Title: Homeland Security
1Homeland Security
- Obviously since 9/11, homeland security has been
brought to the forefront of public concern and
national research - in AI, there are numerous applications
- The Dept of HS has identified the following
problems as critical - intelligence and warning surveillance,
monitoring, detection of deception - border and transportation security traveler
identification, vehicle identification location
and tracking - domestic counterterrorism tying crimes at the
local/state/federal level to terrorist cells and
organizations, includes tracking organized crime - protection of key assets similar to
transportation security, here the targets are
fixed, security might be aided through
camera-based surveillance and recording, this can
include the Internet and web sites as assets - defending against catastrophic terrorism
guarding against weapons of mass destruction
being brought into the country, tracking such
events around the world - emergency preparedness and response includes
infrastructure to accomplish this such as
wireless networks, information sources, rescue
robots
2The Dark Web
- Goal collect relevant web pages from terrorism
web sites and make them accessible for specific
terrorism-related queries and inferences - Starting from reliable URLs, use a web spider to
accumulate related web pages - link analysis and human input are both applied to
prune irrelevant pages
- Automatically collect the pages from the URLs and
annotate the pages (including those with
multimedia and multilingual content) - Content analysis performed by humans using domain
specific attributes of interest
3Clustering on the Dark Web
Clustering and classification algorithms are run
on web site data to date, much of the data is
manually annotated from the web sites before
clustering/classification algorithms are
run Here are some results
Middle East terror organizations sites
Domestic web sites of US hate groups
Clustering performed using statistical
hierarchical clustering, features include those
derived through social analysis, link analysis,
and patterns derived through groups of links and
sites
4Dark Web Continued
- With web pages annotated, the Dark Web can now be
used for document retrieval based on search
queries (including cross-lingual searches) - SOM mapping
- link analysis
- content analysis
- Activity scale(c, d)
- c cluster
- d attribute of interest
- wi,j 1 if attribute i is in site j, 0 otherwise
- m total number of web sites, n total number
of attributes
5Dynamic Interoperability of 1st Responders
- Test implemented on Philadelphia Area Urban
Wireless Network - Idea is to test ability of mobile communication
network and devices (PDA, laptop, tablet
computers, etc) to register agents and services
for first responders who might be mobile as well
to acquire information during an emergency - Need
- service registration
- service discovery
- service choreography
- Solution use OWL-S service registry for manets
(mobile ad hoc networks)
Simulated Manet topology Agent x is looking for
a host with service e but x is only aware of
hosts A, B and D
6Experiment
- Researchers ran two experiments, first on a
simulation and second on a small portion of a
network in Philly using mobile devices (in about
a 2 block area) - agents had various options of when to consult
host registries and how to migrate to another
host (as packets or bundles) - Findings
- cross-layered design where agents reason about
network and service dynamics worked well - using static lists, performing random walks,
using inertia (following the path of the last
known location of a service) all had problems
leading to poor performance from either too many
hops or not succeeding in locating the desired
service - p-early binding agent would consult local
hosts registry to identify nearest host that
contained needed service and would migrate to
that host as a packet - b-early binding agent would consult local
hosts registry to identify nearest host that
contained needed service and would migrate to
that host as a bundle - late binding agent would consult a local
registry at each step as it migrated, always
selecting the nearest host with the available
service
7Ontology for Bioterrorism Surveillance
- This research deals with an ontology and problem
solving agents to monitor health threats - the system, BioStorm, includes a central ontology
of domain-specific and task-specific knowledge
for reasoning about bioterrorism events and
two-tier medication to reconcile heterogeneous
data - problem solving methods include statistical
(counting) approaches for low-level analysis,
knowledge-based approaches for qualitative
reasoning, disease-specific knowledge reasoners
and temporal reasoners
8Surveillance of San Francisco 911 Emergency
Dispatch Data
9Deception Detection
- Analyze hand and face motion and orientation (via
video) to determine degree of truth or deception
during an interview - Capture video features
- head position, head velocity
- left/right hand position, left/right hand
velocity - distance from left/right hand to head, from hand
to hand - Use features as input to classifier on four
classifiers
- trained neural network
- support vector machine (perform non-linear
classification using linear classification
techniques) - alternative decision trees (an ensemble)
- discriminant analysis
- NN and SVM had highest accuracy in experiments
10Authorship Identification
- Determine who the author was of some Internet web
forum message in Arabic or English - Attributes
- lexical word (or character for Arabic) choice
- syntactic choice of grammar
- structural organization layout of the message
- content-specific topic/domain
- Approaches were to perform classification using
C4.5 and SVMs - English identification uses 301 features used
from a test set (87 lexical, 158 syntactic, 45
structural and 11 content-specific) - Arabic identification uses 418 features used from
