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CS 510: Intro to
Articial IntelligenceRachel GreenstadtDepartment of Computer Science
Drexel Universitywww.cs.drexel.edu/~greenie/cs510/index.html
http://www.cs.drexel.edu/~greeniehttp://www.cs.drexel.edu/~greeniehttp://www.cs.drexel.edu/~greenie8/13/2019 Intoduction to Artificial Intelligence
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Overview
What is Articial Intelligence?
History of AI
What is CS 510? Syllabus, Schedule, Grading Final Project
Overview of AI Topics
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Introductions
Introduce yourself: Your name Undergrad/Masters/Ph.D/How many years at Drexel? What is your research area? Which faculty member(s) do you work with? What brings you to CS 510? What else should we know about you? :)
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What is AI?
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Class Exercise
Answer the following questions on threeindex cards:
What is Intelligence? What is Articial Intelligence? What is an agent? What attributes does an agent have?
When youre done, swap your answers witha neighbor
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A 42
Each card has a number or letter on one side
and a square or circle on the other side
Which cards must you turn over to determine if the
following statement is true:
Every card with a letter on one side has asquare on the other side
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Thinking like humans
90% of humans get it wrong Answer is cards 2 and 3
Most people pick 1 and 3
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20 yrs Beer24 yrs Cola
Each card has an age on one side
and a drink on the other side
Which cards must you turn over to determine if thefollowing statement is true:
Everyone in the bar is following the law.
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What is AI?
Thinking like
a human
Thinking
rationally
Acting like ahuman
Actingrationally
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Why Study AI?
Fundamental scientic questions What does it mean to be smart? What makes us smart?
Can our intelligence be replicated orexceeded? And how?
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Why Study AI?
Fundamentally useful engineering question
AI in computers increases humanityscollective intelligence and abilities
Areas where computers lack the abilityto act rationally limit us
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But is it even possible?
Billions of human computers must be doingsomething....
Strong vs. Weak AI Human-level intelligent machines,
conscious?
thinking-like features to makecomputers more useful
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agent
1. One that acts or has the power or authority to acts
2. One empowered to act for or represent another
13
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Agents
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Simple reex agent
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Modern AI Agents
Not just AI, but AI situated in some environment
Not just inference, but inference used in some context Not just a control loop, but complex autonomous decision-making Not just an algorithm, but an intelligent system
Holistic approach to AI
Multiple AI tools can be integrated to build an Agent
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PEAS
Performance Measure Environment Actuators
Sensors
Examples?
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Autonomous Cars
Consider an automated taxi driver: Performance measure: Safe, fast, comfortable trip, maximize prots Environment: Roads, other trafc, pedestrians, customers Actuators: Steering wheel, accelerator, brake, signal, horn Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors,
keyboard, lidar
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Agent or Program?
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The easy stuff is hard
Computers still cant speak, see, or reasonlike a 5 year old child
And the hard stuff is easy.... Playing chess
Proving theorems Diagnosing medical conditions
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AI Historical Highlights 5th century
Aristotle invents syllogistic logic 13th century
zairja device used by Arab astrologers to calculate ideas mechanically Ramon Llull creates Ars Magna theological argumentation device 17th century
Material arguments for thinking: Hobbes, Descartes Pascal invents mechanical calculating device
19th century Babbage and Lovelace work on programmable mechanical machine Boolean algebra representing some laws of thought
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AI Historical Highlights 1928 von Neumans minimax algorithm, used for game-playing 1950 Turing test devised
1950 Asimov publishes the 3 laws of robotics 1956 McCarthy coins Articial Intelligence / Dartmouth conference Early years (1956-1970)
Micro-worlds Reasoning by search Many successes, lots of optimism/hype - Samuels checkers, Gelemters
Geometry theorem prover, Shakey, Dendral
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AI Historical Highlights AI Winter (1970s)
Perceptrons - limits of neural networks
language difculties - The spirit is willing but the esh is weak ==>The vodka is good but the meat is rotten Development of computational complexity Loss of funding
AI becomes an industry (1980s) Expert, intelligent systems all the rage Bubble happens and expectations raised again
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AI Historical Highlights 2nd AI Winter (late 1980s - 1990s)
More disappointment as AI fails to make people rich Expert systems are brittle
Funding cut again AI Becomes a Science/Intelligent Agents (1987-present) Victory of the neats (vs scrufes) Statistical machine learning/HMMs has many successes
AI starts to make people rich
Moores law makes a lot more possible Emergence of Intelligent Agent approach Availability of very large data sets
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AI State of the Art
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AI in Space
Autonomous satelliteseparation and docking
Exploring Mars Monitoring the skywith telescope arrays
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AI Art
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What is CS 510?
