AI Technologies for Tactical Edge Networks

imminentpoppedIA et Robotique

23 févr. 2014 (il y a 3 années et 1 mois)

54 vue(s)

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1

AI Technologies for

Tactical Edge Networks

Karen Zita Haigh

Raytheon BBN Technologies

May 2011

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2

What is AI?

The Odd Paradox

Practical AI successes … were soon assimilated into whatever
application domain they were found to be useful in, and became silent
partners …, which left AI researchers to deal only with the failures.”
[McCorduck, 2004]

Karen Zita Haigh

Artificial
Intelligence

Mathematics

Economics

Psychology

Control Theory

Natural
Language
Processing

Speech
Recognition

Machine Vision

Robotics

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3

Joe Mitola’s OOPDAL Loop
















Joseph Mitola III,
Cognitive Radio: An Integrated Agent Architecture for Software
Defined Radio
, Phd Thesis, Royal Institute of Technology (KTH), 2000

Karen Zita Haigh

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4

Joe Mitola’s OOPDAL Loop (2)

Karen Zita Haigh

Orient

Plan

Act

Observe

Decide

Learn

Collect

Validate

Assess situation

Infer Intent

Impact Analysis

Select Goals

Generate Plans

Schedule

Select Plan

Allocate Resources

Implement

Update
Models

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5

Roles for AI in Networking


Cyber Security


Network Configuration
(which modules to use)


Network Control (which
parameter settings to
use)


Policy Management


Traffic Analysis


Performance Analysis


Sensor fusion /
situation assessment


Planning


Coordination


Optimization


Constraint reasoning


Learning (Modelling)


Complex Domain


Dynamic Domain


Unpredictable by
Experts

AI enables real
-
time, context
-
aware adaptivity

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6

MANET Characteristics

What AI is good at


Dynamic


Diverse


Massive Scale


Complex Parameter
Interactions


Partially
-
observable
feedback


Complex Access Policies


Multi
-
objective
performance requirements


Main challenges for AI


Ambiguous feedback


High
-
latency feedback


Resource Constrained


Heterogeneous
Intercommunication

Karen Zita Haigh

Cross
-
Layer Optimization on Steroids

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7

Knowledge Engineering


Captures knowledge so that a computer system
can solve complex problems, e.g.


models of physics and signal propagation, constraints
on the system, analysis of interactions, and rules of
thumb (e.g., about how to configure the system).


A formal ontology may help a cognitive system
reason about how and when capabilities are
interchangeable


Knowledge bases can help optimize the network


e.g. By biasing a learning algorithm


e.g. By constraining a planner

Karen Zita Haigh

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Planning and Scheduling


Organizes tasks to meet performance objectives
under resource constraints


Multi
-
agent planning, dynamic programming, constraint
satisfaction, and distributed or combinatorial
optimization algorithms


Planning and scheduling techniques in networks
can decide what content to move, where, when,
and how


Prefetch

/
prepush

data


Power
-
aware computing


Node activity and task scheduling


Network management


Server placement; when to handle queries

Karen Zita Haigh

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9

Multi
-
Agent Systems


Traditional MAS approaches fail in MANET
because they assume that communications are (a)
infinite and (b) always available


Biologically
-
inspired approaches have done better.


Demonstrated Applications:


Routing:
AntHocNet

uses both proactive and reactive
schemes to update the routing tables, and outperforms
AODV.


Network connectivity


Dynamic load balancing


Service placement

Karen Zita Haigh

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10

Machine Learning


ML improves the performance of a system by
observing the environment and updating models


the learner must generalize so that the learned model is
useful for new (previously unseen) situations.


Artificial neural networks
, support vector machines,
clustering, explanation
-
based learning, induction,
reinforcement learning
,
genetic algorithms
, nearest
neighbour methods, and case
-
based learning.


Demonstrated Applications


Routing


Energy management


Node mobility


Parameter interaction

Karen Zita Haigh

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Concrete Example: ML in ADROIT


Adaptive Dynamic Radio Open
-
source Intelligent
Team (ADROIT)


Create cognitive radio teams that


Recognize that the situation has changed


Anticipates changes in networking needs


Adapts the network, in real
-
time, for improved
performance


Real
-
time composability of the stack


Real
-
time control of parameters


On one node and across the network

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ADROIT’s Experimental Testbed

Maximize %
of shared
map of the
environment

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13

Experimental Results

Training Run:


In first run nodes learn
about environment


Train neural nets with
(Conditions,Strategy)

Performance

tuples


Every 5s, measure and
record progress, conditions,
& strategy


Observations are local, so
each node learns different
model!

