This thesis presents a new approach to building a design for testability (DFT) system. The system takes a digital circuit description, finds out the problems in testing it, and suggests circuit modifications to correct those problems. The key contributions of the thesis research are (1) setting design for testability in the context of test generation (TG), (2) using failures during FG to focus on testability problems, and (3) relating circuit modifications directly to the failures. A natural functionality set is used to represent the maximum functionalities that a component can have. The current implementation has only primitive domain knowledge and needs other work as well. However, armed with the knowledge of TG, it has already demonstrated its ability and produced some interesting results on a simple microprocessor.

This paper describes ARLO, a representation language loosely modelled after Greiner and Lenant's RLL-1. ARLO is a structure-based representation language for describing structure-based representation languages, including itself. A given representation language is specified in ARLO by a collection of structures describing how its descriptions are interpreted, defaulted, and verified. This high level description is compiles into lisp code and ARLO structures whose interpretation fulfills the specified semantics of the representation. In addition, ARLO itself- as a representation language for expressing and compiling partial and complete language specifications- is described and interpreted in the same manner as the language it describes and implements. This self-description can be extended of modified to expand or alter the expressive power of ARLO's initial configuration. Languages which describe themselves like ARLO- provide powerful mediums for systems which perform automatic self-modification, optimization, debugging, or documentation. AI systems implemented in such a self-descriptive language can reflect on their own capabilities and limitations, applying general learning and problem solving strategies to enlarge or alleviate them.

Advances in information technology produce large sets of data for decision makers. In both military and civilian efforts to achieve decision superiority, decision makers have to act agilely with proper, adequate and relevant information available. Information fusion is a process aimed to support decision makers’ situation awareness. This involves a process of combining data and information from disparate sources with prior information or knowledge to obtain an improved state estimate about an agent or other relevant phenomena. The important issue in decision making is not only assessing the current situation but also envisioning how a situation may evolve. In this work we focus on the prediction part of decision making called predictive situation awareness. We introduce new methodology where simulations and plan recognition are tools for achieving improved predictive situation awareness. Plan recognition is the term given to the process of inferring an agent’s intentions from a set of actions and is intended to support decision making. Beside its main task that is to support decision makers’ predictive situation awareness, plan recognition could also be used for coordination of actions and for developing computer-game agents that possess cognitive ability to recognize other agents’ behaviour. Successful plan recognition is heavily dependent on the data that is supplied. Therefore we introduce a bridge between plan recognition and sensor management where results of our plan recognition are reused to the control of, to give focus of attention to, the sensors that are expected to acquire the most important/relevant information. Our methodologies include knowledge representation, embedded stochastic simulations, microeconomics, imprecise knowledge and statistical inference issues. / QC 20100922

In two studies, the author attempts to develop a general symbol theoretical approach to knowledge representation. The first study, Modeling change - the frame problem, critically examines the - so far unsuccessful - attempts to solve the notorious frame problem. By discussing and analyzing a number of related problems - the prediction problem, the revision problem, the qualification problem, and the book-keeping problem - the frame problem is distinguished as the problem of finding a representational form permitting a changing, complex world to be efficiently and adequately represented. This form, it is argued, is dictated by the metaphysics of the problem world, the fundamental form of the symbol system we humans use in rightly characterizing the world. In the second study, Pictures and words, the symbol theoretical approach is made more explicit. The subject Is the distinction between pictorial (non-linguistic, non-propositional, analogical, "direct") representation and verbal (linguistic, propositional) representation, and the further implications of this distinction. The study focuses on pictorial representation, which has received little attention compared to verbal representation. Observations, ideas, and theories in AI, cognitive psychology, and philosophy are critically examined. The general conclusion is that there is as yet no cogent and mature theory of pictorial representation that gives good support to computer applications. The philosophical symbol theory of Nelson Goodman is found to be the most thoroughly developed and most congenial with the aims and methods of AI. Goodman's theory of pictorial representation, however, in effect excludes computers from the use of pictures. In the final chapter, an attempt is made to develop Goodman's analysis of pictures further turning it into a theory useful to AI. The theory outlined builds on Goodman's concept of exemplification. The key idea is that a picture is a model of a description that has the depicted object as its standard model. One consequence Is that pictorial and verbal forms of representation are seen less as competing alternatives than as complementary forms of representation mutually supporting and depending on each other. / digitalisering@umu

The purpose of the architecture of software systems in business organizations is to support those organizations in achieving business goals. In software development organizations the design of an architecture is a collective effort that involves various organizational stakeholders and designers, who identify, interpret, and reason about intents, and communicate, delegate, commit to, and implement intents and decisions. Current architectural design descriptions are by-and-large based on block-and-arrow notations representing "coarse-grained" solution elements of the system. They lack explicit representation for modeling and analyzing the decision-making of stakeholders and architectural designers who hold different organizational responsibilities, and pursue conflicting and/or synergistic business or system goals, while collectively pursuing organizational objectives. This thesis considers the proposition that a distributed intentionality perspective is applicable in the design of software system architectures. During architectural design, relationships between intentional actors define the context in which intentional actors pursue business and system goals and in which they negotiate architectural decision-making. The objective of this research is to investigate what an Intentional Architecture Language (IAL) could be like that utilizes intentional and organizational modeling and analysis concepts to support architectural decision-making efforts in organizational settings. Drawing from prior work on organizational modeling and analysis, this thesis first defines a core IAL, and then explores its use to model and analyze architectural decision-making both reported in the literature and empirically observed at a number of commercial projects in industry. Drawing from these explorations, this thesis proposes a number of extensions to the core IAL, discusses lessons learned, and points to the advantages and limitations in using an IAL to model and analyze architectural decision-making in an organizational setting.

