IISI Overview Carla P. Gomes gomes@cs.cornell Apr 5, 2006

1. IISI OverviewCarla P. Gomesgomes@cs.cornell.eduApr 5, 2006

2. Boosting AFRL/IF Research Profile Mission Scientific Excellence 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. Scientific Excellence Boosting AFRL/IF research involvement

3. IISI AFRL/IF Cornell Visitors Outside Researchers IISI Model IISI is modeled after successful national research institutes such as the DIMACS center for Discrete Mathematics and the Aspen Center for Physics. • 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 Research Interactions

4. IISI Scientific Advisory Board Dr. Robert Constable --- Dean, Faculty of Computing and Information Sciences, Cornell Dr. Juris Hartmanis --- Sr. Associate Dean for Computing and Information Sciences, Cornell Major Amy Magnus, Ph.D. --- Progr. Manag., AFOSR Dr. John Bay --- Chief Scientist, AFRL/IF Ms. Julie Brichacek and Mr. Charles Messenger- Branch Chiefs, AFRL/IF

5. Research Agenda

6. Design and Study of Intelligent Systems Satisfiability Start Goal (A or B) (D or E or notA) Data Mining Quasigroup Autonomous Agents Software & Hardware Verification Fiber optics routing Automated Reasoning Modeling Uncertainty Machine LearningInformation Retrieval Information Retrieval Focus: Computational and Data Intensive Methods Planning & Scheduling Games Air Tasking Order

7. Compute Intensive Many computational tasks, such as planning, scheduling, negotiation, canin principle be reduced toan exploration of a large set of all possible scenarios. Try all possible schedules, try all possible plans etc. Problem: combinatorial explosion!

8. Complexity Exponential Explosion of number of possible scenarios to consider 1M 5M War Gaming 10301,020 0.5M 1M VLSI Verification 10150,500 Case complexity 100K 450K Military Logistics 106020 20K 100K Chess (20 steps deep) 103010 10K 50K Deep space mission control No. of atoms On earth 100 200 Car repair diagnosis 1047 1030 Seconds until heat death of sun Variables 100 10K 20K 100K 1M Rules (Constraints) (Kumar/Selman, Darpa IPTO)

9. Data intensive What can we store with 1 Terabyte? Storage for $200 Yr ’05, 1 Terabyte for $200. Wal-Martcustomer 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.

10. 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. Boosting AFRL/IF Research Profile AFRL/IF Researchers Across Several Divisons(Curent and past IF researchers/activities ) Andrew Boes – Inductive Logic Programmingand reasoning and Reasoning Joe Carozzoni – Mixed Initiative Planning and Agent Systems Jerry Dussault – Decision Theory Nathan Gemelli - Asynchronous Chess Jeff Hudack - Information Extraction / Knowledge Representation James Lawton - Agent technology Jim Nagy - A Peer to peer Databases Mark 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 complexity Louis Pochet: Active memory systems Nancy Roberts:Bayesian predictive model of an interactive environment/AFRL Virtual World Peter 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  

12. Collaborations With Outside Researchers IISI Visitors - Summer 2001/2003/2004/2005 • 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) • 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) 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 StudyNetwork 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. Networks are pervasive New Science of Networks Utility Patent network 1972-1999 (3 Million patents) Gomes,Hopcroft,Lesser,Selman Neural network of the nematode worm C- elegans (Strogatz, Watts) NYS Electric Power Grid (Thorp,Strogatz,Watts) Network of computer scientists ReferralWeb System (Kautz and Selman) Cybercommunities (Automatically discovered) Kleinberg et al

18. Discovering Natural Communities in Large Linked Networks John Hopcroft, Bart Selman, Omar Khan and Brian Kulis Proc. National Academy Of Sciences Motivation Results are structured… Huge Data sets, Readily Available Black Box/Oracle (Data Miner) Genome Data … but how well? The Internet Data and Results Random Graphs NEC CiteSeer Citation graph (no text) Hierarchical Structure CiteSeer Structure compared to Random Structure RG1: Same degree structure NO NATURAL COMMUNITIES Natural communities – appear in many randomized runs Natural Community Tree RG2: Adjacency Matrix with embedded Structure NATURAL COMMUNITIES?

