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1 IISI Overview Carla P. Gomes Apr 5, 2006.

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1 1 IISI Overview Carla P. Gomes Apr 5, 2006

2 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 Scientific Excellence

3 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 Outside Researchers 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 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 Goal Start Planning & Scheduling Software & Hardware Verification Satisfiability (A or B) (D or E or not A) Quasigroup Data Mining Fiber optics routing Air Tasking Order Information Retrieval Autonomous Agents Focus: Computational and Data Intensive Methods Automated Reasoning Modeling Uncertainty Machine Learning Information Retrieval Games

7 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!

8 Seconds until heat death of sun No. of atoms On earth Explosion of number of possible scenarios to consider Rules (Constraints) K 50K 1M 5M 20K 100K 0.5M 1M Variables , , Case complexity Car repair diagnosis Deep space mission control Chess (20 steps deep) VLSI Verification War Gaming 100K 450K Military Logistics 10010K20K100K1M Exponential Complexity (Kumar/Selman, Darpa IPTO)

9 Data intensive video1 Gigabyte/hour1000 hours scanned images 1 Megabyte each1 million images text pages3300 bytes/page300 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?

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 11 Andrew Boes – Inductive Logic Programming and 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 AFRL/IF Researchers Across Several Divisons (Curent and past IF researchers/activities ) Boosting AFRL/IF Research Profile

12 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) Collaborations With Outside Researchers 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 13 IISI research featured in: And of course lots of standard peered reviewed publications…

14 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 15 1 – Mathematical and Computational Foundations of Complex Networks Examples

16 16 The National Academies Study Network Science John Hopcroft (Co-Chair) Networks and Network Research in the 21 st 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 17 New Science of Networks NYS Electric Power Grid (Thorp,Strogatz,Watts) Cybercommunities (Automatically discovered) Kleinberg et al Network of computer scientists ReferralWeb System (Kautz and Selman) Neural network of the nematode worm C- elegans (Strogatz, Watts) Networks are pervasive Utility Patent network (3 Million patents) Gomes,Hopcroft,Lesser,Selman

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

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

20 20 Fully Automatic Cross Metric Supervised Learning Rich Caruana Learning Retrieval Functions from Implicit Feedback Thorsten Joachims

21 21 Research Themes 2 – Automated Reasoning: Complexity and Problem Structure Prof. Selman will provide an overview of this area

22 22 Formal Models. Problem structure, Backdoors H. 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 Processes K. Athreya (Cornell) HOT: Robustness vs. Fragility John 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 Strategies E. Horvitz (Micrsoft Research) H. Kautz and Y. Ruan (U. Washington) Nudelman and Shoham (Stanford) Random CSP Models C. 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 Randomization Lucian Leahu (Cornell) David Shmoys (Cornell)

23 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 Formulaglobal 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)” 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 case study: capture black king in k moves : Goal: initial position : actions and effects of White (Black) - 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 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

25 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

26 26 Research Themes 3 – Autonomous Distributed Agents, Complex Systems, and Advanced Archictetures Examples

27

28 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 PERFORMANCE 1 STRATEGY TRAJECTORY GENERATION LOCAL CONTROL DESIRED FINAL POSITIONS AND VELOCITIES, TIME TO TARGET FEASIBILITY OF REQUESTS DESIRED VELOCITIES INTERCONNECTED SYSTEMS LARGE numbers of actuators and sensors Distributed computation Limited connectivity DISTRIBUTED ARCHITECTURES: d z y u GG KK d(t, s ): disturbances z(t, s ): errors y(t, s ): sensors u(t, s ): actuators 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 29 José F. Martínez Electrical 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

30 IISI - AFRL/IF Boosting AFRL/IF Research Profile

31 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 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)

33 Examples of IISI/IF Collaborations

34 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 Opportunism Jamie Lawton (AFRL/IF-IFED) Carmel Domshlak (Cornell) Boosting AFRL/IF Research Profile Researchs Paper

35 35 Bayesian Predictive Model of an Interactive Environment 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,IFED Carla Gomes Cornell University. Michael Pittarelli SUNYIT Michael Pittarelli SUNYIT Domain: Office Security Hardware Used: 3 X10 Sensors, X10 Tranceiver, and ActiveHome  X10 CM11A computer interface VBscript X10 Motion Sensor Software Used: HomeSeer, MSBNx, and Visual Basic 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 TimeDay BreakIn Sensor  What is P(BreakIn=Yes |Day=Sunday, Time= , 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= )P(BI=yes|D=Sun, T= )P(S=On|BI=yes)  i=(yes,no) P(D=Sun)P(T= )P(BI i |D=Sun, T= )P(S=On|BI i ) Maximize Expected Utility “utility(or desirability) X probability” EU(a) =  s  states u(a,s)p(s|a) Calculations Boosting AFRL/IF Research Profile Master’s Degree

36 36 –e.g. model current available resources, psychological state of soldiers, etc.) Identify Points of Failure as Preferable Targets 3 rd Generation War-Games  System-on-System  Model effectiveness of units wrt current state units wrt current state within the system within the system Abstract System as a Network Identify Points of Failure as Preferable Targets Boosting AFRL/IF Research Profile Analysis of Network Vulnerabilities Cornell / IF Project Robert Wright (AFRL/IF-IFED) Meinolf Sellmann (Cornell) Research Paper

