New Trends in Intelligent Systems

1. New Trends in Intelligent Systems Dr. Jay Liebowitz Professor Johns Hopkins University Jliebow1@jhu.edu

2. “AI: Past, Present, and Future”, AI Magazine, 25th Anniversary Issue of AAAI, Vol. 26, No. 4, Winter 2005 • “We are a scientific society devoted to the study of artificial intelligence”…Allen Newell, The First AAAI President’s Message, 1980 • “As AI matures, its focus is shifting from inward-looking to outward-looking. Some of the new concerns of the field are social awareness, networking, cross-disciplinarity, globalization, and open access”…Alan Mackworth, Current AAAI President, July 2005

3. The Next 50 Years… • “The Semantic Web is to KR as the Web is to hypertext”…James Hendler, U. of Maryland • “AI has not yet succeeded in its most fundamental ambitions. Our systems are fragile when outside their carefully circumscribed domains”…Rod Brooks, MIT • “Reasoning programs still exhibit little common sense”…Patrick Winston, MIT

4. More Quotes • “Integrative research will be particularly challenging for research students. To do it, they must master a wide range of formal techniques and understand not just the mathematical details but also their place in overall accounts of intelligent behavior”…Haym Hirsh, Rutgers University • “Another reason for the slow progress is the fragmentation of AI”…Aaron Sloman, U. of Birmingham

5. Innovation, 2004 (Patent Applications Filed)—Financial Times, June 8, 2005, Thomson Scientific

6. Patents Filed by Sector in 2004 (Spain); Financial Times, Oct. 26, 2005, Thomson Scientific • 48%: Chemicals, materials and instrumentation • 14%: Telecom, IT, and electronics • 13%: Food and agriculture • 11%: Automotive and transport • 10%: Pharmaceutical and medical • 4%: Energy and power • “Biotechnology: Spanish research highly rated in agro-industry, medicine, and alternative fuels” • “Spanish biotechnology is growing 4 times faster than the average of the European 15” • “Spain accounts for 4% of all biotech research published in the world” • “Sluggish integration of IT solutions into daily life”

7. Integrative Research in Knowledge Management PEOPLE PROCESS Building and Nurturing a Knowledge Sharing Culture Systematically Capturing and Sharing Critical Knowledge TECHNOLOGY Creating a Unified Knowledge Network

8. Applying AI to KM:Expert Systems Technology • Knowledge elicitation techniques to acquire lessons learned (via structured/unstructured interviews, protocol analysis, etc.) • On-line pools of expertise (rule or case-based) • Knowledge representation techniques for developing an ontology

9. Intelligent Agent Technology • Intelligent multi-agent systems with learning capabilities to help users in responding to their questions • Searching and filtering tools • User profiling and classification tools • Agent-Oriented Knowledge Management AAAI Symposium (Stanford University)

10. Data Mining and Knowledge Discovery Techniques • Inductively determine relationships/rules for further developing the KM system • Help deduce user profiles for better targeting the KM system • Help generate new cases

11. Neural Networks, Genetic Algorithms, etc. • Help weed out rules/cases • Help look for inconsistencies within the knowledge repository • Help filter noisy data

12. KM Research Issues --Develop “active” analysis and dissemination techniques for knowledge sharing and searching via “intelligent” agent technology (i.e., where “learning” takes place) --Apply knowledge discovery techniques (e.g., data/text mining, neural networks, etc.) for mining knowledge bases/repositories --Improve query capabilities through natural language understanding techniques --Develop metrics for measuring value-added benefits of knowledge management --Develop standardized methodologies for knowledge management development and knowledge audits --Provide improved techniques for performing knowledge mapping and building knowledge taxonomies/ontologies

13. KM Research Issues (cont.) --Develop techniques for building collaborative knowledge bases --Develop improved tools for capturing knowledge from various media (look at multimedia mining to induce relationships among images, videos, graphics, text, etc.) --Develop techniques for integrating databases to avoid stovepiping, functional silos --Build improved software tools for developing and nurturing communities of practice --Develop techniques for categorizing, synthesizing, and summarizing lessons learned (look at text summarization techniques) --Explore ways to improve human-agent collaboration --Explore human language technologies for KM (input analysis, extraction, question-answer, translation, etc.)

14. WBM 2005 Research Problem (James Simien, NPRST, April 2005) • How to provide IT support for the Navy’s future distributed business processes involving sailors and commands as outlined in the Navy’s Human Capital Strategy? • Distributed processes provide tremendous opportunity for increasing efficiencies across the enterprise. • Proposed solution: • Develop a Multi-Agent System incorporating software agents to intelligently assist Users in performing tasks.