19. Impact: Referral Web to Track Nuclear Scientists in Iraq

20. Research Themes 2 – Automated Reasoning: Complexity and Problem Structure Prof. Selman will provide an overview of this area

21. Results presented at: Heavy-tailed Phenomena in Computational Processes C. Gomes (Cornell) B. Selman (Cornell) Annual meeting (2005). Connections and Collaborations Branching Processes K. Athreya (Cornell) Power laws vs. Small-world S. Strogatz (Cornell) T. Walsh (U. New South Wales) Approximations and Randomization Lucian Leahu (Cornell) David Shmoys (Cornell) Random CSP Models C. Fernandez, M. Valls (U. Lleida) C. Bessiere (LIRMM-CNRS) C. Moore (U. New Mexico) Formal Models. Problem structure, Backdoors H. Chen (Cornell) John Hopcroft (Cornell) Jon Kleinberg (Cornell) R. Williams (CMU) Joerg Hoffman (Max-Planck Inst.) HOT: Robustness vs. Fragility John Doyle (Caltech) Walter Willinger (AT&T Labs) Learning Dynamic Restart Strategies E. Horvitz (Micrsoft Research) H. Kautz and Y. Ruan (U. Washington) Nudelman and Shoham (Stanford) Information Theory: S. Wicker (Cornell)

22. Boosting Reasoning Technology Through Randomization, Structure Discovery, and Hybrid Strategies natural search space illegal search space : initial position : actions and effects of White (Black) : Goal Quantified Boolean Formula global indicator variable global indicator (z) value ? Conditional monitor QB solver backtrack if z is up True or False Problem Solving Strategies Using Quantified Boolean Formulas • 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 • 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)” • 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. The problem: case study: capture black king in k moves • variables : : moves and locations at step i • axioms : • 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”. The solution: Capture is PSPACE-Complete Help capture (when all universals are relaxed) is NP-Complete - Approach: during search, relax subsets of universal quantifiers (between “capture” and “help capture”), and check the reachability of the Goal The results: Performance of QB solvers • 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 • Carlos Ansotegui • Robert Constable • Carla Gomes • Christoph Kreitz • Bart Selman Time (secs): ‘-’ did not complete in 20,000 seconds; ‘*’ formula too large to execute

23. Problem Solving Strategies Using Quantified Boolean FormulasQBF • New results: • CNF and DNF formulations for QBF (submitted to SAT 06) • Automated generation of so-called Streamlining constraints (submitted to AAI06)

24. Operations Research Techniques in Constraint Programming Willem-Jan van Hoeve Combinatorial Problems: logistics, circuit verification, scheduling, … solve solve solve • 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

25. Research Themes 3 – Autonomous Distributed Agents, Complex Systems, and Advanced Archictetures Examples

26. GDIAC: The Game Design Initiative at CornellDavid Shwartz gdiac.cis.cornell.edu • Research Projects: • ►Wargame development and design►Game Library►Curricula►Outreach

27. z d G G y u K K 1 PERFORMANCE COMPLEXITY Raff D Andrea HIERARCHICAL DECOMPOSITION Control of Complex Systems OBJECTIVE: Develop hierarchy-based tools for designing complex, multi-asset systems in uncertain and adversarial environments • System level decomposition • Bottom up design • Model Simplification • Uncertainty Propagation • Heuristics and Verification Relaxation, Restriction EXAMPLE: ROBOCUP DESIRED FINAL POSITIONS ANDVELOCITIES, TIME TO TARGET DESIREDVELOCITIES STRATEGY TRAJECTORYGENERATION LOCALCONTROL FEASIBILITY OF REQUESTS INTERCONNECTED SYSTEMS • Vehicle platoons • Finite difference approximations of PDEs • Cellular automata, artificial life, etc. • Behavior of groups, swarm intelligence, etc. DISTRIBUTED ARCHITECTURES: CHALLENGES: • LARGE numbers of actuators and sensors • Distributed computation • Limited connectivity s d(t, ): disturbances s z(t, ): errors s y(t, ): sensors s u(t, ): actuators SEMI-DEFINITE PROGRAMMING APPROACH:

