1

IISI Overview

Carla P. Gomesgomes@cs.cornell.edu

Apr 5, 2006

IISI Overview

Carla P. Gomesgomes@cs.cornell.edu

Apr 5, 2006

2

To perform and stimulate research in the design and study of

Intelligent Information Systems.

To foster collaborations between Cornell, AFRL/IF, and the research

community in general, in Computing and Information Science.

To play a leadership role in the research and dissemination

of the core areas of the institute.

Mission

Scientific Excellence

Boosting AFRL/IF research involvement

Boosting

AFRL/IF

Research Profile

ScientificExcellence

3

IISI Model

• Research collaborations and projects

• Visiting scientists

• Research conferences and workshops

• Special research programs (special periods concentrating on specific topics and challenges)

• Technical reports and other publications

IISI

AFRL/IF Cornell

Visitors

OutsideResearchers

Research Interactions

IISI is modeled after successful national research institutes such as the DIMACS center for Discrete Mathematics and the Aspen Center for Physics.

4

IISI Scientific Advisory Board

Dr. Robert Constable --- Dean, Faculty of Computing and Information Sciences, CornellDr. Juris Hartmanis --- Sr. Associate Dean for Computing and Information

Sciences, Cornell Major Amy Magnus, Ph.D. --- Progr. Manag., AFOSRDr. John Bay --- Chief Scientist, AFRL/IF

Ms. Julie Brichacek and Mr. Charles Messenger - Branch Chiefs, AFRL/IF

Research Agenda

Design and Study of Intelligent Systems

GoalStart

Planning & Scheduling

Software & HardwareVerification

Satisfiability

(A or B) (D or E or not A)

Quasigroup

Data Mining

Fiber optics routing

Air Tasking Order

Information Retrieval

AutonomousAgents

Focus:Computational and Data

Intensive Methods

Automated Reasoning Modeling UncertaintyMachine LearningInformation Retrieval

Games

7

Compute Intensive

Many computational tasks, such as planning, scheduling, negotiation, can in principle be reduced to an exploration of a large set of all possible scenarios.

Try all possible schedules, try all possible plans etc.

Problem: combinatorial explosion!

Seconds until heat death of sun

No. of atomsOn earth

Explosion of number of possible scenarios to consider

Rules (Constraints)

1047 100 200

10K 50K

1M5M

20K 100K

0.5M 1M

Variables

1030

10301,020

10150,500

106020

103010

Cas

e co

mp

lexi

ty

Car repair diagnosis

Deep space mission control

Chess (20 steps deep)

VLSIVerification

War Gaming

100K 450K

Military Logistics

100 10K 20K 100K 1M

Exponential

Compl

exity

(Kumar/Selman, Darpa IPTO)

Data intensive

video 1 Gigabyte/hour 1000 hours

scanned images

1 Megabyte each 1 million images

text pages 3300 bytes/page 300 million pages (Library of Congress)

Wal-Mart customer data: 200 terabyte --- daily data mining for customer trends

Microsoft already working on a PC where nothing is ever deleted.

Personal Google on your PC.

Storage for

$200

Yr ’05, 1 Terabyte for $200.

What can we store with 1 Terabyte?

IISI Cornell Researchers

Carlos Ansótegui: Encodings and solvers for combinatorial problems (Computer Science)Raffaello D'Andrea: Dynamics and Control (Mechanical & Aerospace Engineering)Claire Cardie: Natural language understanding and machine learning. (Computer Science)Rich Caruana: Machine learning, data mining and bioinformatics (Computer Science)JonConrad: Resource economics, environmental economics (Appl. Economics)Johannes Gehrke: Database systems and data mining. (Computer Science)Carla Gomes: AI/OR for combinatorial problems and reasoning (Computer Science)Joseph Halpern: Knowledge representation and uncertainty. (Computer Science)Juris Hartmanis – Theory of computational complexity. (Computer Science)John Hopcroft: – Information Capture and Access. (Computer Science)Thorsten Joachims: Machine learning for information retrieval (Computer Science)Lillian Lee: Statistical methods for natural language processing (Computer Science)Bill Lesser: Technology transfer, property rights issues (Appl. Economics)Keshav Pingali: Intelligent software systems, self-optimizing programs (Computer Science)Venkat Rao: control theory, planning and scheduling, multi-vehicle systems, AI-controls gap. (Mechanical & Aerospace Engineering)