15. Major Focus in FY05 (Simien, 2005) • Development of a formal methodology for knowledge acquisition and management for Navy’s business rules used in the assignment process (Liebowitz et al., 2005) • Exploring use of genetic algorithms in Sailor job matching • Development of agent bi-lateral negotiation for those assignment matches that occur outside of the general matching process • Experimentation with multiple forms of distributed architecture to determine performance and scalability (Liebowitz et al., 2004; 2005)

16. Next Generation of Data Mining Applications (M. Kantardzic & J. Zurada, IEEE Press, 2005) • Current data warehouses in the terabyte range (FedEx, UPS, Wal-Mart, Royal Dutch/Shell Group, etc.) • Diversity of data (multimedia data) • Diversity of algorithms (GAs, fuzzy sets, etc.) • Diversity of infrastructures for data mining applications (web-based services and grid architectures) • Diversity of application domains (Internet-based web mining, text mining, on-line images and video stream mining) • Emphasis on security and privacy aspects of data mining (protect data usually in a distributed environment)

17. Red Light Cameras and Motor Vehicle Accidents (Solomon, Nguyen, Liebowitz, Agresti, 2005; funded through GEICO Found.) • Objective • Employ data mining techniques to explore the relationship between red light cameras and motor vehicle accidents • Data • FARS database • 2000 – 2003 in MD and Washington, D.C. • 16,840 entries

18. Findings • Strongest relationships are collisions with moving objects and angle front-to-side crashes. • The 3pm – 4pm hour and months later in the year. • Car collisions are more likely to happen on Fridays and Sundays. • Types of car crashes involved in running red lights are mostly rear-end crashes and angle front-to-side collisions. • High relative importance of gender.

19. New/Repackaged Growth Areas for AI • Business rule engines • The acquisition of RulesPower assets allows Fair Isaac's customers a higher-performance business rule engine (BRE) option that leverages the RETE III algorithm (September 27, 2005; Gartner Group Report). • Annual Business Rules Conference (November 2006 in Washington, D.C.)

20. Another Area for Growth • Strategic Intelligence: The Synergy of Knowledge Management, Business Intelligence, and Competitive Intelligence (see Liebowitz, J., Strategic Intelligence book, Auerbach Publishing/Taylor & Francis, NY, April 20, 2006)

21. Continued Growth in Discovery Informatics (Knowledge Discovery) • New curricula at the undergraduate level at College of Charleston (Discovery Informatics), Washington & Jefferson (Data Discovery), etc. • New Graduate Certificate in Competitive Intelligence (Johns Hopkins University; Jay Liebowitz, Program Director) • SCIP (Society of CI Professionals—www.scip.org)—CI analysts • Web and Text Mining

22. Steady Growth • Robotics and Computer Vision • Natural Language and Speech Understanding • Neural Networks, Genetic Algorithms, Self-Organizing Maps • Intelligent/Multi-Agents • Fuzzy Logic

23. Papers Are Being WrittenWorldwide… EXPERT SYSTEMS WITH APPLICATIONS is a refereed international journal whose focus is on exchanging information relating to expert and intelligent systems applied in industry, government, and universities worldwide. Published by Elsevier; Entering Volumes 30 & 31 (2006)

24. Trends in Intelligent Scheduling Systems • Constraint-based • Expert scheduling system shells/generic constraint-based satisfaction problem solvers • Object/Agent-oriented, hierarchical architectures • Hybrid intelligent system approaches

25. NASA Scheduling Environment • Two of the most pressing tasks in the future for NASA: Data capture/analysis and scheduling

26. GUESS (Generically Used Expert Scheduling System) • A generic intelligent scheduling tool to aid the human scheduler and to keep him/her in the loop • Programmed in Visual C++ and runs on an IBM PC Windows environment (about 9,500 lines of code) • 2.5 year effort

27. Features of GUESS • OOPS feature of GUESS is that classes represent various abstractions of scheduling objects, such as events, constraints, resources, etc. • Resources--binary, depletable, group, etc. • Constraints--before, after, during, notduring, startswith, endswith, meta, etc. • Repair-based scheduling

28. Major Scheduling Approaches in GUESS • Suggestion Tabulator: uses suggestions derived from the constraints • Hill climbing algorithm • Genetic algorithm--used EOS, a C++ class library for creating GAs • Hopfield neural network algorithm

29. Neural Networks in Scheduling • The existing work demonstrated that scheduling problems can be attacked and appropriately solved by NNs • The majority of the artificial NNs proposed for scheduling were based on the Hopfield network (an optimizer) • Most of the neural networks developed for scheduling have been in manufacturing domains

30. Hopfield Network (NN Connections) • Each of the constraints on an event produces an error signal. The error signal is chosen to cause the event to move in the correct direction to produce a "satisfied" schedule. The errors on a given event induced by the constraints are summed together and then passed through a sigmoid function. The output of the sigmoid function f(x) is used to shift the begin and end times of the event to drive the schedule to a more satisfied state. Several different sigmoid functions were tried. The most promising was f(x) = tanh (x). This yielded the following equation for the neural network:

31. Equation Used for NN Connections

33. Different Types of Scheduling Applications Using GUESS • City of Rockville Baseball Scheduling • Army strategic problem of scheduling arrival of units in a deployed theater • Army operational problem of scheduling Army battalion training exercises • College course timetabling at MC • NASA satellite scheduling

36. Lessons Learned • Don’t underestimate the amount of time required for the user interface design • Scheduling is a difficult (but pervasive) problem • Nothing goes according to schedule--so have efficient ways of handling rescheduling

37. Future Work • Develop database links for ease of inputting • Classify different scheduling types and models and incorporate them into GUESS • Expand the number of scheduling methods (OR+AI, etc.)

38. Questions to Ponder?? • Will AI ever achieve natural/human intelligence? • Should we have called our field IA (Intelligence Amplification) versus AI, since most of the AI applications are still for decision support? • Have we found the “killer application” for AI yet? • Will AI survive as a field or discipline?

39. THE END • GRACIAS!!