a test set (79 lexical, 262 syntactic, 62
structural and 15 content-specific) - used web spider to collect test set of documents
from various Internet forums - Results are given below
- feature sets F1, F2, F3, F4 are the use of
lexical, lexical, syntactic, structural and
content-specific respectively
Notice how accuracy improves significantly as
higher level features are added
11Arabic Feature Set
Elongation words 10 characters are rare in
Arabic, so words that were overly long
were considered to be no more than 10 characters
so that word length distribution was not distorted
12Wearable AI
- Wearable computer hardware is becoming more
prevalent in society - we want to enhance the hardware with software
that can supply a variety of AI-like services - The approach is called humanistic intelligence
(HI) - HI includes the human in the processing such that
the human is not the instigator of the process
but the beneficiary of the results of the process - HI embodies three operational modes
- Constancy the HI device is always operational
(no sleep mode) with information always being
projected (unlike say a wrist watch where you
have to look at it) - Augmentation the HI augments the humans
performance by doing tasks by itself and
presenting the results to the human - Mediation the HI encapsulates the human, that
is, the human becomes part of the apparatus for
instance by wearing special purpose glasses or
headphones (but the HI does not enclose the
human) - These systems should be unmonopolizing,
unrestrictive, observable, controllable,
attentive, communicative
13HI Applications
- Filtering out unwanted information and alerting
- imagine specialized glasses that hide
advertisements or replace the content with
meaningful information (e.g., billboards replaced
with news) - alerting a driver of an approaching siren
- Recording perceptions
- example uses are to have the wearable record hand
motions while the user plays piano or to record
foot motions while the user dances to capture
choreography, or for computer animation models - Military applications
- aiming missiles or making menu selections in an
airplane so that the pilot doesnt have to move
his hands from the controls - reconnaissance by tracking soldiers in the field,
seeing what they are seeing - Minimizing distractions
- using on-board computing to determine what a
distraction might be to you and to prevent it
from arising or blocking it out - Helping the disabled
- HI hearing aids, HI glasses for filtering,
internal HI for medication delivery, reminding
and monitoring systems for the elderly
14Beyond AI Wearables
- As the figure below shows, these devices may be
more intimately wound with the human body - We are currently attaching ID/GPS mechanisms to
children and animals - Machine-based tattoos are currently being
researched
- What about underneath the skin?
- Nano-technology
- Hardware inside the human body (artificial
hearts, prosthetic device interfaces, etc)
15Sensor Interpretation
- Given sensor readings, interpret what they are
telling you - come to an understanding of the state of the
device or environment - robot/autonomous vehicle, power plant or factory,
automobile engine, biological weapons sensors,
etc - given the readings, what conclusions can be
drawn? - this is typical a process of credit assignment
finding the cause of the sensor readings - in a simple situation, the reading(s) points to a
simple conclusion that is, a one-to-one mapping
from sensor value to conclusion - but more often, each sensor reading is explained
by a different hypothesis, and the hypotheses
have to be combined into a single, coherent and
consistent explanation - this can be accomplished through abduction
- many approaches have been tried
knowledge-based, Bayesian probabilities or
Bayesian network, hidden Markov model,
logic-based, case-based, even neural network
approaches
16Abduction Example
- Given a set of findings, generate some hypotheses
that could potential explain the findings - Evaluate the hypotheses (how plausible are they?)
- Select those hypotheses that best explain the
findings while maintaining a consistent and
coherent explanation - Best includes such factors as maximally
plausible, simplest (most parsimonious), most
complete
Edges indicate which data a hypothesis can
explain The dotted line indicates mutually
incompatible hypotheses
17Sensor Fusion for Autonomous Car
- Fusion coming to a high-level understanding of
the sensor input values and how this relates to
(or threatens) goal(s) - Obstacle detection
- Environmental state (road conditions in this
case) - Sensor malfunctions
- Sensors and sensor interpretation are often
distributed, but sensor fusion is performed by a
central controller/reasoner
Figure of the Alice Land-based Autonomous Vehicle
18Automated Highways
- Features
- Provide guidance information for cooperative
(autonomous) vehicles - Monitor and detect non-cooperative vehicles and
obstacles - Plan optimum traffic flow
- Architecture
- Network of short-range hi-resolution radar
sensors on elevated poles
- Additional equipment in vehicles (transponders
for instance for location and identification) - Sensors on the road for road conditions and on
the vehicles for traction information - Sensors for other obstacles (e.g., animals)
- Computer network
- Roadway blocked off from sidewalk and pedestrian
traffic
19Example AV Control Architecture
20Automated Vehicles
- Three different forms
- Remote controlled largely of no interest since
it involves little or no AI - Semi-autonomous decision making performed
remotely (by humans) but the vehicle must still
plan paths and avoid obstacles - Autonomous all aspects controlled by computer
(usually on-board) - path planning, sensor interpretation, obstacle
avoidance - goal/mission directed planning (achieving
objectives while maintaining safety) - failure handling, on-board diagnostics