David Schwartz: Computer Game Design (Computer Science)Bart Selman: Knowledge representation, complexity, and agents. (Computer Science)Phoebe Sengers:   Human-comp. interaction (Information Science)David Shmoys: Algorithms for large-scale discrete optimization. (Operations Research)Chris Shoemaker: Large scale optimization and modeling. (Civil Engineering)Steve Strogatz: Complex networks in natural and social science (Applied Mathematics)Willem van Hoeve: CP and OR methods for combinatorial (optimization) problems (Computer Science)Stephen Wicker: Intelligent wireless information networks. (Electrical Computer Engineering)

Graduate, MEng, and Undergrad students

11

Andrew Boes – Inductive Logic Programming and reasoning and ReasoningJoe Carozzoni – Mixed Initiative Planning and Agent SystemsJerry Dussault – Decision Theory Nathan Gemelli - Asynchronous Chess Jeff Hudack - Information Extraction / Knowledge Representation James Lawton - Agent technologyJim Nagy - A Peer to peer DatabasesMark Linderman - Modeling Preferences in JBI Richard Linderman - Architectures and Systems for Cognitive Processing Robert Paragi - Study and visualization of the effect of structure on problem complexityLouis Pochet: Active memory systems Nancy Roberts: Bayesian predictive model of an interactive environment/ AFRL Virtual WorldPeter Lamonica: Information retrieval. Justin Sorice: Games and Reasoninng. John Spina: Information routing in wireless ad-hoc networks Matthew Thomas: Dynamic probabilistic target tracking in a distributed sensor network Robert Wright : Analysis of network vulnerabilities / Asynchronous Chess Mark Zappavigna: Information Extraction / Knowledge Representation  

AFRL/IF Researchers Across Several Divisons

(Curent and past IF researchers/activities )

Boosting

AFRL/IF

Research Profile

12

IISI Visitors - Summer 2001/2003/2004/2005

• Dimitris Achlioptas (Microsoft Research) • Shai Ben-David, (Technion, Israel)• Carmel Domshlak (Ben-Gurion Univ.)• Cesar Fernandez (University of Barcelona) • Eric Horvitz (Microsoft Research)• Joerg Hoffman (Max Plank Inst. )• Henry Kautz (U. Washington)• Leslie Kaebiling (MIT)• Scott Kirkpatrick (IBM/Hebrew University)

• Kevin Leyton-Brown (Stanforf Univ.) • Michael Littman (AT&T Research) • Felip Mańa (University of Barcelona)• Fernando Pereira (University of Penn)

CollaborationsWith

OutsideResearchers

•Jean-Charles Regin (ILOG/CPLEX)

•Joao Marques-Silva (U. Lisbon)

•Meinolf Sellmann (U. Paderborn)

•Yoav Shoam (Stanford Univ.)

•Cosntantino Tsallis (Physics Center Br)

•Manuela Veloso (CMU) •Toby Walsh (York University,UK)

•Walker White (U. Texas)

•Filip Zelezny (Czech Tech.Un. )

•Wayne Zhang (Un. Washington)

And more…

13

IISI research featured in:

And of course lots of standard peered reviewed publications…

14

Research Themes

1– Mathematical and Computational Foundations of Complex Networks

2 – Automated Reasoning: Complexity and Problem Structure

3 – Autonomous Distributed Agents, Complex Systems, and Advanced Architectures

15

1 – Mathematical and Computational Foundations of Complex Networks

Examples

16

The National Academies Study Network Science

John Hopcroft (Co-Chair)

•Networks and Network Research in the 21st Century•Networks and the Military•The definition and Promise of Network Science•The content of Network Science•Status and Challenges of network Science•Creating Value from Network Science:

Scope and Opportunity•Conclusions and Recommendations

17

Sub-Category GraphNo Threshold

New Science of Networks

NYS Electric Power Grid(Thorp,Strogatz,Watts)

Cybercommunities(Automatically discovered)

Kleinberg et al

Network of computer scientistsReferralWeb System(Kautz and Selman)

Neural network of the nematode worm C- elegans

(Strogatz, Watts)

Networks arepervasive

Utility Patent network 1972-1999

(3 Million patents)Gomes,Hopcroft,Lesser,Selman

18

Huge Data sets, Readily Available

Black Box/Oracle(Data Miner)

Results are structured…

… but how well?

