CS 510: Intro to Artificial Intelligence

Rachel GreenstadtDepartment of Computer Science

Drexel Universitywww.cs.drexel.edu/~greenie/cs510/index.html

Overview

• What is Artificial Intelligence?

• History of AI

• What is CS 510?

• Syllabus, Schedule, Grading

• Final Project

• Overview of AI Topics

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? :)

What is AI?

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

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

Thinking like humans

• 90% of humans get it wrong

• Answer is cards 2 and 3

• Most people pick 1 and 3

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.

What is AI?

Thinking like a human

Thinking rationally

Acting like a human

Acting rationally

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?

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

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

agent

1. One that acts or has the power or authority to acts

2. One empowered to act for or represent another

13

Agents

Simple reflex agent

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

Intelligent Software Agents

• Responsive

• Goal-Directed

• Autonomous

• Social

17

PEAS

• Performance Measure

• Environment

• Actuators

• Sensors

• Examples?

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

Agent or Program?

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

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”

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

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

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

AI State of the Art

AI Applications

AI in Space

Autonomous satellite separation and docking

Exploring MarsMonitoring the sky

with telescope arrays

AI Art

What is CS 510?

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.html

Course Objectives

• Learn about AI techniques

• Learn how to do AI research (grad class)

Schedule

• Intro to AI

• Search and Problem Solving

• Planning

• Knowledge Representation

• Learning

Evaluation

• 30% Exams

• Midterm 15%

• Final 15%

• 5% Machine Learning exercise

• 25% Class Participation

• 40% Final Project

Class Participation

• In class exercises

• Class discussions

• bbvista Online discussions

• More instructions on website

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 led by you

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 (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

• NLP 11/24

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)

The Final Project Proposal

• 2 pages long

• Problem Statement and Motivation

• Brief Description of Approach

• Related Work and novelty

• Evaluation approach

• Milestones

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)

AI Topics

Search

• The “Heuristic Search Hypothesis”

- (Newell and Simon)

• Subroutine of intelligent systems

• problem solving

• planning

• knowledge

• games

Some search issues we’ll discuss

• Intractability of exhaustive search

• Use of heuristics (A*)

• Local search “satisficing”

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, 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

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)

Applications

• Supply chain management

• Scheduling

• Resource and task allocation

• Multiagent coordination

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

• 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 topic in this area if you want

Games / Adversarial Search

• 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/

Mechanism Design

• Construct incentives for agents that are:

• self-interested

• utility-maximizing

• Applications

• Auctions

• Reputation systems

• Traffic systems

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?

• Links

• http://openmind.media.mit.edu/

• http://rtw.ml.cmu.edu/rtw/

Model-based Reflex Agent

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...

Goal-based Agent

Utility-based Agent

Planning problems

• Planning in the real world

• Highly dynamic environments

• Uncertain information

Learning

• What does it mean for computers to learn?

• Supervised

• Unsupervised

Learning

• What does it mean for computers to learn?

• Supervised

• Unsupervised

“circle” “square” “circle” “square” …

Learning

• What does it mean for computers to learn?

• Supervised

• Unsupervised

“circle” “square” “circle” “square” …

Learning

• What does it mean for computers to learn?

• Supervised

• Unsupervised

“circle” “square” “circle” “square” …

“group these into two categories”

Learning Agent

• Predicting community ratings on web forums and blogs

• We have applied this to slashdot.org

• Authorship recognition

• learning who wrote a document by linguistic style

• Experiment with applying to text messages/twitter/transcribed speech

Learning Projects

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

Project starting points• psal.cs.drexel.edu

• Current research by my research group

• 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

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

Resources

• http://www.aaai.org/AITopics

• http://aima.cs.berkeley.edu

• http://library.drexel.edu

• http://aispace.org

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.