Self-calibrating models for dynamic monitoring and diagnosis

A method for automatically building qualitative and semi-quantitative models of dynamic systems, and using them for monitoring and fault diagnosis, is developed and demonstrated. The qualitative approach and semi-quantitative method are applied to monitoring observation streams, and to design of non-linear control systems

Self-calibrating models for dynamic monitoring and diagnosis

The present goal in qualitative reasoning is to develop methods for automatically building qualitative and semiquantitative models of dynamic systems and to use them for monitoring and fault diagnosis. The qualitative approach to modeling provides a guarantee of coverage while our semiquantitative methods support convergence toward a numerical model as observations are accumulated. We have developed and applied methods for automatic creation of qualitative models, developed two methods for obtaining tractable results on problems that were previously intractable for qualitative simulation, and developed more powerful methods for learning semiquantitative models from observations and deriving semiquantitative predictions from them. With these advances, qualitative reasoning comes significantly closer to realizing its aims as a practical engineering method

The Stanford how things work project

We provide an overview of the Stanford How Things Work (HTW) project, an ongoing integrated collection of research activities in the Knowledge Systems Laboratory at Stanford University. The project is developing technology for representing knowledge about engineered devices in a form that enables the knowledge to be used in multiple systems for multiple reasoning tasks and reasoning methods that enable the represented knowledge to be effectively applied to the performance of the core engineering task of simulating and analyzing device behavior. The central new capabilities currently being developed in the project are automated assistance with model formulation and with verification that a design for an electro-mechanical device satisfies its functional specification

Multi Domain Design: Integration and Reuse

Design of mechatronic systems is becoming increasingly complex. Companies must continuously reduce time-to-market while increasing the quality, diversity, and functionality of their products. As a result, more and more specialists from various domains are needed to develop such products. To reduce time-to-market, many companies look to reducing the time it takes to design a product. Many focus on the reuse of design objects, leading to libraries of templates and standard components to speed up their design process. However, these reusable design objects are developed and maintained in the specialists’ domains, resulting in communication and integration issues between these domains. This paper discusses these issues and proposes a combined approach for model reuse, design integration, and communication between the designers, design tools, and models involved. A case study at a multi-national company successfully demonstrated that the approach leads to a faster and more consistent design process

Robustness of Model Predictions under Extension

Often, mathematical models of the real world are simplified representations of complex systems. A caveat to using models for analysis is that predicted causal effects and conditional independences may not be robust under model extensions, and therefore applicability of such models is limited. In this work, we consider conditions under which qualitative model predictions are preserved when two models are combined. We show how to use the technique of causal ordering to efficiently assess the robustness of qualitative model predictions and characterize a large class of model extensions that preserve these predictions. For dynamical systems at equilibrium, we demonstrate how novel insights help to select appropriate model extensions and to reason about the presence of feedback loops. We apply our ideas to a viral infection model with immune responses.Comment: Accepted for oral presentation at the Causal Discovery & Causality-Inspired Machine Learning Workshop at Neural Information Processing Systems, 202

Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation

With the rise of autonomous systems, there is a need for them to have high levels of robustness and safety. This robustness can be achieved through systems that are self-repairing. Underlying this is the ability to diagnose subtle failures. Likewise, online planners can generate novel responses to exceptional situations. These planners require an accurate estimate of state. Estimation methods based on hybrid discrete/continuous state models have emerged as a method of computing precise state estimates, which can be employed for either diagnosis or planning in hybrid domains. However, existing methods have difficulty scaling to systems with more than a handful of components. Discrete state estimation capabilities can scale to this level by combining best-first enumeration and conflict-directed search. Best-first methods have been developed for hybrid estimation, but the creation of conflict-directed methods has previously been elusive. While conflicts are used to learn from constraint violation, probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach to hybrid estimation that unifies best-first enumeration and conflict-directed search through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. This paper presents a general best-first search and enumeration algorithm based on bounding conflicts (A*BC) and a hybrid estimation method based on this enumeration algorithm. Experiments show that an A*BC powered state estimator produces estimates faster than the current state of the art, particularly on large systems

Compositional Model Conversion

This dissertation presents an initial work towards the development of a technique to convert compositional models from one modelling paradigm to another, by means of a pair of equivalent compositional modelling domain theories. The mapping between model fragments of the two domain theories is not necessarily in a one-to-one manner. It might be the case that a model fragment in one domain theory covers parts of several model fragments in the other domain theory. This is one of the major conversion problems that this technique will focus on. The compositional modelling of ecological systems is used as a testing domain for the implemented conversion technique. For this work, system dynamics and object-oriented representations are the two modelling paradigms adopted. The major intention of this conversion application, implemented in the C++ programming language, is to convert a system dynamics model, composed through a compositional modelling technique, to an object-oriented model. The resulting object-oriented model is expected to reflect the same scenario, but with a different representation, compared to the model produced within the system dynamics modelling paradigm

Multimodal Reasoning about Physical Systems

Abstract We present a knowledge representation and reasoning framework that integrates qualitative reasoning, qualitative simulation, numerical simulation, geometric reasoning, constraint reasoning, resolution, reasoning with abstraction levels, declarative meta-level control, and a simple form of truth maintenance. The framework is the core of PRET, a system identification program that automates the process of modeling physical systems. Introduction Models are powerful tools that are used to understand physical systems. The process of inferring an internal model from external observations of a system---often called system identification--is a routine and difficult problem faced by engineers in a variety of domains The program PaET (Bradley & Stolle 1996) automates both stages of the system identification process; its goal is to find a system of ODEs that models

Causality and independence in perfectly adapted dynamical systems

Perfect adaptation in a dynamical system is the phenomenon that one or more variables have an initial transient response to a persistent change in an external stimulus but revert to their original value as the system converges to equilibrium. The causal ordering algorithm can be used to construct an equilibrium causal ordering graph that represents causal relations and a Markov ordering graph that implies conditional independences from a set of equilibrium equations. Based on this, we formulate sufficient graphical conditions to identify perfect adaptation from a set of first-order differential equations. Furthermore, we give sufficient conditions to test for the presence of perfect adaptation in experimental equilibrium data. We apply our ideas to a simple model for a protein signalling pathway and test its predictions both in simulations and on real-world protein expression data. We demonstrate that perfect adaptation in this model can explain why the presence and orientation of edges in the output of causal discovery algorithms does not always appear to agree with the direction of edges in biological consensus networks.Comment: 32 page