Please pick up a copy of the course syllabus from the front desk.

ECE457 Applied Artificial Intelligence R. Khoury (2007)

Please pick up a copy of the course syllabus from the front desk.

http://www.pami.uwaterloo.ca/~khoury/ece457

Introduction to AI

ECE457 Applied Artificial IntelligenceSpring 2007 Lecture #1


Outline What is an AI?

Russell & Norvig, chapter 1 Agents Environments

Russell & Norvig, chapter 2


Artificial Intelligence

Computer players in video games

Robotics Assembly-line robots,

auto-pilot, Mars exploration robots, RoboCup, etc.

Expert systems Medical diagnostics,

business advice, technical help, etc.

Natural language Spam filtering,

translation, document summarization, etc.

Artificial intelligence is all around us


What is an AI? Systems that…

Rationality vs. Humans: emotions, instincts, etc.

Thinking vs. acting: Turing test vs. Searle’s Chinese room

Engineers (and this course) focus mostly on rational systems

Humanly Rationally

Think Neural networks

Theorem proving

Act ELIZA Deep Blue


Act Rationally Perceive the environment, and act so as to

achieve one’s goal Not necessary to do the best action

There’s not always an absolutely best action There’s not always time to find the best action An action that’s good enough can be acceptable

Example: Game playing Sample approach: Tree-searching strategies Problem: Choosing what to do given the

constraints


Think Rationally Uses logic to reach a decision or

goal via logical inferences Example: Theorem proving Sample approach: First-order logic Problems:

Informal knowledge Uncertainty Search space


1. X = Y/Z XZ = Y2. X = Y

X + Z = Y + Z

3. X * Y + X * Z X * (Y + Z)

4. b/c = AH/b5. a/c = BH/a6. AH + BH = c

Think Rationallya. b² = AH * cb. a² = BH * cc. a² + b² =

BH * c + AH * c

d. a² + b² = c * (AH +

BH)e. a² + b² = c²


Act Humanly “Turing-test” AI Improve human-machine

interactions up to human-human level

Drawbacks: In some cases, requires dumbing

down the AI Lots of man-made devices work well

because they don’t imitate nature


Think Humanly Cognitive science Neural networks Helps in other fields

Computer vision Natural language processing


Rational Agents An agent has

Sensors to perceive its environment

Actuators to act upon its environment

A rational agent has an agent program that allows it to do the right action given its precepts

Environment

Perce

pts A

ction

s

Sensors

Actuators

Agent Progra

m


Properties of the Environment Fully observable vs. partially observable

Chess vs. Stratego Deterministic vs. stochastic vs. strategic

Sudoku vs. Yahtzee vs. chess Episodic vs. sequential

Face recognition vs. chess Static vs. dynamic vs. semi-dynamic

Translation vs. driving vs. chess with timer Discrete vs. continuous

Chess vs. driving Single agent vs. cooperative vs.

competitive Sudoku vs. sport team vs. chess


Types of Agents Simple reflex agent

Selects action based only on current perception of the environment

Model-based agent Keeps track of perception history

Goal-based agent Considers what will happen given its actions

Utility-based agent Adds the ability to choose between

conflicting/uncertain goals Learning agent

Adds the ability to learn from its experiences

Please pick up a copy of the course syllabus from the front desk.

Documents

Transcript of Please pick up a copy of the course syllabus from the front desk.