Formal Verification of Industrial Software and Neural Networks

Software ist ein wichtiger Bestandteil unsere heutige Gesellschaft. Da Software vermehrt in sicherheitskritischen Bereichen angewandt wird, müssen wir uns auf eine korrekte und sichere Ausführung verlassen können. Besonders eingebettete Software, zum Beispiel in medizinischen Geräten, Autos oder Flugzeugen, muss gründlich und formal geprüft werden. Die Software solcher eingebetteten Systeme kann man in zwei Komponenten aufgeteilt. In klassische (deterministische) Steuerungssoftware und maschinelle Lernverfahren zum Beispiel für die Bilderkennung oder Kollisionsvermeidung angewandt werden. Das Ziel dieser Dissertation ist es den Stand der Technik bei der Verifikation von zwei Hauptkomponenten moderner eingebetteter Systeme zu verbessern: in C/C++ geschriebene Software und neuronalen Netze. Für beide Komponenten wird das Verifikationsproblem formal definiert und neue Verifikationsansätze werden vorgestellt

Explaining and Refining Decision-Theoretic Choices

As the need to make complex choices among competing alternative actions is ubiquitous, the reasoning machinery of many intelligent systems will include an explicit model for making choices. Decision analysis is particularly useful for modelling such choices, and its potential use in intelligent systems motivates the construction of facilities for automatically explaining decision-theoretic choices and for helping users to incrementally refine the knowledge underlying them. The proposed thesis addresses the problem of providing such facilities. Specifically, we propose the construction of a domain-independent facility called UTIL, for explaining and refining a restricted but widely applicable decision-theoretic model called the additive multi-attribute value model. In this proposal we motivate the task, address the related issues, and present preliminary solutions in the context of examples from the domain of intelligent process control

Structural optimization of numerical programs for high-level synthesis

This thesis introduces a new technique, and its associated tool SOAP, to automatically perform source-to-source optimization of numerical programs, specifically targeting the trade-off among numerical accuracy, latency, and resource usage as a high-level synthesis flow for FPGA implementations. A new intermediate representation, MIR, is introduced to carry out the abstraction and optimization of numerical programs. Equivalent structures in MIRs are efficiently discovered using methods based on formal semantics by taking into account axiomatic rules from real arithmetic, such as associativity, distributivity and others, in tandem with program equivalence rules that enable control-flow restructuring and eliminate redundant array accesses. For the first time, we bring rigorous approaches from software static analysis, specifically formal semantics and abstract interpretation, to bear on program transformation for high-level synthesis. New abstract semantics are developed to generate a computable subset of equivalent MIRs from an original MIR. Using formal semantics, three objectives are calculated for each MIR representing a pipelined numerical program: the accuracy of computation and an estimate of resource utilization in FPGA and the latency of program execution. The optimization of these objectives produces a Pareto frontier consisting of a set of equivalent MIRs. We thus go beyond existing literature by not only optimizing the precision requirements of an implementation, but changing the structure of the implementation itself. Using SOAP to optimize the structure of a variety of real world and artificially generated arithmetic expressions in single precision, we improve either their accuracy or the resource utilization by up to 60%. When applied to a suite of computational intensive numerical programs from PolyBench and Livermore Loops benchmarks, SOAP has generated circuits that enjoy up to a 12x speedup, with a simultaneous 7x increase in accuracy, at a cost of up to 4x more LUTs.Open Acces

Terrain Representation And Reasoning In Computer Generated Forces : A Survey Of Computer Generated Forces Systems And How They Represent And Reason About Terrain

Report on a survey of computer systems used to produce realistic or intelligent behavior by autonomous entities in simulation systems. In particular, it is concerned with the data structures used by computer generated forces systems to represent terrain and the algorithmic approaches used by those systems to reason about terrain

Proceedings of the NASA Conference on Space Telerobotics, volume 5

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotics technology to the space systems planned for the 1990's and beyond. Volume 5 contains papers related to the following subject areas: robot arm modeling and control, special topics in telerobotics, telerobotic space operations, manipulator control, flight experiment concepts, manipulator coordination, issues in artificial intelligence systems, and research activities at the Johnson Space Center

CLiFF Notes: Research in the Language Information and Computation Laboratory of The University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLIFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science, Psychology, and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. With 48 individual contributors and six projects represented, this is the largest LINC Lab collection to date, and the most diverse