Page 1 AI Technologies for AI Technologies for Tactical Edge Networks Tactical Edge Networks Karen Zita Haigh Raytheon BBN Technologies May 2011
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AI Technologies forAI Technologies forTactical Edge NetworksTactical Edge Networks
Karen Zita HaighRaytheon BBN Technologies
May 2011
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What is AI?
The Odd ParadoxPractical 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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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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Joe Mitola’s OOPDAL Loop (2)
Karen Zita Haigh
Orient
Plan
Act
Observe
Decide
LearnCollectValidate
Assess situationInfer Intent
Impact Analysis
Select GoalsGenerate PlansSchedule
Select PlanAllocate Resources
Implement
Update Models
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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 DomainUnpredictable by
Experts
AI enables real-time, context-aware adaptivity
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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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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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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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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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Experimental ResultsTraining 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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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)
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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 2Module 2
Module 1 Bro
ker
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SOFTWARE ARCHITECTURE
Karen Zita HaighMay 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, interfacefor 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 StackN
etw
ork
Mo
du
le
Network Module
Registering Modules
Re/Setting Modules
Observing Params
Registering Modules & Parameters
Re/Setting Modules
Observing Params
Applications / QoS
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Benefits of a Generic Architecture
• It supports network architecture design & maintenance– Solves the nхm 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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SOCIAL ENGINEERING
Karen Zita HaighMay 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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Cultural Issues: But how?
• Reliance on centralized Broker:– Networking folks don’t
like the single bottleneckDesign 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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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