Discovering Natural Communities in Large Linked Discovering Natural Communities in Large Linked NetworksNetworks

John Hopcroft, Bart Selman, Omar Khan and Brian Kulis

CiteSeer Structure compared to Random Structure

Data and ResultsHierarchical Structure

Natural communities – appear in many randomized runs

Random GraphsNEC CiteSeer

Citation graph (no text)

RG1: Same degree structureNO NATURAL COMMUNITIES

Natural Community Tree

Motivation

RG2: Adjacency Matrix with embedded Structure

NATURAL COMMUNITIES?

Genome Data

The Internet

Proc. National Academy Of Sciences

19

Impact: Referral Web to Track Nuclear Scientists in Iraq

21

Research Themes

2 – Automated Reasoning:

Complexity and Problem Structure

Prof. Selman will provide an overview of this area

22

Formal Models. Problem structure, BackdoorsH. Chen (Cornell)John Hopcroft (Cornell)Jon Kleinberg (Cornell)R. Williams (CMU)Joerg Hoffman (Max-Planck Inst.)

Heavy-tailed Phenomena in Computational Processes

Information Theory:S. Wicker (Cornell)

Branching ProcessesK. Athreya (Cornell)

HOT:Robustness vs.FragilityJohn Doyle (Caltech)Walter Willinger (AT&T Labs)

Power laws vs. Small-world S. Strogatz (Cornell)T. Walsh (U. New South Wales)

C. Gomes (Cornell)B. Selman (Cornell)

Learning Dynamic Restart StrategiesE. Horvitz (Micrsoft Research)H. Kautz and Y. Ruan (U. Washington)Nudelman and Shoham (Stanford)

Random CSP ModelsC. Fernandez, M. Valls (U. Lleida)C. Bessiere (LIRMM-CNRS)C. Moore (U. New Mexico)

Results presented at:

Annual meeting (2005).

Connections and Collaborations

Approximations and RandomizationLucian Leahu (Cornell)David Shmoys (Cornell)

Boosting Reasoning Technology Through Randomization, Structure Discovery, and Hybrid Strategies

Does there exist a 1st move for White, such that for all possible 1st moves for Black, such that there exists a 2nd move for White, such that for all possible 2nd moves for Black, such that

… [the set of logical clauses encoding “Black king captured” is satisfied.]

Prevent Black to falsify the QBF by performing “illegal” actions (moves). Ex: “Black moves twice at a step i”.

global indicator (z) value ?

backtrack if z is up

QB solver Conditional monitor

Quantified Boolean Formula global indicator variable

True or False

Extending state-of-the-art QB Solvers:- Objective: preserve the natural search space

- Idea: backtrack as soon as an indicator variable indicates an illegal action.

To clausal normal form (CNF) :

- Objective: : produce QBF in CNF. Avoid exponential blown-up in size due to translation

- Idea: introduce a hierarchy of auxiliary (indicator) variables. Indicator variables represent illegal actions

- Issue: the addition of new indicator variables can increase the natural search space

Problem Solving Strategies Using Quantified Boolean Formulas

Relaxing universal quantifiers:-Objective: given a set of decisions detect, as soon as possible, the unsatisfiability of the formula, i.e., the unreachability of the Goal.