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Cou rse Information Textbook
Stuart Russell and Peter Norvig Articial Intelligence: A Modern Approach
Prentice-Hall ( Third Edition) Supplementary Readings
Available on course website http://www.cs.drexel.edu/~greenie/cs510.html
http://www.cs.drexel.edu/~greenie/cshttp://www.cs.drexel.edu/~greenie/cshttp://www.cs.drexel.edu/~greenie/cs8/13/2019 Intoduction to Artificial Intelligence
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Course Objectives
Learn about AI techniques Learn how to do AI research (grad class)
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Schedule
Intro to AI Search and Problem Solving Planning
Knowledge Representation
Learning
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Evaluation
30% Exams
Midterm 15%
Final 15% 5% Machine Learning exercise 25% Class Participation 40% Final Project
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Class Participation
In class exercises Class discussions bbvista Online discussions
More instructions on website
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Facilitation Each person will be responsible for leading a short
discussion around one topic/paper
Discussion should involve either :
A 5-10 minute presentation/demo on related material Related paper, background, application
A summary/highlights reel of the online discussion
Rest should be class discussion/exercises ledby you
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Topics the ai enterprise (Turing) 09/29 multiagent systems (Sycara)
09/29
search - google (Page, et al) 10/6
search - dynamic environments(Koenig and Likhachev) 10/13
constraint reasoning (RN Ch 6)10/13
games (Billings et al (poker))10/27
game theory (MechanismDesign) 10/27
logic/knowledge representation(RN Ch 7-8, BDI) 11/3
teamwork/joint intention (Cohenand Levesque) 11/3
planning (RN Ch 11) 11/10 distributed planning 11/10 machine learning (general) 11/17 machine learning (adversarial
classication) 11/17
Reinforcement learning 11/24 NLP 11/24
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Final Project
Read Handout! Free form researchproject
Groups of 1-3 people Milestones
Groups and topic (Sept 29)
Proposal draft sent toreviewer (Oct 6)
Reviewer comments due(Oct 8)
Proposal due (Oct 13)
Reviewer discussion notesdue (Nov 10)
Presentation (Dec 1)
Project write up (Dec 1)
Review due (Dec 4)
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The Final Project
Pro osal 2 pages long
Problem Statement and Motivation
Brief Description of Approach Related Work and novelty Evaluation approach Milestones
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Peer Review Each of you will be assigned another group
to shepherd through the project process
Goal is to provide constructive feedback onthe other project At week 3 (project proposal draft) At week 7 (project progress) At week 10 (nal paper)
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AI Topics
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Search The Heuristic Search Hypothesis
- (Newell and Simon)
Subroutine of intelligent systems problem solving
planning
knowledge games
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Some search issues
well discuss
Intractability of exhaustive search Use of heuristics (A*)
Local search satiscing
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Open Problems
Distributed search
Dynamic search Check out STAIRS workshop Last year: A Travel-Time Optimizing Edge Weighting Scheme for Dynamic Re-planning . Andrew
Feit, Lenrik Toval, Raf Hovagimian and Rachel Greenstadt. AAAI 2010 Workshop on Bridging TheGap Between Task And Motion Planning (BTAMP)
http://www.seas.upenn.edu/~maximl/wt/AAAI10_ws/BTAMP10_schedule.html
http://www.seas.upenn.edu/~maximl/wt/AAAI10_ws/BTAMP10_schedule.htmlhttp://www.seas.upenn.edu/~maximl/wt/AAAI10_ws/BTAMP10_schedule.htmlhttp://www.seas.upenn.edu/~maximl/wt/AAAI10_ws/BTAMP10_schedule.html8/13/2019 Intoduction to Artificial Intelligence
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Constraint Reasoning Way of representing knowledge and structure on a
problem so that standard heuristics can be applied
Problems expressed as: Set of variables that need values Set of domains from which the values are drawn
Set of constraints that represent relationshipsbetween the variables (must be satised oroptimized)
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Applications
Supply chain management
Scheduling Resource and task allocation Multiagent coordination
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Open problems in
Constraint Reasonin How to easily express problems as constraint problems
What if the domain is dynamic or uncertain?
How do you measure performance in distributed systems? See the Constraint Programming (CP) conference or the Distributed
Constraint Reasoning (DCR) workshop
I do research in Distributed Constraint Reasoning (as does Evan Sultanik,the other section designer), we can work with you to come up with a topicin this area if you want
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Games /
Adversarial Searc h Inherently multiagent and competitive
Classic work in turn based games
Chess Checkers Now poker, general game playing
http://www.computerpokercompetition.org/ http://games.stanford.edu/
http://www.computerpokercompetition.org/http://games.stanford.edu/http://games.stanford.edu/http://www.computerpokercompetition.org/http://www.computerpokercompetition.org/8/13/2019 Intoduction to Artificial Intelligence
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Mechanism Design Construct incentives for agents that are:
self-interested utility-maximizing
Applications Auctions Reputation systems Trafc systems
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Knowledge
Re resentation What is common sense? How a pr oblem is represented grea tly affects its
efciency
How can we encode the things we know socomputers understand them?
