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ROGUEWOLF SmartCities: Anticipating Agents of Change Adam Amos-Binks Colleen Stacy Lucia Titus Kathleen Vogel Lori Wachter November 2, 2016 Outline Motivation: SmartCities + Anticipatory thinking Approach Background: SmartCities


  1. ROGUEWOLF SmartCities: Anticipating Agents of Change Adam Amos-Binks Colleen Stacy Lucia Titus Kathleen Vogel Lori Wachter November 2, 2016

  2. Outline Motivation: SmartCities + Anticipatory thinking Approach Background: SmartCities Anticipatory thinking Intentional planning Future oriented SATs Case Study: SmartCities Agents Summary

  3. SmartCities IoT - $4.6 Trillion value-at-stake for public sector 1 Cities will generate 63% of total value-at-stake City infrastructure transformed through IoT 1 - J Bradley, C Reberger, A Dixit, and V Gupta. 2013. “Internet of Everything: A $4.6 Trillion Public-Sector Opportunity.” Cisco , no. 23: 1–17 .

  4. Anticipatory Thinking (AT) Deliberately thinking about the future 2 2 - Klein, G., D. Snowden, and C. L. Pin. 2011. “nticipatory ThinkingA.” KL Mosier, & UM Fischer, Informed by Knowledge (2011)

  5. Approach Support Anticipatory Thinking ( trajectory tracking ) with Artificial Intelligence ( intentional planning ) and structured analytic techniques ( AFA + backcasting ) to produce Anticipatory Intelligence for an emerging use case ( SmartCities )

  6. Background

  7. Smart Cities ● Interconnected power grids reducing power waste ● Smarter transportation resulting in increased traffic management ● Smarter infrastructures that reduce hazards and increase efficiency But also means: ● Expanded attack surface for malicious actors ● Cyber vulnerabilities resulting in physical consequences

  8. Smart Cities ● Case Examples: ○ Ukraine Power Grid Hack: Cyber Used in Warfare (Dec. 2015) ○ OVH WebCam Attack: 1Tbps DDoS (Sept. 2016) ○ Dyn DDoS: IoT->DNS Attack (Oct. 2016) ● Ideal for AT as it’s coming, still time to have an impact! ● What entities will influence the value at stake?

  9. Anticipatory Thinking ● Different from prediction, relies on expertise ● Generate a view of the future ● Identified forms : ○ Pattern Matching ○ Convergent Thinking ○ Trajectory Tracking: “Narrative...is of particular use in trajectory tracking to understand the possible moves of other actors in the decision space” - Klein et al. (2011) 2 - Klein, G., D. Snowden, and C. L. Pin. 2011. “Anticipatory Thinking.” KL Mosier, & UM Fischer, Informed by Knowledge (2011)

  10. Anticipatory Thinking ● Trajectory Tracking ○ Actors actions influence our decision space ○ Does not scale, hard to incorporate new actors

  11. Artificial Intelligence ● Russell and Norvig’s AI taxonomy Thinking Humanly Thinking Rationally Acting Humanly Acting Rationally

  12. Artificial Intelligence ● Automated planning: Industrial problems ● Plans: Theorized to represent plots 3 ○ Lead to intentional planning 3 - Schank, R, Abelson, R 1977. “Scripts, plans, goals and understanding: An inquiry into human knowledge structures.”

  13. Intentional Planning ● Intentional path ○ Character actions are believable, goal-oriented ○ Motivated when goals are not the current state ● Intentional plan ○ Consists of intentional paths ○ Character goals conflict + collaborate 4 - Riedl, M. Young, R. M. 2010. “Narrative Planning: Balancing Plot and Character” Journal of Artificial Intelligence Research

  14. Intentional Planning ● Need library of intentional paths!

  15. Future-oriented SATs ● What are SATs? ○ Mechanisms to guide analysts to think about a problem. ● Why use SATs? ○ Externalize and decompose thinking ○ Help analyst mitigate cognitive limitations ○ Enable peer review

  16. Future-oriented SATs ● Alternative Futures Analysis ○ Identified alternative trajectories ○ Based on critical uncertainties ○ Inform and illuminate decisions, plans and actions today ● Backcasting ○ Scenario analysis ○ Desirable futures ○ Plot a point in time, work backward to present

  17. Case Study Knowledge Engineering

  18. Alternative Futures Analysis How might the world of the Internet evolve? Independent Corporate Involvement Bottom line Bliss uncertainty (corporate (hackers have dominance with leverage ) little risk) La Shangri Clockwork Security of the Internet Orange Government Regulations Nation State without bite Security (government (increasing regulates to no regulatory avail) control) Leviathan 5 - Healey, Jason, and Hughes Barry. 2015. “Risk Nexus: Overcome by Cyber Risks? Economic Benefits and Costs of Alternate Cyber Futures.” The Atlantic Group .

  19. Internet Evolution Axes: ● Security ○ Shangri La - Clockwork Orange ● Gov. Involvement ○ Independent - Leviathan

  20. Industry Sector Actors Sectors: ● Electricity ● Water ● Transportation

  21. Strategic Plans Strategic plans Intentional plans Corporate strategies (e.g. Apple, Duke Energy, Toyota) Government future vision policy docs (e.g. Raleigh 2030) NGO/NPO strategies (e.g. WRF) Non-state actor strategies (e.g. ISIS 2020)

  22. Intentional Paths Actor: Water Research Foundation (NGO) Sector: Water Goal: Shangri-La (H), Leviathan (H)

  23. Intentional Paths Actor: ISIS (Militant) Sector: Transportation Goal: Clockwork Orange (H), Leviathan (H)

  24. Intentional Paths Actor: Government Sector: Transportation Goal: Shangri-La (M), Independent (L)

  25. Insights (Cooperation) Water Research Foundation & Government, water Leviathan

  26. Insights (Conflict) ISIS vs Government, Transportation

  27. Use Case ● Hand crafted knowledge engineering ○ SmartCities actors (sectors from DHS) ○ SATs (AFA, backcasting) ○ Strategic plans → Intentional paths ○ Trajectory tracking (conflicts, collaboration)

  28. Summary ● Trajectory tracking w/ intentional planning ● SmartCity knowledge engineering using SATs ● Strategic plans → intentional paths

  29. Next Steps DO7 ● OpenKE: automate strategic → intentional paths ● Input for trajectory tracking in serious game

  30. Discussion Questions? Email: {aaamosbi, cstacy, lltitus, kmvogel, lawachte}@ncsu.edu

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