28. 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

29. Boosting AFRL/IF Research Profile IISI - AFRL/IF

30. 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

31. 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)

32. Examples of IISI/IF Collaborations

33. Boosting AFRL/IF Research Profile Working on PhD Researchs Paper Multi-Agent OpportunismJamie Lawton (AFRL/IF-IFED)Carmel Domshlak (Cornell) • 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”)

34. Boosting AFRL/IF Research Profile Master’s Degree Day Time BreakIn Sensor 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.) Bayesian Predictive Model of an Interactive Environment Nancy Roberts - AFRL/IF,IFED Carla Gomes Cornell University. Michael Pittarelli SUNYIT Domain: Office Security Software Used: HomeSeer, MSBNx, and Visual Basic VBscript X10 Motion Sensor Hardware Used: 3 X10 Sensors, X10 Tranceiver, and ActiveHome X10 CM11A computer interface VBscript • 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 Calculations • 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) = sstates u(a,s)p(s|a)

35. Boosting AFRL/IF Research Profile Research Paper Analysis of Network VulnerabilitiesCornell / IF ProjectRobert Wright (AFRL/IF-IFED)Meinolf Sellmann (Cornell) 3rd Generation War-Games • e.g. model current available resources, psychological state of soldiers, etc.) • Identify Points of Failure as Preferable Targets • System-on-System • Model effectiveness of units wrt current state within the system Abstract System as a Network Identify Points of Failure as Preferable Targets

36. Impact: Applications Detection Probability (%) Radar range Communication range Communication cost Radar range Communication range Complexity in Ad-hoc Wireless Networks sensor target Generalization to Other Ad-hoc Wireless NetworkProblems • Challenge Problem: • Wireless Target Tracking System • Communicating Doppler radar sensors • tracking multiple targets • The probability of detecting all targets undergoes aphase transitionwith respect to theradar and communication range. Increasing communication range • Increasing the communication range in an ad-hoc wireless system increases the density of the network graph. • The computational and communication complexity peaks near the phase transition region. • Phase transition analysis provides a mechanism for identifying and quantifying the critical range of network resources needed for scalable, self-configuring, ad-hoc networks Computational cost Radar range Communication range

37. Boosting AFRL/IF Research Profile Probabilistic Target Tracking with a Network of Distributed Sensor Agents Matthew Thomas (AFRL/IF)Bhaskar Krishnamachari(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

38. 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 3D Virtual World Nancy Roberts (AFRL-IFED), Margaret Corbit and Dan White (Cornell), AFRL Virtual World Amphitheatre Hall of History

39. NEW PROJECTS (AFRL/IF-IISI) • 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

40. Realtime Reasoning in Competitive Evironments Asynchronous Chess • Nathaniel Gemelli;Robert Wright • Andrew Boes; James Lawton;Jeff Hudack; • AFRL/IF IFSB • Roger Mailler (IISI)

41. Multi-Agent Systems I II III Multi-Agent Sokoban James Lawton (AFRL/IF-IFSB) Single Agent Version Willem van Hoeve (IISI) Anton Amoroso (IISI) Bart Selman (IISI)

42. 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

43. 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 ?

44. n-Queens Completion Problem Research goals: • identify complexity class • gain insight in problem structure • phase transition from SAT to UNSAT? • hardness region? phase transition hardness region time % SAT #pre-placed queens #pre-placed queens

45. 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

46. 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

47. 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 Efficient Mission-based Information Retrieval

48. Research 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 Efficient Mission-based Information Retrieval

49. 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)

50. IISI/IFTutorials, Seminars, Workshops, Meetings