Real
-
time learning run:


In second run, nodes adapt
behaviour to perform
better.


Adapt each minute by
changing strategy
according to current
conditions


Real
-
time cognitive control of a

real
-
world wireless network

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14

Observations from Learning


Selected configurations explainable but not
predictable


Farthest
-
refraining was usually better


congestion, not loss dominated


Unicast/Multicast was far more complex


close: unicast wins (high data rates)


medium: multicast wins (sharing gain)


far: unicast wins (reliability)

14

System performed better with learning

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Biggest remaining challenges


Social engineering


the human
-
to
-
human interaction of the AI
community differs dramatically from that of the
networking community


Software architecture


Network architectures are traditionally tightly
coupled; we need to provide hooks

May 2011

Karen Zita Haigh

Module 1

Module 2

Module 2

Module 1

Broker

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SOFTWARE ARCHITECTURE

Karen Zita Haigh

May 2011

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A Need for Restructuring


SDR gives opportunity to create
highly
-
adaptable systems, BUT


They usually require network experts to
exploit the capabilities!


They usually rely on module APIs that are
carefully designed to expose each
parameter separately.


This approach is not maintainable


e.g. as protocols are redesigned or new
parameters are exposed.


This approach is not amenable to
real
-
time cognitive control


Hard to upgrade


Conflicts between module & AI

Module 1

Module 2

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A Need for Restructuring


We need one consistent, generic, interface

for all modules to expose their parameters

and dependencies.

Module 2

Module 1

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A Generic Network Architecture

exposeParameter(
parameter_name
,
parameter_properties
)

setValue(
parameter_handle
,
parameter_value
)

getValue(
parameter_handle

)

Broker


-
Assigns
handles

-
Provides
directory
services

-
Sets up event
monitors

-
Pass through
get/set

Cognitive Control

Command Line
Interface

Network Management

Network Stack

Network Module

Network Module

Registering
Modules

Re/Setting
Modules

Observing
Params

Registering
Modules &
Parameters

Re/Setting
Modules

Observing
Params

Applications / QoS

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20

Benefits of a Generic Architecture


It supports network architecture design &
maintenance


Solves the
n
х
m

problem (upgrades or
replacements of network modules)


It doesn’t restrict the form of cognition


Open to just about any form of cognition you
can imagine


Supports
multiple
forms of cognition on each
node


Supports
different

forms across nodes


It doesn’t
mandate

cognition

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21

SOCIAL ENGINEERING

Karen Zita Haigh

May 2011

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Cultural Issues: But why?


Benefits and scope of
cross
-
layer design:


More than 2 layers!


More than 2
-
3
parameters per layer



Drill
-
down walkthroughs
highlighted benefits to
networking folks;
explained restrictions to
AI folks


Simulation results for
specific scenarios
demonstrated the power


Traditional network
design includes
adaptation


But this works against
cognition: it is hard to
manage
global

scope


AI people want to
control everything


But network module
may be better at doing
something focussed



Design must include
constraining

how a
protocol adapts

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23

Cultural Issues: But how?


Reliance on centralized
Broker:


Networking folks don’t
like the single bottleneck


Design must have fail
-
safe default operation


Asynchrony and
Threading:


AI people tend to like
blocking calls.


e.g. to ensure that
everything is consistent


Networking folks outright
rejected it.


Design must include
reporting and alerting

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Cultural Issues: But it’ll break!?!


Relinquishing control
outside the stack:


Outside controller
making decisions scares
networking folks


AI folks say “give me
everything & I’ll solve
your problem”



Architecture includes
“failsafe” mechanisms to
limit both sides



Heterogeneous and
non
-
interoperable

nodes


Networks usually have
homogeneous
configurations to
maintain
communications


AI likes heterogeneity
because of the benefit


But always assumes safe
communications!



“Orderwire” bootstrap
channel as backup

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Cultural Issues: New horizons?


Capability Boundaries


Traditional Networking has very clear boundary
between “network” and “application”


Generic architecture blurs that boundary


AI folks like the benefit


Networking folks have concerns about complexity


Removing this conceptual restriction will
result in interesting and significant new
ideas.


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26

Conclusion


AI techniques are ready to be
challenged with this complex real
-
world
domain, just as Networking
requirements are reaching the limits of
what can be done without AI.



To demonstrate the power of cognitive
networking, both AI folks & Networking
folks need to recognize and adapt