ECE 4524 Arti cial Intelligence and Engineering...
Transcript of ECE 4524 Arti cial Intelligence and Engineering...
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ECE 4524 Artificial Intelligence and EngineeringApplications
Spring 2018 – CRN 12983TR 3:30-4:45 in Randolph 220https://filebox.ece.vt.edu/~ECE4524
Today’s Schedule:
I What is AI?
I Introduce the Bounded Rational Agent
I Course objectives and content
I Course administration
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What is AI?
ar-ti-fi-cial adjectivehumanly contrived often on a natural model : MAN-MADE
in-tel-li-gence nounthe ability to learn or understand or to deal with new or tryingsituations : REASON; also : the skilled use of reason (2) : theability to apply knowledge to manipulate one’s environment or tothink abstractly as measured by objective criteria (as tests)
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Human intelligence resides in the mind, the product ofelectro-chemical activity in our brain.
Some statistics:
I 3 lbs
I 100 billion neurons
I 100 trillion connections(synapses)
1
Further reading:
I Pinker, ”How the Mind Works”
I Marcus, The Birth of the Mind: How a Tiny Number of GenesCreates the Complexities of Human Thought.
1Image by Patrick Lynch, CC BY 2.5
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What kind of intelligence are we talking about
Howard Gardner, 1983 ”Frames of Mind: The Theory of MultipleIntelligences”
I musical
I visual
I verbal
I logical
I bodily
I interpersonal
I intrapersonal
I naturalistic
I moral?
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How did we acquire it?
30,000 to 50,000 years ago humansbecame behaviorally modern. There aremany theories about what drove thischange. Two examples are:
I Social brain hypothesis
I Sexual selection
Ridley, 2003
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Example milestones in human development
I 2 yo - recognize a wide variety of objects
I 4 yo - astounding language comprehension and production
I 6 yo - enough dexterity to tie shoes
I 8 yo - social skills develop, emotional intelligence
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Some definitions of AI
Rich and Knight - An attempt to program computers to do thingsthat people can do better
Winston - A study of the computations that make it possible toperceive, reason, and act
Our working definition will be: an attempt to programcomputers to do things that would be said to requireintelligence if people did them.
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Often the simplest tasks for humans are incredibly difficultin AI.
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Often the simplest tasks for humans are incredibly difficultin AI.
Bill and Mary went out to a restaurant. After being seated theyordered from the menu. Later, they paid the check and went home.
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But, often the simplest tasks for computers are difficult forhumans.
After I read the following, try to recall it:
I 1 5 7 4 0 2 4 8 5 1
I 5 4 0 2 3 1 6 6 5 8
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But, often the simplest tasks for computers are difficult forhumans.
After I read the following, try to recall it:
I 1 5 7 4 0 2 4 8 5 1
I 5 4 0 2 3 1 6 6 5 8
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But, often the simplest tasks for computers are difficult forhumans.
After I read the following, try to recall it:
I 1 5 7 4 0 2 4 8 5 1
I 5 4 0 2 3 1 6 6 5 8
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And, humans often make mistakes
Partial list of human biases and cognitive errors
I Confirmation bias
I Framing
I Hindsight bias
I Availability Heuristic
I Anchoring
I IKEA effect
I on and on and on
http://en.wikipedia.org/wiki/List_of_cognitive_biases
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The rotating snake illusion (Akiyoshi Kitaoka)
http://www.ritsumei.ac.jp/~akitaoka/rotsnake.gif
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This is one of my favorites.
Margaret Thatcher (Peter Thompson, 1980)
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This is one of my favorites.
Margaret Thatcher (Peter Thompson, 1980)
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Course Topics
I State-space search, solve complex problems by defining astate, transitions between states, and search for a solution
I Knowledge representation and reasoning, represent facts anduse automated reasoning to arrive at conclusions
I Dealing with uncertainty and inference, uncertainty asprobability and statistical inference
I How to make systems learn, create models with parametersthat can be found given examples
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The Rational Agent
The course is centered on the concept of a rational agent.
I Agents perceive their environment through sensors and acton the environment using actuators.
I A rational agent is one that makes decisions to optimize itsperformance given the percept sequence and any priorknowledge.