Relax (universal quantifier) = existential quantifier

- Idea: in our chess problem, to relax the universal quantifiers at a certain level forces Black to cooperate with White at that level. “The unreachability of the Goal under cooperation (help mate) is a sufficient condition for the unreachability of the Goal without cooperation (regular mate)”

Non Conditional Conditional instance quaffle semprop qube cquaffle

1 3708 0.01 0.01 0.01

2 - * 133 9

3 - - - 0.01

4 - - - 0.02

5 - - - 0.01

6 - * - 9

7 - * * 3.5

8 - * * 5.12

9 * * * *

Performance of QB solvers

Time (secs): ‘-’ did not complete in 20,000

seconds;

‘*’ formula too large to execute

natural search space

illegal search space

The problem:

The solution:

The results:

Help capture (when all universals are relaxed) is NP-Complete

Capture is PSPACE-Complete

Carlos Ansotegui

Robert Constable

Carla Gomes

Christoph Kreitz

Bart Selman

Encoding problems as Quantified Boolean Formulas (QBF):

- Objective: generate efficient encodings for QBF

- Idea: keep the cost of detecting local consistency close to the cost of detecting local inconsistency

ii LM ,

G

case study: capture black king in k moves

))(( GEEAIA bwwb 101210 k

wk

bk

wk

bw LLMMMMM

: Goal G: initial position

I: actions and effects of White (Black)),(, bbww EAEA

- Approach: during search, relax subsets of universal quantifiers (between “capture” and “help capture”), and check the reachability of the Goal

• axioms :

• variables :

: moves and locations at step i

24

• New results:– CNF and DNF formulations for QBF

(submitted to SAT 06)

– Automated generation of so-called Streamlining constraints

(submitted to AAI06)

Problem Solving Strategies Using Quantified Boolean Formulas

QBF

Willem-Jan van Hoeve

Combinatorial Problems:logistics, circuit

verification, scheduling, …

Operations Research:• linear programming• semi-definite programming• dedicated algorithms

Constraint Programming:• exhaustive search • constraint propagation (search space reduction)

Combination:• OR relaxations guide CP search and prove optimality faster• dedicated OR algorithms for fast constraint propagation

Operations Research Techniques in Constraint Programming

solve solve

solve

26

Research Themes

3 – Autonomous Distributed Agents, Complex Systems, and Advanced Archictetures

Examples

GDIAC: The Game Design Initiative at CornellGDIAC: The Game Design Initiative at CornellDavid Shwartz David Shwartz gdiac.cis.cornell.edu

Research Projects:

► Wargame development and design► Game Library► Curricula► Outreach

28

Control of Complex Systems

HIERARCHICAL DECOMPOSITION

OBJECTIVE: Develop hierarchy-based tools for designing complex, multi-asset systems in uncertain and adversarial environments

EXAMPLE: ROBOCUP

•System level decomposition•Bottom up design•Model Simplification•Uncertainty Propagation•Heuristics and Verification

Relaxation,Restriction

COMPLEXITY

PERFORMANCE1

STRATEGY TRAJECTORYGENERATION

LOCALCONTROL

DESIRED FINAL POSITIONS ANDVELOCITIES, TIME TO TARGET

FEASIBILITY OF REQUESTS

DESIREDVELOCITIES

INTERCONNECTED SYSTEMS

•LARGE numbers of actuators and sensors•Distributed computation•Limited connectivity

DISTRIBUTED ARCHITECTURES:

dz

y uGG

KK

d(t, s ): disturbancesz(t, s ): errorsy(t, s ): sensorsu(t, s ): actuators

* *1 1*

1 11* *1 11

C0

A A BU C I D U

B D I

YY

+ Y Y

SEMI-DEFINITE PROGRAMMING APPROACH:

•Vehicle platoons•Finite difference approximations of PDEs•Cellular automata, artificial life, etc.•Behavior of groups, swarm intelligence, etc.

CHALLENGES:

Raff D Andrea

29

José F. MartínezElectrical and Computer Engineering

• Reconfigurable chip multiprocessors– Application-driven dynamic adaptation

• Turn on/off cores• Fuse/separate cores• Adjust voltage/frequency

– Multilevel adaptation (HW+SW)– Applying machine learning (w/ Caruana)

• Learning-based architecture design

• Workshop IISI/IF– Architectures and Systems for Cognitive Processing

IISI - AFRL/IF

Boosting

AFRL/IF

Research Profile

31

What can IISI provide to stimulate research at IF?

