Ian M. Mitchell

Bio: Ian M. Mitchell is an academic researcher from University of British Columbia. The author has contributed to research in topics: Wheelchair & Reachability. The author has an hindex of 30, co-authored 99 publications receiving 6149 citations. Previous affiliations of Ian M. Mitchell include University of California, Berkeley & Stanford University.

A time-dependent Hamilton-Jacobi formulation of reachable sets for continuous dynamic games

Abstract: We describe and implement an algorithm for computing the set of reachable states of a continuous dynamic game. The algorithm is based on a proof that the reachable set is the zero sublevel set of the viscosity solution of a particular time-dependent Hamilton-Jacobi-Isaacs partial differential equation. While alternative techniques for computing the reachable set have been proposed, the differential game formulation allows treatment of nonlinear systems with inputs and uncertain parameters. Because the time-dependent equation's solution is continuous and defined throughout the state space, methods from the level set literature can be used to generate more accurate approximations than are possible for formulations with potentially discontinuous solutions. A numerical implementation of our formulation is described and has been released on the web. Its correctness is verified through a two vehicle, three dimensional collision avoidance example for which an analytic solution is available.

Best Practices for Scientific Computing

Abstract: Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many are unaware of tools and practices that would allow them to write more reliable and maintainable code with less effort. We describe a set of best practices for scientific software development that have solid foundations in research and experience, and that improve scientists' productivity and the reliability of their software. Software is as important to modern scientific research as telescopes and test tubes. From groups that work exclusively on computational problems, to traditional laboratory and field scientists, more and more of the daily operation of science revolves around developing new algorithms, managing and analyzing the large amounts of data that are generated in single research projects, combining disparate datasets to assess synthetic problems, and other computational tasks. Scientists typically develop their own software for these purposes because doing so requires substantial domain-specific knowledge. As a result, recent studies have found that scientists typically spend 30% or more of their time developing software [1],[2]. However, 90% or more of them are primarily self-taught [1],[2], and therefore lack exposure to basic software development practices such as writing maintainable code, using version control and issue trackers, code reviews, unit testing, and task automation. We believe that software is just another kind of experimental apparatus [3] and should be built, checked, and used as carefully as any physical apparatus. However, while most scientists are careful to validate their laboratory and field equipment, most do not know how reliable their software is [4],[5]. This can lead to serious errors impacting the central conclusions of published research [6]: recent high-profile retractions, technical comments, and corrections because of errors in computational methods include papers in Science [7],[8], PNAS [9], the Journal of Molecular Biology [10], Ecology Letters [11],[12], the Journal of Mammalogy [13], Journal of the American College of Cardiology [14], Hypertension [15], and The American Economic Review [16]. In addition, because software is often used for more than a single project, and is often reused by other scientists, computing errors can have disproportionate impacts on the scientific process. This type of cascading impact caused several prominent retractions when an error from another group's code was not discovered until after publication [6]. As with bench experiments, not everything must be done to the most exacting standards; however, scientists need to be aware of best practices both to improve their own approaches and for reviewing computational work by others. This paper describes a set of practices that are easy to adopt and have proven effective in many research settings. Our recommendations are based on several decades of collective experience both building scientific software and teaching computing to scientists [17],[18], reports from many other groups [19]–, guidelines for commercial and open source software development [26],, and on empirical studies of scientific computing [28]–[31] and software development in general (summarized in [32]). None of these practices will guarantee efficient, error-free software development, but used in concert they will reduce the number of errors in scientific software, make it easier to reuse, and save the authors of the software time and effort that can used for focusing on the underlying scientific questions. Our practices are summarized in Box 1; labels in the main text such as “(1a)” refer to items in that summary. For reasons of space, we do not discuss the equally important (but independent) issues of reproducible research, publication and citation of code and data, and open science. We do believe, however, that all of these will be much easier to implement if scientists have the skills we describe. Box 1. Summary of Best Practices Write programs for people, not computers. A program should not require its readers to hold more than a handful of facts in memory at once. Make names consistent, distinctive, and meaningful. Make code style and formatting consistent. Let the computer do the work. Make the computer repeat tasks. Save recent commands in a file for re-use. Use a build tool to automate workflows. Make incremental changes. Work in small steps with frequent feedback and course correction. Use a version control system. Put everything that has been created manually in version control. Don't repeat yourself (or others). Every piece of data must have a single authoritative representation in the system. Modularize code rather than copying and pasting. Re-use code instead of rewriting it. Plan for mistakes. Add assertions to programs to check their operation. Use an off-the-shelf unit testing library. Turn bugs into test cases. Use a symbolic debugger. Optimize software only after it works correctly. Use a profiler to identify bottlenecks. Write code in the highest-level language possible. Document design and purpose, not mechanics. Document interfaces and reasons, not implementations. Refactor code in preference to explaining how it works. Embed the documentation for a piece of software in that software. Collaborate. Use pre-merge code reviews. Use pair programming when bringing someone new up to speed and when tackling particularly tricky problems. Use an issue tracking tool. Write Programs for People, Not Computers Scientists writing software need to write code that both executes correctly and can be easily read and understood by other programmers (especially the author's future self). If software cannot be easily read and understood, it is much more difficult to know that it is actually doing what it is intended to do. To be productive, software developers must therefore take several aspects of human cognition into account: in particular, that human working memory is limited, human pattern matching abilities are finely tuned, and human attention span is short [33]–[37]. First, a program should not require its readers to hold more than a handful of facts in memory at once (1a). Human working memory can hold only a handful of items at a time, where each item is either a single fact or a “chunk” aggregating several facts [33],[34], so programs should limit the total number of items to be remembered to accomplish a task. The primary way to accomplish this is to break programs up into easily understood functions, each of which conducts a single, easily understood, task. This serves to make each piece of the program easier to understand in the same way that breaking up a scientific paper using sections and paragraphs makes it easier to read. Second, scientists should make names consistent, distinctive, and meaningful (1b). For example, using non-descriptive names, like a and foo, or names that are very similar, like results and results2, is likely to cause confusion. Third, scientists should make code style and formatting consistent (1c). If different parts of a scientific paper used different formatting and capitalization, it would make that paper more difficult to read. Likewise, if different parts of a program are indented differently, or if programmers mix CamelCaseNaming and pothole_case_naming, code takes longer to read and readers make more mistakes [35],[36].

