New Trends in Intelligent Systems

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Oct 29, 2013 (3 years and 7 months ago)

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New Trends in Intelligent Systems

Dr. Jay Liebowitz

Professor

Johns Hopkins University

Jliebow1@jhu.edu

“AI: Past, Present, and Future”, AI Magazine,
25
th

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

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

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

Innovation, 2004 (Patent Applications
Filed)

Financial Times, June 8, 2005,
Thomson Scientific

1. Japan

342,726

2. US

167,183

3. South Korea


71,483

4. Germany


55,478

5. China


40,426

6. Russia


19,104

7. France


13,246

8. UK


12,245

9. Taiwan


8,684

10. Italy


4,869

11. Australia


4,142

12. Brazil


3,700

13. Canada


3,125

14. Sweden


2,272

15. Spain


2,260

…30. Ireland


300

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”

Integrative Research in Knowledge
Management


PEOPLE

TECHNOLOGY


Building and
Nurturing a
Knowledge
Sharing Culture

Systematically

Capturing and
Sharing

Critical

Knowledge

Creating a

Unified

Knowledge

Network



PROCESS

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

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)

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

Neural Networks, Genetic
Algorithms, etc.


Help weed out rules/cases


Help look for inconsistencies within the
knowledge repository


Help filter noisy data

--
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


KM Research Issues

--
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.)

KM Research Issues (cont.)

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.

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)

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)

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



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.

Findings

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.)


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)

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

Steady Growth


Robotics and Computer Vision


Natural Language and Speech Understanding


Neural Networks, Genetic Algorithms, Self
-
Organizing Maps


Intelligent/Multi
-
Agents


Fuzzy Logic



Papers Are Being Written

Worldwide…

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)

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

NASA Scheduling Environment


Two of the most pressing tasks in the future
for NASA: Data capture/analysis and
scheduling

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

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

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

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

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:


Equation Used for NN
Connections

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

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

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.)

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?

THE END


GRACIAS!!