• Immersion in an active research environment• Research advice and infrastructure• Research Collaborations• Working group meetings (at IF and Cornell)• Reading Groups• Visits by IISI fellows and associates • Cornell AI seminar and colloquia• Joint Cornell / IF projects• Library privileges• Computer accounts at Cornell• Office space at Cornell

32

Interactions Cornell/IF

• Peer to peer collaborations

• Cornell mentoring to IF researchers– Independent project;

– MSc and PhD co-advising;

– Informal project;

• Courses at Cornell (including independent research)

• Coordinated research groups at CU and IF

• Coordinated research workshops

• Collaborative research involving both organizations

• Joint projects

• Regular Seminars (at IF and CU)

Examples of IISI/IF Collaborations

34

Working on

PhD

• Project Objective: Develop a model of multi-agent opportunism for cooperative, heterogeneous agents operating in open, real-world multi-agent systems

– Single-Agent Opportunism: The ability of an individual agent to alter a pre-planned course of action to pursue a different goal, based upon a change in the environment or in the agent’s internal state – an opportunity

– Multi-Agent Opportunism: The ability of agents operating in a MAS to assist one another by recognizing potential opportunities for each other’s goals, and responding by taking some action and/or notifying the appropriate agent or agents

• Approach: Augment existing approaches to single-agent opportunism and MAS coordination mechanisms with sufficient knowledge-sharing capabilities to allow agents to recognize and respond to opportunities for one another.

• Benefits:– Allow the MAS to better adapt to its changing environment by

exploiting unexpected events– Improve in the overall performance of the MAS by allowing agent to

complete suspended goals/tasks early (or at all)– Ensure agents obtain critical information in a timely fashion (i.e.

“Precision-Guided Information”)

Multi-Agent OpportunismMulti-Agent OpportunismJamie Lawton (AFRL/IF-IFED)Jamie Lawton (AFRL/IF-IFED)

Carmel Domshlak (Cornell)Carmel Domshlak (Cornell)Recognize

Opportunity Cue

DetermineFacilitated Action

Decide if Pursuit is Appropriate

Respond toOpportunity

Ignore Opportunity

None

Mine

Ignore Opportunity

No (otheragent)

Yes

Informed of Opportunity Cue

InformOther Agent

Other agent’s

Opportunity Cue

Negotiate withOther Agent

No (me only)

Multi-Agent Opportunism Process

Manual Agent

History Agent

Supply AgentSupply Agent

Vendor Agent

Vendor Agent

Mechanic AgentMechanic Agent

History Agent

• • •

• • •

•••

•••

MiddleAgents

Aircraft Maintenance Information System

Boosting

AFRL/IF

Research Profile

Researchs

Paper

35

Bayesian Predictive Model of an Interactive Bayesian Predictive Model of an Interactive EnvironmentEnvironment

Objective

To apply uncertainty techniques (Bayesian Networks and Decision Theory) to COTS tools in the area of home automation and thus, add intelligence to it.

Home Automation - Allows a person to monitor and control devices(e.g., lights, sensors, cameras, TV’s) in their own home based on some simple rules.

Problem: To be accurate, you need to model every situation or else you could get undesired result. (e.g. Lights turn on or off when you don’t want them to.)

Nancy Roberts - AFRL/IF,IFEDNancy Roberts - AFRL/IF,IFEDCarla Gomes Cornell University.Carla Gomes Cornell University.

Michael Pittarelli SUNYITMichael Pittarelli SUNYITDomain: Office Security

Hardware Used:3 X10 Sensors, X10 Tranceiver, and ActiveHomeX10 CM11A computer interface

VBscript

X10 Motion Sensor Software Used:HomeSeer,MSBNx, andVisual BasicVBscript

– Provides Improved Accuracy for COTS S/W– Saves Energy and Money– Other Domains it could be Applied to:

• Digital Avatars• Agents – Sensor Planning• Interactive Data Wall• Intelligent Intrusion Detection

AF Payoff

TimeDay

BreakIn

Sensor

What is P(BreakIn=Yes |Day=Sunday, Time=830-1700, Sensor=On)?