Computational techniques for the verification of hybrid systems

Abstract: Hybrid system theory lies at the intersection of the fields of engineering control theory and computer science verification. It is defined as the modeling, analysis, and control of systems that involve the interaction of both discrete state systems, represented by finite automata, and continuous state dynamics, represented by differential equations. The embedded autopilot of a modern commercial jet is a prime example of a hybrid system: the autopilot modes correspond to the application of different control laws, and the logic of mode switching is determined by the continuous state dynamics of the aircraft, as well as through interaction with the pilot. To understand the behavior of hybrid systems, to simulate, and to control these systems, theoretical advances, analyses, and numerical tools are needed. In this paper, we first present a general model for a hybrid system along with an overview of methods for verifying continuous and hybrid systems. We describe a particular verification technique for hybrid systems, based on two-person zero-sum game theory for automata and continuous dynamical systems. We then outline a numerical implementation of this technique using level set methods, and we demonstrate its use in the design and analysis of aircraft collision avoidance protocols and in verification of autopilot logic.

Level Set Methods for Computation in Hybrid Systems

Abstract: Reachability analysis is frequently used to study the safety of control systems We present an implementation of an exact reachability operator for nonlinear hybrid systems After a brief review of a previously presented algorithm for determining reachable sets and synthesizing control laws--upon whose theory the new implementation rests--an equivalent formulation is developed of the key equations governing the continuous state reachability The new formulation is implemented using level set methods, and its effectiveness is shown by the numerical solution of three examples

Planning Algorithms: Introductory Material

Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

MIMIC-III, a freely accessible critical care database

Abstract: MIMIC-III ('Medical Information Mart for Intensive Care') is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.

MIMIC-III, a freely accessible critical care database

Abstract: MIMIC-III ('Medical Information Mart for Intensive Care') is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

Abstract: ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of