Links
http://openmind.media.mit.edu/ htt ://rtw.ml.cmu.edu/rtw/
http://openmind.media.mit.edu/http://rtw.ml.cmu.edu/rtw/http://rtw.ml.cmu.edu/rtw/http://rtw.ml.cmu.edu/rtw/http://openmind.media.mit.edu/http://openmind.media.mit.edu/8/13/2019 Intoduction to Artificial Intelligence
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Model-based Reex
A ent
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Planning
Given
a set of actions
a goal state a present state
Choose actions to get to the goal state And what if you have a team of agents...
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Goal-based Agent
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Utility-based Agent
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Planning problems
Planning in the real world Highly dynamic environments
Uncertain information
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Learning
What does it mean for computers to learn?
Supervised
Unsupervised
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Learning
What does it mean for computers to learn?
Supervised
Unsupervised
circle square circle square
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Learning
What does it mean for computers to learn?
Supervised
Unsupervised
circle square circle square
group these into two categories
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Learning Agent
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Predicting community ratings on webforums and blogs
We have applied this to slashdot.org Authorship recognition
learning who wrote a document bylinguistic style Experiment with applying to text
messages/twitter/transcribed speech
Learning Projects
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Topics the ai enterprise (Turing) 09/29 multiagent systems (Sycara)
09/29
search - google (Page, et al) 10/6 search - dynamic environments
(Stentz) 10/13
constraint reasoning (RN Ch 5)10/13
games (Billings et al (poker))10/27
game theory (MechanismDesign) 10/27
logic/knowledge representation(RN Ch 7-8, BDI) 11/3
teamwork/joint intention (Cohenand Levesque) 11/3
planning (RN Ch 11) 11/10 distributed planning 11/10 machine learning (general) 11/17
machine learning (adversarialclassication) 11/17 reinforcement learning 11/24 ai and the brain 11/24
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Proje ct startin g points psal.cs.drexel.edu
Current research by my research group Robocup soccer
http://ww w.robocup.org Search and re scue
http://ww w.robocuprescue.org
http://ma ple.cs.umbc.edu/~erice aton/searchandrescue/ Animated lifelike agents
http://hmi.ewi.utwente.nl/gala
http://hmi.ewi.utwente.nl/galahttp://maple.cs.umbc.edu/~ericeaton/searchandrescue/http://maple.cs.umbc.edu/~ericeaton/searchandrescue/http://www.robocuprescue.org/http://www.robocup.org/http://hmi.ewi.utwente.nl/galahttp://hmi.ewi.utwente.nl/galahttp://maple.cs.umbc.edu/~ericeaton/searchandrescue/http://maple.cs.umbc.edu/~ericeaton/searchandrescue/http://www.robocuprescue.org/http://www.robocuprescue.org/http://www.robocup.org/http://www.robocup.org/8/13/2019 Intoduction to Artificial Intelligence
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Project starting points The JavaFF planner
http://personal.cis.strath.ac.uk/~ac/JavaFF/
Sports predict ion markets http://www.facebook.com/apps/application.php?id=8575690818
Games http://www.cs.ualberta.ca/~games/
Electronic ma rkets http://www.sics.se/tac/page.php?id=1
http://www.sics.se/tac/page.php?id=1http://www.facebook.com/apps/application.php?id=8575690818http://www.sics.se/tac/page.php?id=1http://www.sics.se/tac/page.php?id=1http://www.cs.ualberta.ca/~games/http://www.cs.ualberta.ca/~games/http://www.facebook.com/apps/application.php?id=8575690818http://www.facebook.com/apps/application.php?id=8575690818http://personal.cis.strath.ac.uk/~ac/JavaFF/http://personal.cis.strath.ac.uk/~ac/JavaFF/8/13/2019 Intoduction to Artificial Intelligence
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Resources
http://www.aaai.org/AITopics
http:// aima.cs.berkeley. edu http:// library.drexel.edu
http:// aispace.org
http://library.drexel.edu/http://library.drexel.edu/http://aima.cs.berkeley.edu/http://library.drexel.edu/http://library.drexel.edu/http://library.drexel.edu/http://library.drexel.edu/http://aima.cs.berkeley.edu/http://aima.cs.berkeley.edu/http://www.aaai.org/AITopicshttp://www.aaai.org/AITopics8/13/2019 Intoduction to Artificial Intelligence
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Readings this week:
Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460. Katia Sycara, Multiagent Systems, AI Magazine 19(2): Summer 1998, 79-92.
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