P(A|B)=P(A,B)/P(B): P(BI|D,T, S) = P(D, T, S, BI)/P(D,T,S)

= P(D=Sun)P(T=830-1700)P(BI=yes|D=Sun, T=830-1700)P(S=On|BI=yes)

i=(yes,no) P(D=Sun)P(T=830-1700)P(BIi |D=Sun, T=830-1700)P(S=On|BIi )

Maximize Expected Utility

“utility(or desirability) X probability”

EU(a) = sstates u(a,s)p(s|a)

Calculations

Boosting

AFRL/IF

Research Profile

Master’s

Degree

36

33rdrd Generation War- Generation War-GamesGames System-on-SystemSystem-on-System

Model effectiveness Model effectiveness of of units wrt current units wrt current statestate within the systemwithin the system

Abstract System as a Abstract System as a NetworkNetwork

Identify Points of Failure Identify Points of Failure as Preferable Targets as Preferable Targets

Boosting

AFRL/IF

Research Profile

Analysis of Network VulnerabilitiesAnalysis of Network VulnerabilitiesCornell / IF ProjectCornell / IF Project

Robert Wright (AFRL/IF-IFED)Robert Wright (AFRL/IF-IFED) Meinolf Sellmann (Cornell)Meinolf Sellmann (Cornell)

Research

Paper

38

• Increasing the communication range in an Increasing the communication range in an ad-hoc wireless system increases the density ad-hoc wireless system increases the density of the network graph. of the network graph.

Complexity Complexity in Ad-hoc Wireless Networksin Ad-hoc Wireless Networks

Challenge Problem: Challenge Problem: Wireless Target Tracking SystemWireless Target Tracking System

Communicating Doppler radar sensors Communicating Doppler radar sensors tracking multiple targetstracking multiple targets

• The probability of detecting all The probability of detecting all targets undergoes atargets undergoes a phase transitionphase transition with respect to thewith respect to the radar and radar and communication range.communication range.

Computational costComputational cost

Communication rangeCommunication rangeRadar rangeRadar range

Communication costCommunication cost

Communication rangeCommunication rangeRadar rangeRadar range

Communication rangeCommunication rangeRadar rangeRadar range

Detection Probability (%)Detection Probability (%)

Generalization to Other Ad-hoc WirelessGeneralization to Other Ad-hoc WirelessNetworkProblemsNetworkProblems

• Phase transition analysisPhase transition analysis provides a provides a mechanism for identifying and mechanism for identifying and quantifying the quantifying the critical range of critical range of network resources needed for scalable, network resources needed for scalable, self-configuring, ad-hoc networksself-configuring, ad-hoc networks

Increasing communication rangeIncreasing communication range

•The The computational and computational and communication complexitycommunication complexity peaks near the phase peaks near the phase transition region.transition region.

sensorsensor

targetargett

Impact: Applications

39

Probabilistic Target Tracking with a Network of Distributed Sensor Agents

Matthew Thomas (AFRL/IF) (AFRL/IF)

Bhaskar Krishnamachari (Cornell)(Cornell)

• Project Goals:– Extend ongoing work on target

tracking using sensor networks

– Investigate how the incorporation of probability reasoning can reduce energy consumption by sensors

– Study the communication costs involved in distributed decision making with imperfect information

–Distributed sensor network

•limited range, limited communications, limited power resources

•no centralized control

•how get sensors to work cooperatively in order to most efficiently track targets?

Model:

–Multi-agent system of sensor network agents using probabilistic reasoning

Boosting

AFRL/IF

Research Profile

40

AFRL 3D Virtual World AFRL 3D Virtual World Nancy Roberts (AFRL-IFED),

Margaret Corbit and Dan White (Cornell),

The objective of this project is to

explore and apply various artificial

intelligence techniques to enhance a

digital informational environment.

3-D virtual world based on Active Worlds™ used to provide information about AFRL.

