Artificial Intelligence

Lecture 1: Welcome and Introduction

Do You Know

, Graph vs Tree BFS O(n) Implication

Today

What is AI?

Brief History of AI

What is this course?

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

Models of Intelligence

Logic Models Formal logic Fuzzy logic Non-monotonic logic

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!

Imaging the Brain

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

Artificial Intelligence

Lecture 2: AI Application Areas, Representation & Search

Today

What can AI do?

Representation

Search

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

Today

What can AI do?

Representation

Search

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

Different representations of the real number π.

Representation of

• 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.

Today

What can AI do?

Representation

Search

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

Portion of the state space for tic-tac-toe.

Tic-tac-toe State Space

State space description of the automotive diagnosis problem.

Auto Diagnosis State Space