cs 886: multiagent systems · 2008. 9. 9. · introduction this course examples cs 886: multiagent...

IntroductionThis Course

Examples

CS 886: Multiagent Systems

Kate Larson

Cheriton School of Computer ScienceUniversity of Waterloo

September 8, 2008

Kate Larson CS 886

Examples

Outline

1 IntroductionIntroductionTwo Communities

2 This Course

3 ExamplesSelfish RoutingLondon Bus System

Kate Larson CS 886

Examples

IntroductionTwo Communities

Outline

2 This Course

Kate Larson CS 886

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Introduction

Kate LarsonFaculty Member in CSMember of the AI research group

Research Interests: Multiagent SystemsStrategic Reasoning

bounded rationality/limited resourcesargumentation

Electronic market design

Kate Larson CS 886

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Introduction

Focus of this course is self-interested Multiagent Systemsaka competitive Multiagent Systems

Study of autonomous agentsDiverging informationDiverging interests

IssuesCooperationCoordinationOvercoming self-interest of agents in order to achievesystem-wide goals

Kate Larson CS 886

Examples

Introduction

Growth in settings where there are multiple self-interestedinteracting parties

NetworksElectronic marketsGame playing...

To act optimally, participants must take into account howother agents are going to actWe want to be able to

Understand the ways in which agents will interact andbehaveDesign systems so that agents behave the way we wouldlike

Kate Larson CS 886

Examples

Introduction

Kate Larson CS 886

Examples

Introduction

Kate Larson CS 886

Examples

Outline

2 This Course

Kate Larson CS 886

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Two Communities

EconomicsTraditional emphasis ongame theoretic rationalityDescribing how agentsshould behaveMultiple self-interestedagents

Computer ScienceTraditional emphasis oncomputational andinformational constraintsBuilding agentsIndividual or cooperativeagents

Kate Larson CS 886

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New Research Problems

How do we use game theory and mechanism design incomputer science settings?How do we resolve conflicts between game-theoretic andcomputational constraints?Development of new theories and methodologies

Kate Larson CS 886

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Explosion of Research

Explosion of research in the area (Algorithmic game theory,computational mechanism design, Distributed algorithmicmechanism design, computational game theory,...)

Papers appearing in AAAI, AAMAS, UAI, NIPS, PODC,SIGCOMM, INFOCOMM, SODA, STOC, FOCS, ...Papers by CS researchers appearing in Games andEconomic Behavior, Journal of Economic Theory,Econometrica,...Numerous workshops and meetings,...

Kate Larson CS 886

Examples

This Course

Introduction to game theory, social choice, mechanismdesignStudy how they are used in computer science (in particularin AI)Study computational issues that arise

Course structureIntroductory lecturesCurrent research papers

Kate Larson CS 886

Examples

This Course

Introduction to game theory, social choice, mechanismdesignStudy how they are used in computer science (in particularin AI)Study computational issues that arise

Course structureIntroductory lecturesCurrent research papers

Kate Larson CS 886

Examples

Prerequisites

No formal prerequisitesStudents should be comfortable with mathematical proofsSome familiarity with probabilityIdeally students will have an AI course but I can providebackground material when neededI will cover the game theory and mechanism designrequired

Kate Larson CS 886

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Grading

2-3 assignments on game theory and mechanism design:10%In class presentation(s): 20%

Peer-reviewed

Class participation: 20%Research project: 50%

Kate Larson CS 886

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Presentations

Every student is responsible for presenting a research paper inclass

Short survey + a critiqueEveryone in class will provide feedback on the presentationMarks given on coverage of material + organization +presentation

Kate Larson CS 886

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Class Participation

You must participate!Before each class (before 6:00 am the day of thepresentation) you must submit a review of one of thepapers being discussed

What is the main contribution?Is it important? Why?What assumptions are made?What applications might arise from the results?How can it be extended?What was unclear?· · ·

Kate Larson CS 886

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Projects

The goal of the project is to develop a deep understanding of atopic related to the course.

The topic is openTheoretical, experimental, in-depth literature review,...Can be related to your own researchIf you have trouble coming up with a topic, come and talk tome

Proposals due October 20Final projects due December 1st1

Students will present projects in class

1I will likely be flexible with this.Kate Larson CS 886

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Projects

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Projects

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Projects

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Projects

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Other Information

Class times: Monday-Wednesday 10:00-11:30Office Hours: By appointment (just send me email or talkto me after class to set up an appointment)Course website

http://www.cs.uwaterloo.ca/~klarson/teaching/F08-886

Kate Larson CS 886

Examples

Selfish RoutingLondon Bus System

Outline

2 This Course

Kate Larson CS 886

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Selfish RoutingWe want to find the least-cost route from S to T .Costs are private information – we do not know themWe do know that agents (nodes) are interested inmaximizing revenueHow can we use this to figure out the least-cost route?

Kate Larson CS 886

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Outline

2 This Course

Kate Larson CS 886

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London Bus System2

5 million passengers daily7500 buses700 routesThe system has been privatized since 1997 by usingcompetitive tenderingIdea: Run an auction to allocate routes to companies

2As of April 2004Kate Larson CS 886

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Auction ProtocolLet G be set of all routes, I be the set of biddersAgent i submits bid vi(S) for all bundles S ⊆ GCompute allocation S∗ to maximize sum of reported bids

V ∗(I) = max(S1,...,Sn)

vi(Si)

Compute best allocation without each agent

V ∗(I \ i) = max(S1,...,Sn)

∑j 6=i

v∗j (Sj)

Allocate each agent S∗i , each agent pays

P(i) = v∗i (S∗

i )− [V ∗(I)− V ∗(I \ i)]

Kate Larson CS 886

Examples

V ∗(I) = max(S1,...,Sn)

vi(Si)

V ∗(I \ i) = max(S1,...,Sn)

∑j 6=i

v∗j (Sj)

P(i) = v∗i (S∗

i )− [V ∗(I)− V ∗(I \ i)]

Kate Larson CS 886

Examples

V ∗(I) = max(S1,...,Sn)

vi(Si)

V ∗(I \ i) = max(S1,...,Sn)

∑j 6=i

v∗j (Sj)

P(i) = v∗i (S∗

i )− [V ∗(I)− V ∗(I \ i)]

Kate Larson CS 886

Examples

V ∗(I) = max(S1,...,Sn)

vi(Si)

V ∗(I \ i) = max(S1,...,Sn)

∑j 6=i

v∗j (Sj)

P(i) = v∗i (S∗

i )− [V ∗(I)− V ∗(I \ i)]

Kate Larson CS 886

Examples

London Bus System

Mechanism: Generalized Vickrey AuctionSpecifies the rulesDescribes how outcome will be determined

StrategiesPolicies which specify what actions to takeAgents are self-interested and rational

GVA is efficient and strategy-proof

Kate Larson CS 886

Examples

Computational Issues

Winner determination problem: Select bids to maximizesum of reported values

Maximum weighted set packing (NP-hard)Solve this problem I + 1 times

Agent valuation problemCommunication complexity

Each agent has to communicate 2700 bids to the auctioneer

Kate Larson CS 886

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Kate Larson CS 886

Examples

Kate Larson CS 886

Examples

Kate Larson CS 886

cs 886: multiagent systems · 2008. 9. 9. · introduction this course examples cs 886: multiagent...

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