vault backup: 2025-07-31 10:18:54
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.obsidian/plugins/colored-tags/data.json
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"Security": 308,
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"Systematic-mapping-study": 309,
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"InFirstPaks": 310,
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"Formal-Methods": 311
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"Formal-Methods": 311,
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"admin": 312
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},
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"_version": 3
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}
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---
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---
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# Today's Journal
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# 2025-07-30
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## Morning Brief
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Today's goal is to finish writing up some thesis ideas. I
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Zettelkasten/Fleeting Notes/Journal/2025_07_31.md
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Zettelkasten/Fleeting Notes/Journal/2025_07_31.md
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---
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---
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# 2025-07-31
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## Morning Brief
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Yesterday I wrote 2 thesis ideas and started a third. Today,
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we finish them! I'm struggling to write outcomes but we'll
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get there over time. I can do this.
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I started the morning with an hour of doomscrolling. I
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really have to sort that out.
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Tasks are the same from [[2025-07-30|yesterday]]. Maybe I
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should make a centralized page for tasks.
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- [ ] Make task page #admin
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## Lunch Brief
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## Note for tomorrow
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@ -88,36 +88,55 @@ systems.
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___________________________________________________________
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## **Temporal Logic Specifications for Autonomous Controller Shield Synthesis**
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(3)
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### Goals:
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### Outcomes:
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### Impact:
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### Related Papers:
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___________________________________________________________
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## **Formally Verified Runtime Monitoring and Fallback**
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### Goals:
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If this research is successful, we will be able to generate
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autonomous controllers that provably adhere to specifications
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autonomous controller shields that provably adhere to specifications
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written with temporal logic automatically.
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### Outcomes:
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- Create an intermediary shield that mediates signals between an
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optimal control system and the physical plant
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optimal control system and the physical plant (MODBUS)?
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-
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- Translate specifications in a language like TLA+ into an
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executable program
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- Provide proof artifacts that automatically generated
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shield components will not allow an arbitrary controller to
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reach an unsafe state.
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### Impact:
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Shielding is one of the preeminent ways to do safe machine
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learning controllers. Instead of putting the proof burden on
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the machine learning component, shielding creates a safe
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boundary in the state space where a safety controller will
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step in if the machine learning controller endangers the
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system. This technology solves a critical problem with high
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assurance systems: high assurance systems have critical
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safety requirements that make scrutiny on autonomous systems
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safety intense. Shielding can provide a safety barrier for
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the controller, allowing the architecture of the control
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laws to be amenable to more efficient machine learning based
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methods. Finally, utilizing an automatic translation from a
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temporal logic formulation of a speculation will allow the
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engineers of these systems to quickly and clearly implement
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a shield, without all of the cumbersome derivation.
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### Related Papers:
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[[on-using-real-time-reachability-for-the-safety-assurance-of-machine-learning-controllers]]
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[[enhancing-cyber-physical-system-dependability-via-synthesis-challenges-and-future-directions]]
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[[safe-reinforcement-learning-via-shielding]]
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___________________________________________________________
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## **Formally Verified Runtime Monitoring and Fallback**
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(4)
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### Goals:
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### Outcomes:
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### Impact:
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### Related Papers:
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___________________________________________________________
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