# Presentation: 542887

## Intelligent SystemsSoftware Assurance Symposium 2004

- Bojan Cukic & Yan Liu, Robyn Lutz & Stacy Nelson, Chris Rouff, Johann Schumann, Margaret Smith
- July 22, 2004

## “What”

- Intelligent Systems research will create “new generations of robust, fault-tolerant software for intelligent, cooperative space systems that operate largely autonomously from ground control” --NASA list of key technology areas for H & RT Advanced Space Technology, 6/04
- New technologies for V&V of Intelligent Systems

## “What” (cont.)

- Technologies demonstrated at this year’s presentations:
- Neural Networks
- AI Planners
- Support Vector Data Description algorithms
- Bayesian-based safety envelopes
- Autonomous contingency identification and recovery technology
- Model Checking
- Hybrid formal methods

## Information Systems Presentations

## Information Systems Presentations

## Intelligent Systems: Why ?

- Long lived missions
- Lower operations costs
- Swarms & constellations of satellites/spacecraft
- Currently used in other domains:
- automotive
- health
- waste water management
- Intelligent Systems are here to stay!

## Intelligent Systems: Why not

- Is the technology:
- Scalable for usage?
- Being oversold?
- Just a piece of a larger puzzle?
- V&V of Intelligent Systems requires a new knowledge set: math, tools, control theory, and highly skilled software engineers.
- V&V is scrambling to catch up to new technologies for Intelligent Systems

## Directions? 

- Do we know yet how to design intelligent systems for verifiability?  (or meaningless to lump them?)
- Is the IV&V process different for intelligent systems?
- Are we ready to demonstrate scalability on real systems?
- Should we be developing V&V standards for intelligent systems?  Tied to criticality levels?
- How do we start establishing benchmarks for intelligent systems?

## Verification and Validation of Adaptive Systems by Bojan Cukic

- Investigate the role of modern AI techniques (Support Vector Machines) in failure detection and identification.
- Failure Detection
- Designing a fast (real-time) SVDD algorithm to detect failure conditions
- Failure Identification
- Failures are identified by studying the correlation between certain longitudinal and lateral dynamics parameters
- Validate the technology in extensive simulations

## Bayesian Verification and Validation tools for Adaptive Systems by Johann Schumann

- Problems with traditional V&V methods applied to Adaptive Systems:
- Fault avoidance design testing applies to base case only
- Unanticipated failures?
-  Unmodeled failures?
- Fault removal cannot test all possible configurations in advance
- Fault tolerant design does not consider all possible problems

## Bayesian Verification and Validation tools for Adaptive Systems by Johann Schumann

- Methods for improvement:
- Improve performance estimation of the neural network (Bayesian approach)
- Use Envelope tool to answer:
-  How large is the current safe envelope?
-  How far is the operational point from the edge?

## Formal Approaches to Swarm Technologies by Chris Rouff

**  ****Survey formal approaches for agent-based, multi-agent and swarm-based systems for appropriate swarm-based methods**
**  ****Apply most promising approaches to parts of ANTS**
**  ****Evaluate methods for needed properties**
**  ****Model and outline swarm-based formal method**
**  ****Develop formal method for swarm-based systems**
**  ****Do formal specification of ANTS using new method**
**  ****Prototype support tools**

## Formal Approaches to Swarm Technologies An ANTS Overview  -  by Chris Rouff

## Contingency Software in Autonomous Systems

- by Robyn Lutz & Stacy Nelson

- The Goal - Mitigate failures via software contingencies resulting in safer, more reliable autonomous vehicles in space and in FAA national airspace
- How?
- 	Adding intelligent diagnostic capabilities by supporting 	incremental autonomy
- 	Responding to anomalous situations currently beyond 	the scope of the nominal fault protection
- 	Contingency planning using the SAFE (Software 		Adjusts Failed Equipment) method

## Model Checking of Artificial Intelligence Based Plannersby Margaret Smith

**Goal:** Using model checking, and specifically the SPIN model checker, retire a significant class of risks associated with the use of Artificial Intelligence (AI) Planners on Missions
- Must provide tangible testing results to a mission using AI technology.
- Should be possible to leverage the technique and tools throughout NASA.
**FY04 Activities:**
- Identify and select candidate risks
- Develop and demonstrate technique for testing AI Planners/artifacts on:
- A toy problem (imaging/downlinking) – demonstrate tangible results with an abstracted clock/timeline
- A real problem (DS4/ST4 Champollion Mission) – demonstrate, using DS4 AI input models, that Spin can determine if an AI input model permits the AI planner to select ‘bad plans’.

## Lyapunov Stability Analysis and On-Line Monitoring by Bojan Cukic

- The Problem:
- Issues with Adaptive Systems: uncertainty/newness
- Need Understanding of self stabilization analysis techniques suitable for adaptive system verification
- Need to investigate effective means to determine the stability and convergence properties of the learner in real-time
- The Approach:
- Online Monitoring
- Confidence Evaluation

## Lyapunov Stability Analysis and On-Line Monitoring by Bojan Cukic

- Relevance to NASA:
-   Artificial Neural Networks are increasingly important in flight control and navigation
-   Autonomy and adaptability are important features in many NASA projects
-   The theory is applicable to future agent-based applications