AI Technologies for Tactical Edge Networks

1. AI Technologies forTactical Edge Networks Karen Zita Haigh Raytheon BBN Technologies May 2011

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] Economics Natural Language Processing Artificial Intelligence Mathematics Speech Recognition Psychology Machine Vision Control Theory Robotics Karen Zita Haigh

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

4. Joe Mitola’s OOPDAL Loop (2) Orient Assess situation Infer Intent Impact Analysis Plan Select Goals Generate Plans Schedule Observe Learn Collect Validate Update Models Decide Select Plan Allocate Resources Act Implement Karen Zita Haigh

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

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 Cross-Layer Optimization on Steroids Karen Zita Haigh

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

8. 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

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

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

11. 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

12. ADROIT’s Experimental Testbed Maximize % of shared map of the environment

13. Training Run: In first run nodes learn about environment Train neural nets with (Conditions,Strategy)Performancetuples 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 Experimental Results Real-time cognitive control of a real-world wireless network

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) System performed better with learning

15. Biggest remaining challenges Module 2 Module 1 Module 2 • 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 Broker Module 1 Karen Zita Haigh

16. Software architecture Karen Zita Haigh

17. A Need for Restructuring Module 2 Module 1 • 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

18. A Need for Restructuring Module 2 Module 1 • We need one consistent, generic, interfacefor all modules to expose their parameters and dependencies.

19. A Generic Network Architecture Network Stack Network Module Network Module Registering Modules & Parameters • Broker • Assigns handles • Provides directory services • Sets up event monitors • Pass through get/set Applications / QoS Registering Modules Cognitive Control Re/Setting Modules Re/Setting Modules Network Management Observing Params Observing Params Command Line Interface exposeParameter( parameter_name, parameter_properties ) setValue( parameter_handle, parameter_value ) getValue( parameter_handle )

20. 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

21. Social engineering Karen Zita Haigh

22. 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 Cultural Issues: But why?

23. 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 Cultural Issues: But how?

24. 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 Cultural Issues: But it’ll break!?!

25. 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.

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