This tuple of descriptions, Performance, Environment, Actuators,Sensors, denoted PEAS characterizes a system.
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PEAS == EASP
I Environment: the aspects of the environment the agentinteracts withobservable? deterministic/stochasticepisodic/sequential static/dynamicdiscrete/continuous single/multiple agent
I Actuators: what actions can the agent take, affects theenvironment
I Sensors: what percepts can the agent acquire
I Performance: embodies the criterion for success of an agentsbehavior as seen by its effect on the environment
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There are a variety of basic agent architectures.
I Simple Reflex
I Model-based
I Goal-based
I Utility-based
I Learning
Real systems usually end up as a hybrid among these.
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Bounded Rational Agents
Rational does not mean omniscient. Agents are bounded by:
I prior knowledge - what information has been provided by thedesigner
I time/energy - how much time (or energy) is available forexploration, learning, and decisions (actions to be taken).
A rational agent takes the action that maximizes performancegiven resource constraints, e.g. the information and time available(Herbert Simon).
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PEAS as a programming framework
A Problem is:
I An Environment
I An Agent with Sensors and Actuators
I A Performance measure
Consider a general programming interface that takes a problemdescription and simulates the agent.
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Demos
I 8-Puzzle
I First-Order Logic
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Course Schedule
I State-space search (1/23 - 2/8)
I Problem Set 1, Quiz 1
I Knowledge representation and reasoning (2/13 - 3/13)
I Problem Set 2, Quiz 2
I Dealing with uncertainty and inference (3/15 - 4/3)
I Problem Set 3, Quiz 3
I How to make systems learn (4/5 - 4/24)
I Problem Set 4, Quiz 4
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Prerequisites
I ECE 2574 for knowledge of data structures and basicalgorithms, complexity theory, and programming ability. Iexpect you to be able to read, understand, and write programsof a substantial nature independently.
I Probability (STAT 4714) is needed for the uncertainty andlearning portions of the course.
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Software
We will be using Python (2.7) forprogram implementations.
I Python is a dynamic, strongly typed language with clear,readable syntax
I high level dynamic data types
I extensive standard libraries and third party modules forvirtually every task
I has a REPL for fast experimentation
See http://www.python.org/ for pointers to documentation.Other resource links are available on the course website.
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Texts and Resources
(AIAMA) Stuart J. Russell, PeterNorvig, Artificial Intelligence: AModern Approach, Prentice Hall;2nd or 3rd edition
The books website is also very helpful with code, demo links, etc.http://aima.cs.berkeley.edu/
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Course Activities
I Readings - Each meeting has a section of the text, see thereading guides to help you
I Warmups - due before (usually) every lecture, test basicunderstanding of assigned reading
I Lectures - I try to make these as interactive and interesting aspossible
I Problem Sets - core of your learning, consists mostly of bothexercises and engineering design problems (programming)
I Quizes
Note the course is four credit hours.
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Grading
The grades will be computed as follows:Warmups: 5% (Extra Credit)Problem Sets: 68% (15% each + PS0)Quizes 32% (8% each)
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Collaboration and Late Policy
I All assignments must be turned in via Canvas by due date andtime
I No late assignments will be accepted, with the followingexception: you get three free late days (24 hour periods)during the semester to accommodate emergencies. To use oneor more of these, just submit the assignment via Canvas asnormal.
I All graded work is expected to be the original work of theindividual student unless otherwise directed.
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Communication
Course Website: https://filebox.ece.vt.edu/~ECE4524
I syllabus, schedule, notes, etc.
I primary way materials are distributed.
Piazza: https://piazza.com/
I forum/wiki like software for QA, polls, announcements
I replaces email listserv, but has a configurable email digest
I good mobile apps
I use it to ask (and answer) questions
Canvas: https://vt.instructure.com
I take warmups
I submit problem set solutions
I grades posted
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Course Goals
At the end of the course you will be able to ...
I formulate a problem as state space search and write aprogram to solve it
I capture knowledge using logic-based grammar an writeprograms that can automatically reason
I write programs that can handle uncertainty
I write programs that can learn from experience
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Questions ?
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Next Actions
I Start PS0 - this is largely to get started with python
I Read through the Python Tutorial
I Take warmup before noon on Thursday 1/18.