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Transcript of CS 510: Intro to Artificial Intelligencegreenie/cs510/cs510-17-01.pdf · CS 510: Intro to...
![Page 1: CS 510: Intro to Artificial Intelligencegreenie/cs510/cs510-17-01.pdf · CS 510: Intro to Artificial Intelligence ... Class Exercise • Answer the following questions: ... Samuel’s](https://reader035.fdocuments.in/reader035/viewer/2022062401/5ab550f97f8b9a6e1c8cab8f/html5/thumbnails/1.jpg)
CS 510: Intro to Artificial Intelligence
Rachel GreenstadtDepartment of Computer Science
Drexel Universitywww.cs.drexel.edu/~greenie/cs510/
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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:
• 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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Why Study AI?
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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
15
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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
19
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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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But huge advances in perceptual AI lately!
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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 Agent approach
• Availability of very large data sets / deep learning
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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 (Third Edition)
• Supplementary Readings
• Available on course website
• http://www.cs.drexel.edu/~greenie/cs510
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Course Objectives
• Learn about AI techniques
• Learn how to do AI research (grad class)
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Expectations and Policies
• Use CS Department academic integrity policy – linked from course website
• When in doubt, be transparent, list collaborators and sources, ask if it’s ok
• Exams will cover materials from lectures and readings
• Two late days for assignments, all other late material 20% off per day (for group assignments, late days are for all group members). If you use up your late days early, don’t expect extensions.
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Schedule
• Intro to AI
• Search and Problem Solving
• Planning
• Knowledge Representation
• Learning
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Evaluation
• 20% Online Exams
• Midterm 10%
• Final 10%
• 20% Programming Assignments
• 20% Class Participation
• 40% Final Project
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Homeworks
• HW 1
• Search with sliding block puzzles
• C/C++
• HW 2
• Sentiment analysis with bayesian learning
• Python
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Sliding Block Puzzle
• Implement program to solve sliding block puzzle using search
• Use C/C++
• Start early, do the setup functions
• Extra credit: heuristic search
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Class Participation
• In-class exercises
• Class discussions
• BBLearn Online discussions
• More instructions on website
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Readings and Discussion
• Expectations for all students
• Read papers before class, come ready to discuss
• Send two discussion points/questions to course board by *Tuesday* before class
• Discussion points should be twitter short (140 chars)
• Online students
• By day of class 3 pm
• Reply to two discussion points before class (1-3 sentences)
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Final Project Read Handout!
• Free form research project
• Groups of 2-3 people
• Topic related to AI
• Milestones
• Groups and topic (Oct 5)
• Proposal due (Oct 26)
• Presentation (Dec 7)
• Project write up (Dec 7)
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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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AI Topics
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Topics
• Week 2 (10/5)
• The AI Enterprise (Turing)
• Search RN Ch 3, 4
• Project pre-proposal due
• Week 3 (10/12)
• Multiagent Systems (Sycara)
• Google (Page, et al.)
• Contraints (RN Ch 6)
• Week 4 (10/19)
• Planning (RN Ch 10)
• Homework 1 due
• Week 5 (10/26)
• Project Proposal due
• Games and game theory (RN Ch 5, 17.5-17.6)
• AlphaGo (Silver et al.)
• Week 6 (11/2)Online Midterm
• Week 7 (11/9)
• BDI (Tambe)
• Intelligence without representation (Brooks)
• Logic RN Ch 7, 8
• Week 8 (11/16)
• Machine learning RN Ch 18
• Adversarial classification (Dalvi et al.)
• Week 9 (11/23) No Class!
• Week 10 (11/30)
• Homework 2 due
• Bias (Caliskan et al.)
• Week 11 (12/7)
• Project presentations
• Project due
• Week 12 (12/15) Online Final
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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
• 2010 class: A Travel-Time Optimizing Edge Weighting Scheme for Dynamic Re-planning. Andrew Feit, Lenrik Toval, Raffi Hovagimian and Rachel Greenstadt. AAAI 2010 Workshop on Bridging The Gap Between Task And Motion Planning (BTAMP)
• http://www.seas.upenn.edu/~maximl/wt/AAAI10_ws/BTAMP10_schedule.html
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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
• Go
• Now poker, general game playing
• http://www.computerpokercompetition.org/
• http://games.stanford.edu/
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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?
• How can representations be biased?
• Word embeddings
• https://www.tensorflow.org/tutorials/word2vec
• http://openmind.media.mit.edu/
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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
• How can plans be recognized?
• Games (Poker? Football?)
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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 web forums and blogs
• Authorship recognition
• learning who wrote a document by linguistic style
• Experiment with applying to text messages/transcribed speech
Learning Projects
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Resources
• www.aitopics.org
• http://aima.cs.berkeley.edu
• http://library.drexel.edu
• http://aispace.org
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Readings for next week:
• Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.
• Initial discussion comments due Tuesday at 6 pm
• Online replies due Thursday at 3 pm.