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