Download New Trends in Intelligent Systems

New Trends in Intelligent Systems
Dr. Jay Liebowitz
Professor
Johns Hopkins University
[email protected]
“AI: Past, Present, and Future”, AI Magazine,
25th Anniversary Issue of AAAI, Vol. 26, No. 4,
Winter 2005
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“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…
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“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
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“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
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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
PROCESS
Building and
Nurturing a
Knowledge
Sharing Culture
Systematically
Capturing and
Sharing
Critical
Knowledge
TECHNOLOGY
Creating a
Unified
Knowledge
Network
Applying AI to KM:
Expert Systems Technology
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Knowledge elicitation techniques to acquire
lessons learned (via structured/unstructured
interviews, protocol analysis, etc.)
On-line pools of expertise (rule or casebased)
Knowledge representation techniques for
developing an ontology
Intelligent Agent Technology
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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
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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.
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Help weed out rules/cases
Help look for inconsistencies within the
knowledge repository
Help filter noisy data
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
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.)
WBM 2005 Research Problem
(James Simien, NPRST, April
2005)
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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?
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Distributed processes provide tremendous opportunity for
increasing efficiencies across the enterprise.
Proposed solution:
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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)
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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.)
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Objective
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Employ data mining techniques to explore the
relationship between red light cameras and motor
vehicle accidents
Data
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FARS database
2000 – 2003 in MD and Washington, D.C.
16,840 entries
Findings
Strongest relationships are collisions with moving
objects and angle front-to-side crashes.
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The 3pm – 4pm hour and months later in the year.
Car collisions are more likely to happen on Fridays and
Sundays.
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Types of car crashes involved in running red lights are
mostly rear-end crashes and angle front-to-side
collisions.
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High relative importance of gender.
New/Repackaged Growth Areas for
AI
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Business rule engines
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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
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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)
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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
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Robotics and Computer Vision
Natural Language and Speech Understanding
Neural Networks, Genetic Algorithms, SelfOrganizing 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
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Constraint-based
Expert scheduling system shells/generic
constraint-based satisfaction problem solvers
Object/Agent-oriented, hierarchical
architectures
Hybrid intelligent system approaches
NASA Scheduling Environment
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Two of the most pressing tasks in the future
for NASA: Data capture/analysis and
scheduling
GUESS (Generically Used Expert
Scheduling System)
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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
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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
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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
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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)
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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
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i  k  tanh   c(ei, ej ) 
 j
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where,
i  delta time to shift the ith event
k  weighting constant
c(ei, ej )  constraint error between th e ith event and the jth event
Table 1 - Comparison of Scheduling Methods using Computer Generated Cases
Satisfaction
(%)
36202
Span (Days)
23974
Satisfaction
(%)
11992
Span (Days)
5985
Satisfaction
(%)
6000
Span (Days)
4000
Satisfaction
(%)
2000
Span (Days)
1000
Selected
Scheduling
Algorithm
Number of
Events
Number of
Constraints
Unscheduled
Suggestion
Tabulator
Hill Climbing
Neural Network
73
73
17.13
28.29
73
73
18.36
27.49
149
149
15.38
23.04
149
149
14.83
22.11
73
73
78.10
67.94
73
73
76.85
66.55
149
149
75.86
64.60
149
149
76.85
66.75
Table 2 - Comparison of Solution Times using Computer Generated Test Cases
Number of Events
1000
2000
4000
6000
Hill Climbing (secs)
21
40
106
200
Neural Network (secs)
8
16
41
64
Different Types of Scheduling
Applications Using GUESS
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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
Table 3 – Comparison of Scheduling Methods using Real World Test Cases
1
1.
2.
3.
4.
Scheduling
Scheduling
Scheduling
Scheduling
College.
3
Span (Days)
Satisfaction (%)
111 events
195 constraints
Satisfaction (%)
165 events
328 constraints
Span (Days)
52 events
333 constraints
Montgomery
4
College
11 events
30 constraints
Satisfaction (%)
CAM
Span (Days)
Baseball
Satisfaction (%)
Suggestion
Tabulator
Hill Climbing
Genetic
Algorithm
Neural
Network
2
Army
Span (Days)
Scheduling
Approaches
59
99.06
47
100.00
77
79.02
4
100.00
59
60
99.06
96.70
47
51
100.00
94.20
122
87
79.96
42.40
4
4
100.00
100.00
59
98.61
47
100.00
368
47.05
4
100.00
for Army battalion training exercises.
for the City of Rockville baseball games.
the arrival of military units to a deployed theatre.
Department of Computer and Information Sciences classes at Montgomery
Lessons Learned
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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
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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??
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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
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GRACIAS!!