The purpose of the architecture of software systems in business organizations is to support those organizations in achieving business goals. In software development organizations the design of an architecture is a collective effort that involves various organizational stakeholders and designers, who identify, interpret, and reason about intents, and communicate, delegate, commit to, and implement intents and decisions. Current architectural design descriptions are by-and-large based on block-and-arrow notations representing "coarse-grained" solution elements of the system. They lack explicit representation for modeling and analyzing the decision-making of stakeholders and architectural designers who hold different organizational responsibilities, and pursue conflicting and/or synergistic business or system goals, while collectively pursuing organizational objectives. This thesis considers the proposition that a distributed intentionality perspective is applicable in the design of software system architectures. During architectural design, relationships between intentional actors define the context in which intentional actors pursue business and system goals and in which they negotiate architectural decision-making. The objective of this research is to investigate what an Intentional Architecture Language (IAL) could be like that utilizes intentional and organizational modeling and analysis concepts to support architectural decision-making efforts in organizational settings. Drawing from prior work on organizational modeling and analysis, this thesis first defines a core IAL, and then explores its use to model and analyze architectural decision-making both reported in the literature and empirically observed at a number of commercial projects in industry. Drawing from these explorations, this thesis proposes a number of extensions to the core IAL, discusses lessons learned, and points to the advantages and limitations in using an IAL to model and analyze architectural decision-making in an organizational setting.

The modern medical diagnostic systems are based on the techniques using digital data formats – a natural feed for the computer based systems. With the use of modern diagnostic techniques the diagnosis process is becoming more complex as many diseases seem to have the same pre-symptoms at early stages. And of course computer based systems require more efficient and effective ways to identify such complexities. However, the existing formalisms for knowledge representation, tools and technologies, learning and reasoning strategies seem inadequate to create meaningful relationship among the entities of medical data i.e. diseases, symptoms and medicine etc. This inadequacy actually is due to the poor design of the knowledge base of the medical system and leads the medical systems towards inaccurate diagnosis. This thesis discusses the limitations and issues specific to the design factors of the knowledge base and suggests that instead of using the deficient approaches and tools for representing, learning and retrieving the accurate knowledge, use of semantic web tools and techniques should be adopted. Design by contract approach may be suitable for establishing the relationships between the diseases and symptoms. The relationship between diseases and symptoms and their invariants can be represented more meaningfully using semantic web. This can lead to more concrete diagnosis, by overcoming the deficiencies and limitations of traditional approaches and tools.

Clinical decision support systems (CDSS) and the Picture Archiving and Communication System (PACS) have been adopted by large healthcare organization to support stroke diagnosis to reduce the level of misdiagnosis occurrence. This research presents a methodology for constructing a stroke decision support system (Stroke DSS) which integrates basic information, physical and image stroke assessment criterions, constructs ischemic, hemorrhage and subarachnoid hemorrhage of stroke diagnosis flow. A prototype embedded methodology was built to support stroke diagnosis in healthcare organization. Using a design science approach, we embed the constructs of our methodology in a prototype and perform a usability evaluation to demonstrate the utility of our approach. The usability evaluation demonstrates the effectiveness of our approach in terms of efficiency, effectiveness and satisfaction. The resulting system allowed flexible knowledge model and representation that are useful for stroke diagnosis.

There is much information on the web, but the overloaded information may cause user¡¦s disorientation. The aim of this research is to address a method for automatically constructing the Knowledge Structure Map with time-line feature, and expect to solve the problems of information overloaded and use¡¦s disorientation by visual interface. In this research, the MIS domain is adopted as apply target and the national Dissertation and Thesis Abstract System as data source. By dealing with the keywords to be the subjects, and performing the Principle Component Analysis and calculating the relation strength for the subjects, the Knowledge Structure Map with time-line feature will finally automatically be implemented. Besides the introduction to the method for implementing the Knowledge Structure Map, the writer also develops an experiment to show the Knowledge Structure Map is very helpful when users are taking tasks of description and procedure knowledge. And finally, the writer utilizes these maps to analyze the knowledge and its evolution and development for Taiwan¡¦s MIS domain. The method of this research can automatically construct Knowledge Structure Map with time-line feature. And the Maps will be useful auxiliary tools in learning, research direction decision and knowledge sharing for the novices, researchers, and people respectively. Besides, the enterprises can also use the method of this research to construct their own internal or external Knowledge Structure Map by applying the existing automatic abstracting and picking-up keywords technologies to extract abstracts and keywords of relative information in their organization. Therefore, the writer will conclude that the method in this research can be easily applied in the field of academic and enterprises, and furthermore to the other organizations.

<p>The modern medical diagnostic systems are based on the techniques using digital data formats – a natural feed for the computer based systems. With the use of modern diagnostic techniques the diagnosis process is becoming more complex as many diseases seem to have the same pre-symptoms at early stages. And of course computer based systems require more efficient and effective ways to identify such complexities. However, the existing formalisms for knowledge representation, tools and technologies, learning and reasoning strategies seem inadequate to create meaningful relationship among the entities of medical data i.e. diseases, symptoms and medicine etc. This inadequacy actually is due to the poor design of the knowledge base of the medical system and leads the medical systems towards inaccurate diagnosis. This thesis discusses the limitations and issues specific to the design factors of the knowledge base and suggests that instead of using the deficient approaches and tools for representing, learning and retrieving the accurate knowledge, use of semantic web tools and techniques should be adopted. Design by contract approach may be suitable for establishing the relationships between the diseases and symptoms. The relationship between diseases and symptoms and their invariants can be represented more meaningfully using semantic web. This can lead to more concrete diagnosis, by overcoming the deficiencies and limitations of traditional approaches and tools.</p>