretation A truth symbol's value is T for true and F for false For a complex formula, first evaluation the operand(s) and then apply the operator according to its truth table.

17. Truth Tables