AFRL Virtual World

Hall of HistoryAmphitheatre

41

• Asynchronous Chess (AChess) Learning: Learning in a real-time, adversarial, multi-agent environment.  Nathaniel Gemelli, Robert Wright (IFSB)

• Multi-Agent Sokoban: MAS control and coordination in a computationally complex logistics domain.  James Lawton (IFSB)

• Automated Reasoning: n-Queens Completion Problem Andrew Boes (IFSB)

• Efficient Mission-based Information Retrieval   Pete Lamonica.  (IFED)

• FLEXDB: An Efficient, Scalable and Secure Peer-to-Peer XML Database.  Jim Nagy. (IFED)

• Information Extraction; Mark Zappavigna, Jeff Hudack (IFED)

• Knowledge-based inference. Mark Zappavigna, Jeff Hudack. (IFED)

• Wargame design, David Ross (IFSB)

• SimBionic for wargame development. David Ross (IFSB)

• WARCON (working title) software for Air Academy David Ross, IFSD

NEW PROJECTS (AFRL/IF-IISI)

42

Nathaniel Gemelli; Robert Wright Andrew Boes; James Lawton; Jeff Hudack;

AFRL/IF IFSBRoger Mailler (IISI)

43

Multi-Agent Systems

Multi-Agent Sokoban

I

II III

James Lawton (AFRL/IF-IFSB )

Single Agent Version

Willem van Hoeve (IISI)Anton Amoroso (IISI)Bart Selman (IISI)

44

Multi-Agent Systems

Challenges:• adversarial strategies

– selfish agents, restricted resources– more aggressively: competing teams

• cooperative strategies– collaborating agents, try to achieve

global goal• plan merging

– each agents has own plan, try to merge and avoid conflicts

• coordination– communication between agents

Real-life applications are often too complex, vague or biased for general analysis

Multi-Agent Sokoban: structured problem domain, yet captures all above challenges

46

n-Queens Completion ProblemAndrew Boes (AFRL/IF-IFSB)

Willem van Hoeve and Carla Gomes (IISI)

n-Queens problem: place n queens on an n x n chessboard such that no queen threatens another

classical AI problem

solvable in polynomial time

applications: parallel memory storage schemes, VLSI testing, traffic control, deadlock prevention,...

n-Queens completion problem: some queens are pre-placed, can we place remaining queens?

unknown complexity, likely to be NP-hard

often very difficult to solve: empty 100 x 100 board takes 0.1 sec

already 1 pre-placed queen may take more than a day!

occurs in practical problems

??

47

n-Queens Completion ProblemResearch goals:• identify complexity class• gain insight in problem structure

– phase transition from SAT to UNSAT?– hardness region?

#pre-placed queens

% SAT

#pre-placed queens

time

phase transition hardness region

48

n-Queens Completion Problem

Experimental Setup:• phase transition:

– for given n (100, 200, 500, ...) randomly generate partly filled board and try to find solution

– report % satisfiable boards for each number of pre-placed queens

• hardness region (solution time):– for given n (100, 200, 500, ...) report solution time for each

number of pre-placed queens

Hypothesis: phase transition exists and occurs at the peak in complexity

49

Efficient Mission-based Information RetrievalPete LaMonica (AFRL/IF-IFED)

Justin Hart (IISI)Claire Cardie (IISI)

• Practical Goal: Simplify information retrieval for analysts in order to improve situational awareness and simplify analysis

• Real-World Challenge: Analysts do not necessarily know what they are looking for prior to finding it. Search queries may not, then, prove informative

• Approach: Document clustering

50

Efficient Mission-based Information Retrieval

Scatter/Gather

• Browsing documents, rather than searching

• Software generates clusters (Scatter)

• User chooses clusters that they find interesting

• (Gather)

• Software then reclusters those items that the user finds interesting

51

Efficient Mission-based Information RetrievalResearch Challenge: In the conclusion of the

Scatter/Gather paper, Cutting et al. state that the obvious next direction of research should be to improve cluster quality though more accurate clustering algorithms

Question: How might Cutting et al. re-implement Scatter/Gather now, almost 15 years later?

ApproachOriginal paper focused on fast clustering algorithms, due to hardware limitations. Replacement of buckshot clustering, used in original paper, with HAC clustering may be feasible on modern hardware

52

New Projects

• Wargame design David Ross (David Schawrtz, IISI)

• SimBionic for AI modeling and implementation in wargame development.

