intoduction to artificial intelligence

8/13/2019 Intoduction to Artificial Intelligence

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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/~greenie


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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/cs


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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)

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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.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 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/


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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/


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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


Supervised

Unsupervised

circle square circle square


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Learning


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)

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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/


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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/


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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/AITopics


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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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