- Sensors depend on the type of vehicle/robot
- Cameras are difficult unless used merely to
determine obstacle/no obstacle - Radar, ladar (laser radar), sonar, microphones,
ultrasound, other - A fully autonomous vehicle will employ a number
of AI techniques - Mathematical modeling for path planning
- Rules or case-based reasoning for failure
handling and goal planning - Bayesian probabilities, HMMs or neural networks
for low level sensor interpretation - Some form of abduction (possibly Bayesian) for
sensor interpretation - Distributed control throughout the robot with a
centralized controller to issue commands and make
high level decisions
21Autonomous Land Vehicles
- Types
- Mobile robots (indoor and level surface robots,
outdoor/terrain robots) - Autonomous automobiles
- Indoor robots tend to be easier to implement
- environment is benevolent
- most interaction/obstacles involve walls and
furniture (static) and humans (the robot is
usually programmed to stop and let the human
pass) - Autonomous cars have an even surface to work on
but because of other drivers and the speed, they
are very challenging its easier if we can
assume all cars are autonomous - then you dont have unexpected vehicle movements
and all vehicles can directly communicate to each
other - Terrain vehicles have to deal with different
types of surfaces and grades making the actual
movement difficult and it requires more complex
path planning - terrain vehicles are often used for exploration
and might be used for warfare in which case they
also have to worry about enemy fire
22Failure Handling No Progress Forward
23Other Autonomous Vehicles
- Water-based (untethered) vehicles
- there are fewer obstacles to worry about so in
fact these vehicles are easier to construct
however, navigation is more problematic - fewer (or no) landmarks, and currents can cause
the vehicle to move in unanticipated ways - these vehicles rely on sonar more than cameras
- an underwater (submarine) vehicle deals with
depth as well as coordinates - the greater range of mobility could make path
planning easier in spite of it being more complex - failure handling could be as simple as surfacing
and moving in a circle, sending out a signal that
a problem has arisen - most forms of UVs (underwater vehicles) are
remote controlled as there is less call for
autonomous UVs than land-based vehicles - Flying vehicles (probably the least researched)
- although they are similar to underwater vehicles
in that they have current (air current) and depth
to worry about, but fewer obstacles - most flying vehicles are remote controlled
(predator drones) but autonomous helicopters are
being researched
24Autonomous Space Probes
- To date, space probes (including the Mars rovers)
have been semi-autonomous at best - Mars rovers are partially controlled from Earth,
but because of the time lag, path planning and
failure handling is performed on-board - other probes (Galileo, Cassini) are largely
remote controlled with only on-board diagnosis
and other simple tasks done on-board - for instance, rotating the probe to point in the
desired direction - NASA wants more autonomy in their probes
- their first attempt at this was the 2003 Earth
Observing-1 satellite with onboard continuous
planning, robust task and goal-based execution,
and onboard machine learning and pattern
recognition - to perform onboard decision-making to detect,
analyze, and respond to science events, and to
downlink only the highest value science data - to manage event-driven processing/low-level
autonomy, such as housekeeping routines - to continuously schedule/plan, execute and replan
for various actions as downlinking
25Example
- Here is an image from the EO-1 satellite
- As can be seen, the satellites software
automatically performs a variety of software
tasks to detect events via image processing and
feature detection, and plan to take a new image
from the results - EO-1s science algorithms perform several
operations - analyze image data
- image feature detection including cloud detection
- detect trigger conditions of science events and
changes relative to previous observations - on-board image editing
26Other Uses of AI in Space/NASA
- Planning/scheduling
- Manned mission planning
- Multi-agent planning, distributed and shared
scheduling, adaptive planning - Planning for geological surveys (for rovers)
- Scheduling for observations (e.g., telescope
usage) - Deliberation vs. reactive control/planning
- Plan recovery (failure handling)
- Conflict resolution for multiple spacecraft
missions - Life support monitoring and control
- Simulations of life support systems
- On-board diagnosis and repair
- Safety (for humans, systems, rovers, probes)
- Science
- Weather forecasting and warning, disaster
assessment, disaster reduction (for floods) - Feature detection from autonomous probes (e.g.,
crater detection by satellites) - Other forms of visual recognition and discovery
27Smart Environments
- Sometimes referred to as smart rooms
- although these can be any environment (autonomous
highway for instance) - Components collection of computer(s), sensors,
networks, AI and other software, actuators (or
robots) to control devices - Goal the environment can modify itself based on
user preferences or goals and safety concerns - A smart building might monitor for break-ins,
fire, flood, alert people to problems, control
traffic (such as elevator usage), etc - A smart house might alter the A/C when people are
away, adjust lighting, volume, perform household
chores (starting/stopping the oven, turn on the
dishwasher), determine when (or if) to run the
sprinkler system for the lawn - A smart restaurant might seat people
automatically, have robot waiters, automatically
order food stock as items are getting low (but
not actually cook anything!)
28Environment 1 Smart City Block
29Environment 2 Smart Highway
30Environment 3 Smart Room