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CS 510: Intro to Artificial Intelligence
Rachel GreenstadtDepartment of Computer Science
Drexel Universitywww.cs.drexel.edu/~greenie/cs510/index.html
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Overview
• What is Artificial 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 three index cards:
• What is Intelligence?
• What is Artificial Intelligence?
• What is an agent? What attributes does an agent have?
• When you’re done, swap your answers with a neighbor
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A 42
Each card has a number or letter on one sideand 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 a square 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 sideand a drink on the other side
Which cards must you turn over to determine if the following 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 a human
Acting rationally
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Why Study AI?
• Fundamental scientific questions
• What does it mean to be smart?
• What makes us smart?
• Can our intelligence be replicated or exceeded? And how?
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Why Study AI?
• Fundamentally useful engineering question
• AI in computers increases humanity’s collective intelligence and abilities
• Areas where computers lack the ability to act rationally limit us
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But is it even possible?
• Billions of human computers must be doing something....
• Strong vs. Weak AI
• Human-level intelligent machines, conscious?
• “thinking-like” features to make computers 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
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Agents
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Simple reflex 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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Intelligent Software Agents
• Responsive
• Goal-Directed
• Autonomous
• Social
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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 profits
• Environment: Roads, other traffic, 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 can’t speak, see, or reason like 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 Neuman’s minimax algorithm, used for game-playing
• 1950 Turing test devised
• 1950 Asimov publishes the 3 laws of robotics
• 1956 McCarthy coins “Artificial Intelligence” / Dartmouth conference
• Early years (1956-1970)
• Micro-worlds
• Reasoning by search
• Many successes, lots of optimism/hype - Samuel’s checkers, Gelemter’s Geometry theorem prover, Shakey, Dendral
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AI Historical Highlights• AI Winter (1970s)
• Perceptrons - limits of neural networks
• language difficulties - “The spirit is willing but the flesh 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 “scruffies”)
• Statistical machine learning/HMMs has many successes
• AI starts to make people rich
• Moore’s law makes a lot more possible
• Emergence of Intelligent Agents
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AI State of the Art
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AI Applications
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AI in Space
Autonomous satellite separation and docking
Exploring MarsMonitoring the sky
with telescope arrays
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AI Art
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What is CS 510?
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Course Information
• Textbook
• Stuart Russell and Peter Norvig
• Artificial Intelligence: A Modern Approach
• Prentice-Hall (Second Edition)
• Supplementary Readings
• Available on course website
• http://www.cs.drexel.edu/~greenie/cs510.html
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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 25 minute discussion around one topic/paper
• Discussion should involve a 5-10 minute presentation/demo on related material
• Related paper, background, application
• Rest should be class discussion/exercises led by 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 (Stentz) 10/13
• constraint reasoning (RN Ch 5) 10/13
• games (Billings et al (poker)) 10/27
• game theory (Mechanism Design) 10/27
• logic/knowledge representation (RN Ch 7-8, BDI) 11/3
• teamwork/joint intention (Cohen and Levesque) 11/3
• planning (RN Ch 11) 11/10
• distributed planning 11/10
• machine learning (general) 11/17
• machine learning (adversarial classification) 11/17
• reinforcement learning 11/24
• ai and the brain 11/24
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Final ProjectRead Handout!
• Free form research project
• Groups of 1-3 people
• Milestones
• Groups and topic (Sept 29)
• Proposal draft sent to reviewer (Oct 6)
• Reviewer comments due (Oct 8)
• Proposal due (Oct 13)
• Reviewer discussion notes due (Nov 10)
• Presentation (Dec 1)
• Project write up (Dec 1)
• Review due (Dec 4)
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The Final Project Proposal
• 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 on the other project
• At week 3 (project proposal draft)
• At week 7 (project progress)
• At week 10 (final 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 we’ll discuss
• Intractability of exhaustive search
• Use of heuristics (A*)
• Local search “satisficing”
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Open Problems
• Distributed search
• Dynamic search
• Check out STAIRS workshop
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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 relationships between the variables (must be satisfied or optimized)
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Applications
• Supply chain management
• Scheduling
• Resource and task allocation
• Multiagent coordination
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Open problems in Constraint Reasoning• 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
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Games / Adversarial Search
• Inherently multiagent and competitive
• Classic work in turn based games
• Chess
• Checkers
• Now poker, general game playing
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Mechanism Design
• Construct incentives for agents that are:
• self-interested
• utility-maximizing
• Applications
• Auctions
• Reputation systems
• Traffic systems
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Knowledge Representation
• What is common sense?
• How a problem is represented greatly affects its efficiency
• How can we encode the things we know so computers understand them?
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Model-based Reflex Agent
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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” …
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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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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 (Mechanism Design) 10/27
• logic/knowledge representation (RN Ch 7-8, BDI) 11/3
• teamwork/joint intention (Cohen and Levesque) 11/3
• planning (RN Ch 11) 11/10
• distributed planning 11/10
• machine learning (general) 11/17
• machine learning (adversarial classification) 11/17
• reinforcement learning 11/24
• ai and the brain 11/24
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Project starting points
• Robocup soccer
• http://www.robocup.org
• Search and rescue
• http://www.robocuprescue.org
• http://maple.cs.umbc.edu/~ericeaton/searchandrescue/
• Animated lifelike agents
• http://hmi.ewi.utwente.nl/gala
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Project starting points
• The JavaFF planner
• http://personal.cis.strath.ac.uk/~ac/JavaFF/
• Sports prediction markets
• http://www.facebook.com/apps/application.php?id=8575690818
• Games
• http://www.cs.ualberta.ca/~games/
• Electronic markets
• http://www.sics.se/tac/page.php?id=1
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Resources
• http://www.aaai.org/AITopics
• http://aima.cs.berkeley.edu
• http://library.drexel.edu
• http://aispace.org
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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.