1 iisi overview carla p. gomes [email protected] apr 5, 2006
TRANSCRIPT
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IISI Overview
Carla P. [email protected]
Apr 5, 2006
IISI Overview
Carla P. [email protected]
Apr 5, 2006
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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
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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.
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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
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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
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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
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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…
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IISI research featured in:
And of course lots of standard peered reviewed publications…
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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
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1 – Mathematical and Computational Foundations of Complex Networks
Examples
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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
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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
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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
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Impact: Referral Web to Track Nuclear Scientists in Iraq
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Research Themes
2 – Automated Reasoning:
Complexity and Problem Structure
Prof. Selman will provide an overview of this area
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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
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• 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
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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
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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
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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
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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
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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
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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
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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
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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
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• 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
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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
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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
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• 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)
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Nathaniel Gemelli; Robert Wright Andrew Boes; James Lawton; Jeff Hudack;
AFRL/IF IFSBRoger Mailler (IISI)
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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)
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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
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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
??
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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
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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
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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
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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
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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
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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
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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)
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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:
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• 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
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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
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Physical Space
New IISI Lab space.
Emphasis on open design.
Space for students, postdocs, and visitors and especiallyIF researchers!
Conclusions
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• 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.
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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
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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