• WARCON software Air Academy, (David Schawrtz, IISI)

• Information Extraction; Mark Zappavigna, Jeff Hudack (IFED)

• Knowledge-based inference. Mark Zappavigna, Jeff Hudack. (IFED)

IISI/IFTutorials, Seminars, Workshops, Meetings

54

IISI Tutorial Series @ AFRL/IF

Module 1 – Problem domain:• logistics, scheduling, resource

allocation, distributed problems,...

Tutorial Series I: Constraint Reasoning in Intelligent Systems

Module 2 - Modeling

• identify key components

• representation

Module 3 - Solving• search & inference techniques

(Applegate, Bixby, Chvatal and Cook, 1998)

logistics: shortest closed route through 13509 cities in USA

Module 4 – Application

• COORDINATORs: distributed plan and schedule management subject to environmental changes

Willem van Hoeve

Regular Seminar @ IFwith the active participation of

IF and IISI Researchers (bi-weekly)

IISI – AI seminar @ Cornell(weekly)

56

Setting Research Directions in AI:Knowledge Representation, Discovery, and IntegrationCraig Anken

IISI (in collaboration with AFRL/IF), 2003

Workshop 1:

Setting Research Directions in AI:Mixed Initiative Decision MakingJoe CarizzoniIISI (in collaboration with AFRL/IF) --- Fall 2003

Workshop 2:

57

• Workshop 3

Research Directions in Architectures and Systems for Cognitive Processing

Jose Martinez (Cornell)

Rich Linderman (IF)

IISI (in collaboration with AFRL/IF and CSL) --- Summer 2005

58

NESCAI: 1st North East Student Colloquium on Artificial Intelligence

28-29 April 2006, Ithaca, NY

NESCAI (North-East Student Colloquium on Artificial Intelligence) Graduate Students Conference

The primary purposes of NESCAI are:

• to foster discussion among graduate students from the region North-Eastern North America, • to provide graduate students opportunities to present their work and get feedback about it,• to allow networking among the students.

Other Resources

60

Physical Space

New IISI Lab space.

Emphasis on open design.

Space for students, postdocs, and visitors and especiallyIF researchers!

Conclusions

62

• IISI --- Benefits to Cornell– Opportunity to focus on the core IISI research areas– Develop collaboration relationships – Insights into interesting real world scenarios– Challenge problems and test beds

• IISI --- Benefits to AFRL/IF– Opportunity to build critical mass in several key research areas with

immersion in an active research environment.– Develop collaborative research ties with Cornell Researchers.– Access to Cornell facilities (library privileges, computer accounts, office

space, etc).

IISI provides an opportunity for a close collaboration between Cornell, IF, and the research community at large,

with a clear potential to further boost the research profile of both IF and Cornell.

63

U. British U. British ColumbiaColumbia

U. WashingtonU. WashingtonMicrosoft Microsoft ResearchResearch

StanfordStanford

U. Texas

U. TorontoU. Toronto

U. CorkU. Cork

U. LisbonU. Lisbon

U. U. BarcelonaBarcelona

ILOGILOG

U. PizzaU. Pizza

U. FreiburgU. Freiburg

Hebrew U. Hebrew U.

Ben-Gurion Ben-Gurion U. U.

Scientific progress byreaching acrossdisciplines,organizations, and the world.

CaltechCaltech

Economics

Computer Science

Mathematics

Operations Research

PhysicsCognitive Science

Engineering

65

10:00 - 10:05  Welcome Prof. Juris Hartmanis, Sr. Associate Dean for CIS

10:05 - 10:35  The Future of Computer Science Keynote Speaker:  Prof. John Hopcroft

10:35 - 11:10  IISI Overview Prof. Carla Gomes, IISI Director

11:10 - 11:15  Break11:15 - 11:35  The Next Generation of Automated Reasoning Methods

Prof. Bart Selman11:35 - 11:55  Research Directions in Architectures and Systems for

Cognitive Processing Prof. Jose Martinez

11:55 - 12:15  The Game Design Initiative Prof. David Schwartz

12:15 - 12:30  Discussion12:30          Lunch

Agenda