37 37 Boosting AFRL/IF Research Profile Collaborations with outside Researchers Controlling Computational Cost: Structure and Phase Transition Phenomena Problem Descripti on Constrain t Analysis Problem Formulat or Constraints Problem Reformulat or Constraints’ Proble m Solver Criticality Monitor / Estimator Cornell Krishna Athreya Ramon Bejar Carla Gomes Bhaskar Krishnamachari Bart Selman Steve Wicker David Shmoys Steve Strogatz IF Bob Paragi James Lawton Mathew Thomas John Spina Meinolf Sellmann Robert Wright Caltech John Doyle CMU Ryan Williams Microsoft Research Dimitris Achlioptas Eric Horvitz IBM/Hebrew Un. Scott Kikpatrick Un. Barcelona Alba Cabiscol Felip Manya Ramon Bejar Un.Washington Henry Kautz York Univ. (UK) Toby Walsh

38 38 Increasing the communication range in an ad-hoc wireless system increases the density of the network graph. Increasing the communication range in an ad-hoc wireless system increases the density of the network graph. Complexity in Ad-hoc Wireless Networks Challenge Problem: Wireless Target Tracking System Communicating Doppler radar sensors Communicating Doppler radar sensors tracking multiple targets The probability of detecting all targets undergoes a phase transition with respect to the radar and communication range. The probability of detecting all targets undergoes a phase transition with respect to the radar and communication range. Computational cost Communication range Radar range Communication cost Communication range Radar range Communication range Radar range Detection Probability (%) Generalization to Other Ad-hoc Wireless NetworkProblems Phase transition analysis provides a mechanism for identifying and quantifying the critical range of network resources needed for scalable, self-configuring, ad-hoc networks Phase transition analysis provides a mechanism for identifying and quantifying the critical range of network resources needed for scalable, self-configuring, ad-hoc networks Increasing communication range The computational and communication complexity peaks near the phase transition region.The computational and communication complexity peaks near the phase transition region. sensor target Impact: Applications

39 39 (AFRL/IF) (Cornell) 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 Boosting AFRL/IF Research Profile

40 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 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 42 Nathaniel Gemelli; Robert Wright Andrew Boes; James Lawton; Jeff Hudack; AFRL/IF IFSB Roger Mailler (IISI)

43 43 Multi-Agent Systems Multi-Agent Sokoban I IIIII James Lawton (AFRL/IF-IFSB ) Single Agent Version Willem van Hoeve (IISI) Anton Amoroso (IISI) Bart Selman (IISI)

44 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

45 45 Multi-Agent Sokoban adversarial strategies: each agent tries to collect as many rocks as possible agents must compete for rocks cooperative strategies: try to put all rocks in place as fast as possible plan merging each agent has to collect own set of rocks; plan deconfliction coordination no centralized strategy; agents need to communicate who collects which rock Technology: can we tackle the multi-agent problem with generic planning or SAT solvers?

46 46 n-Queens Completion Problem Andrew 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 47 n-Queens Completion Problem Research 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 transitionhardness region

48 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 49 Efficient Mission-based Information Retrieval Pete 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 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 51 Efficient Mission-based Information Retrieval 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? Approach Original 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 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)

53 IISI/IF Tutorials, Seminars, Workshops, Meetings

54 54 IISI Tutorial 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 cities in USA Module 4 – Application COORDINATORs: distributed plan and schedule management subject to environmental changes Willem van Hoeve

55 Regular IF with the active participation of IF and IISI Researchers (bi-weekly) IISI – AI Cornell (weekly)

56 56 Setting Research Directions in AI: Knowledge Representation, Discovery, and Integration Craig Anken IISI (in collaboration with AFRL/IF), 2003 Workshop 1: Setting Research Directions in AI: Mixed Initiative Decision Making Joe Carizzoni IISI (in collaboration with AFRL/IF) --- Fall 2003 Workshop 2:

57 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 58 NESCAI: 1 st North East Student Colloquium on Artificial Intelligence 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.

59 Other Resources

60 60 Physical Space New IISI Lab space. Emphasis on open design. Space for students, postdocs, and visitors and especially IF researchers!

61 Conclusions

62 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 63 U. British Columbia U. Washington Microsoft Research Stanford U. Texas U. Toronto U. Cork U. Lisbon U. Barcelona ILOG U. Pizza U. Freiburg Hebrew U. Ben-Gurion U. Scientific progress by reaching across disciplines, organizations, and the world. Caltech

64 Economics Computer Science Mathematics Operations Research Physics Cognitive Science Engineering

65 65 10: :05 Welcome Prof. Juris Hartmanis, Sr. Associate Dean for CIS 10: :35 The Future of Computer Science Keynote Speaker: Prof. John Hopcroft 10: :10 IISI Overview Prof. Carla Gomes, IISI Director 11: :15 Break 11: :35 The Next Generation of Automated Reasoning Methods Prof. Bart Selman 11: :55 Research Directions in Architectures and Systems for Cognitive Processing Prof. Jose Martinez 11: :15 The Game Design Initiative Prof. David Schwartz 12: :30 Discussion 12:30 Lunch Agenda


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