Artificial Intelligence Lecture 1: Welcome and Introduction
An Attempted Definition
AI – the branch of computer science that is concerned with the automation of intelligent behavior Sound theoretical and applied principles Data structures for knowledge representation Algorithms of applying knowledge Languages for algorithm implementation
Problem What is Intelligence?
This course discusses The collection of problems and methodologies studied by AI
researchers
Brief Early History of AI
Aristotle – 2000 years ago The nature of world Logics Modus ponens and reasoning system
Copernicus – 1543 Split between human mind and its surroundings
Descrates (1680) Thought and mind Separate mind from physical world Mental process formalized by mathematics
Modern History Formal logic
Leibniz Boole Turing Frege – first-order predicate calculus
Graph theory Euler
State space search
What is AI?
Think like humans Think rationally
Act like humans Act rationally
The science of making machines that:
Acting Like Humans? Turing (1950) ``Computing machinery and intelligence''
``Can machines think?'' ``Can machines behave intelligently?'' Operational test for intelligent behavior: the Imitation Game
Predicted by 2000, a 30% chance of fooling a lay person for 5 minutes Anticipated all major arguments against AI in following 50 years Suggested major components of AI: knowledge, reasoning, language
understanding, learning Problem: Turing test is not reproducible or amenable to
mathematical analysis
Thinking Like Humans? The Cognitive Science approach:
1960s ``cognitive revolution'': information-processing psychology replaced prevailing orthodoxy of behaviorism
Scientific theories of internal activities of the brain Cognitive science: Predicting and testing behavior of
human subjects (top-down) Cognitive neuroscience: Direct identification from
neurological data (bottom-up) Both approaches now distinct from AI Both share with AI the following characteristic: The available theories do not explain (or engender)
anything resembling human-level general intelligence}
Hence, all three fields share one principal direction!
Neural Basis of Intelligence
How does a system of neurons with specific processes, connectivity, and functions support the ability to think, reason, and communicate?
Brains ~ Computers
1000 operations/sec 100,000,000,000
units stochastic fault tolerant evolves, learns
1,000,000,000 ops/sec
1-100 processors deterministic crashes designed,
programmed
What is AI?
Think like humans Think rationally
Act like humans Act rationally
The science of making machines that:
Thinking Rationally? The “Laws of Thought” approach
What does it mean to “think rationally”?
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: Representing informal knowledge in the formal terms required by logical
notations Being able to solve a problem “in principle” and doing so in practice
Acting Rationally Rational behavior: doing the “right thing”
The right thing: that which is expected to maximize goal achievement, given the available information
Doesn't necessarily involve thinking, e.g., blinking Thinking can be in the service of rational action Entirely dependent on goals! Irrational ≠ insane, irrationality is sub-optimal action Rational ≠ successful
Our focus here: rational agents Systems which make the best possible decisions given goals,
evidence, and constraints In the real world, usually lots of uncertainty
… and lots of complexity Usually, we’re just approximating rationality
“Computational rationality” a better title for this course
AI Research and Application Areas
Game Playing Automated Reasoning and Theorem Proving Expert Systems Natural Language Understanding and Semantic
Modelling Modelling Human Performance Planning and Robotics Languages and Environments for AI Machine Learning Alternative Representation: Neural Nets AI and Philosophy
Representation Systems What is it?
Capture the essential features of a problem domain and make that information accessible to a problem-solving procedure
Measures Abstraction – how to manage complexity Expressiveness – what can be represented Efficiency – how is it used to solve problems
Trade-off between efficiency and expressiveness
• Array representation – 2D
• Image representation -- Digitized image of chromosomes in metaphase.
Image Representation
Logical Clauses describing some important properties and relationships
General rule
A blocks world
Block World Representation
Logical predicates representing a simple description of a bluebird.
Bluebird Representations
Semantic network description of a bluebird.
State Space Search
State space State – any current representation of a problem State space
All possible state of the problem Start states – the initial state of the problem Target states – the final states of the problem that has been solved
State space graph Nodes – possible states Links – actions that change the problem from one state to another
State space search Find a path from an initial state to a target state in the state space Various search strategies
Exhaustive search – guarantee that the path will be found if it exists Depth-first Breath-first
Best-first search heuristics