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ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 1
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 2008Lecture #1
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 3
Outline What is an AI?
Russell & Norvig, chapter 1 Agents Environments
Russell & Norvig, chapter 2
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 4
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
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 5
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
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 6
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
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 7
Think Rationally Use logic to reach a decision or
goal via logical inference Example: Theorem proving Sample approach: First-order logic Problems:
Informal knowledge Uncertainty Search space
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 8
Acting vs. Thinking Rationally Both can lead to the same result
Acting rationally requires rational decision-making
Thinking rationally discovers the most rational action to do
So what’s the difference? Acting rationally can be done without
thinking Thinking rationally can infer new
information
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 9
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
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 10
Think Humanly Cognitive science Neural networks Helps in other fields
Computer vision Natural language processing
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 11
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
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 12
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
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 13
Simple Reflex Agent
EnvironmentPercepts
Actions
Sensors
Actuators
Selected
Action
Current State
If-then Rules
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 14
Simple Reflex Agent Dune II (1992) units were
simple reflex agents Harvester rules:
IF at refinery AND not empty THEN empty
IF at refinery AND empty THEN go harvest
IF harvesting AND not full THEN continue harvesting
IF harvesting AND full THEN go to refinery
IF under attack by infantry THEN squash them
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 15
Model-Based Agent
EnvironmentPercepts
Actions
Sensors
Actuators
Selected
Action
Current State
Previous perceptio
nsImpact of actions
World changes
If-then Rules
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 16
Goal-Based Agent
EnvironmentPercepts
Actions
Sensors
Actuators
Selected
Action
Current State
GoalPrevious perceptio
nsImpact of actions
World changes
State if I do action
X
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 17
Utility-Based Agent
EnvironmentPercepts
Actions
Sensors
Actuators
Selected
Action
Current State
UtilityPrevious perceptio
nsImpact of actions
World changes
State if I do action
X
Happiness in that state
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 18
Learning Agent
EnvironmentPercepts
Actions
Sensors
Actuators
Problem Generat
or
Learning
Element
Feedback
Performance standard
ChangesKnowledge
Learning Goals
Performance Element
Critic
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 19
Properties of the Environment Fully observable vs. partially observable
See everything vs. hidden information Chess vs. Stratego
Deterministic vs. stochastic vs. strategic Controlled by agent vs. randomness vs.
multiagents Sudoku vs. Yahtzee vs. chess
Episodic vs. sequential Independent episodes vs. series of events Face recognition vs. chess
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 20
Properties of the Environment Static vs. dynamic vs. semi-dynamic
World waits for agent vs. world goes on without agent vs. world waits but agent timed
Translation vs. driving vs. chess with timer Discrete vs. continuous
Finite distinct states vs. uninterrupted sequence
Chess vs. driving Single agent vs. cooperative vs.
competitive Alone vs. team-mates vs. opponents Sudoku vs. sport team vs. chess
ECE457 Applied Artificial Intelligence R. Khoury (2008) Page 21
Crossword Puzzle Fully observable, deterministic, sequential,
static, discrete, single-agent Monopoly
Fully observable, stochastic, sequential, static, discrete, competitive multi-agent
Driving a car in the real world Partially observable, stochastic, sequential,
dynamic, continuous, cooperative multi-agent Assembly-line inspection robot
Fully observable, deterministic, episodic, dynamic, continuous, single-agent
Properties of the Environment