Image Understanding by Hierarchical Symbolic Representation and Inexact Matching of Attributed Graphs

We study the symbolic representation of imagery information by a powerful global representation scheme in the form of Attributed Relational Graph (ARG), and propose new techniques for the extraction of such representation from spatial-domain images, and for performing the task of image understanding through the analysis of the extracted ARG representation. To achieve practical image understanding tasks, the system needs to comprehend the imagery information in a global form. Therefore, we propose a multi-layer hierarchical scheme for the extraction of global symbolic representation from spatial-domain images. The proposed scheme produces a symbolic mapping of the input data in terms of an output alphabet, whose elements are defined over global subimages. The proposed scheme uses a combination of model-driven and data-driven concepts. The model- driven principle is represented by a graph transducer, which is used to specify the alphabet at each layer in the scheme. A symbolic mapping is driven by the input data to map the input local alphabet into the output global alphabet. Through the iterative application of the symbolic transformational mapping at different levels of hierarchy, the system extracts a global representation from the image in the form of attributed relational graphs. Further processing and interpretation of the imagery information can, then, be performed on their ARG representation. We also propose an efficient approach for calculating a distance measure and finding the best inexact matching configuration between attributed relational graphs. For two ARGs, we define sequences of weighted error-transformations which when performed on one ARG (or a subgraph of it), will produce the other ARG. A distance measure between two ARGs is defined as the weight of the sequence which possesses minimum total-weight. Moreover, this minimum-total weight sequence defines the best inexact matching configuration between the two ARGs. The global minimization over the possible sequences is performed by a dynamic programming technique, the approach shows good results for ARGs of practical sizes. The proposed system possesses the capability to inference the alphabets of the ARG representation which it uses. In the inference phase, the hierarchical scheme is usually driven by the input data only, which normally consist of images of model objects. It extracts the global alphabet of the ARG representation of the models. The extracted model representation is then used in the operation phase of the system to: perform the mapping in the multi-layer scheme. We present our experimental results for utilizing the proposed system for locating objects in complex scenes

Formalizing graphical notations

The thesis describes research into graphical notations for software engineering, with a principal interest in ways of formalizing them. The research seeks to provide a theoretical basis that will help in designing both notations and the software tools that process them. The work starts from a survey of literature on notation, followed by a review of techniques for formal description and for computational handling of notations. The survey concentrates on collecting views of the benefits and the problems attending notation use in software development; the review covers picture description languages, grammars and tools such as generic editors and visual programming environments. The main problem of notation is found to be a lack of any coherent, rigorous description methods. The current approaches to this problem are analysed as lacking in consensus on syntax specification and also lacking a clear focus on a defined concept of notated expression. To address these deficiencies, the thesis embarks upon an exploration of serniotic, linguistic and logical theory; this culminates in a proposed formalization of serniosis in notations, using categorial model theory as a mathematical foundation. An argument about the structure of sign systems leads to an analysis of notation into a layered system of tractable theories, spanning the gap between expressive pictorial medium and subject domain. This notion of 'tectonic' theory aims to treat both diagrams and formulae together. The research gives details of how syntactic structure can be sketched in a mathematical sense, with examples applying to software development diagrams, offering a new solution to the problem of notation specification. Based on these methods, the thesis discusses directions for resolving the harder problems of supporting notation design, processing and computer-aided generic editing. A number of future research areas are thereby opened up. For practical trial of the ideas, the work proceeds to the development and partial implementation of a system to aid the design of notations and editors. Finally the thesis is evaluated as a contribution to theory in an area which has not attracted a standard approach

GP 2: Efficient Implementation of a Graph Programming Language

The graph programming language GP (Graph Programs) 2 and its implementation is the subject of this thesis. The language allows programmers to write visual graph programs at a high level of abstraction, bringing the task of solving graph-based problems to an environment in which the user feels comfortable and secure. Implementing graph programs presents two main challenges. The first challenge is translating programs from a high-level source code representation to executable code, which involves bridging the gap from a non-deterministic program to deterministic machine code. The second challenge is overcoming the theoretically impractical complexity of applying graph transformation rules, the basic computation step of a graph program. The work presented in this thesis addresses both of these challenges. We tackle the first challenge by implementing a compiler that translates GP 2 graph programs directly to C code. Implementation strategies concerning the storage and access of internal data structures are empirically compared to determine the most efficient approach for executing practical graph programs. The second challenge is met by extending the double-pushout approach to graph transformation with root nodes to support fast execution of graph transformation rules by restricting the search to the local neighbourhood of the root nodes in the host graph. We add this theoretical construct to the GP 2 language in order to support rooted graph transformation rules, and we identify a class of rooted rules that are applicable in constant time on certain classes of graphs. Finally, we combine theory and practice by writing rooted graph programs to solve two common graph algorithms, and demonstrate that their execution times are capable of matching the execution times of tailored C solutions

The Baby project: processing character patterns in textual representations of language.

This thesis describes an investigation into a proposed theory of AI. The theory postulates that a machine can be programmed to predict aspects of human behaviour by selecting and processing stored, concrete examples of previously experienced patterns of behaviour. Validity is tested in the domain of natural language. Externalisations that model the resulting theory of NLP entail fuzzy components. Fuzzy formalisms may exhibit inaccuracy and/or over productivity. A research strategy is developed, designed to investigate this aspect of the theory. The strategy includes two experimental hypotheses designed to test, 1) whether the model can process simple language interaction, and 2) the effect of fuzzy processes on such language interaction. Experimental design requires three implementations, each with progressive degrees of fuzziness in their processes. They are respectively named: Nonfuzz Babe, CorrBab and FuzzBabe. Nonfuzz Babe is used to test the first hypothesis and all three implementations are used to test the second hypothesis. A system description is presented for Nonfuzz Babe. Testing the first hypothesis provides results that show NonfuzzBabe is able to process simple language interaction. A system description for CorrBabe and FuzzBabe is presented. Testing the second hypothesis, provides results that show a positive correlation between degree of fuzzy processes and improved simple language performance. FuzzBabe's ability to process more complex language interaction is then investigated and model-intrinsic limitations are found. Research to overcome this problem is designed to illustrate the potential of externalisation of the theory and is conducted less rigorously than previous part of this investigation. Augmenting FuzzBabe to include fuzzy evaluation of non-pattern elements of interaction is hypothesised as a possible solution. The term FuzzyBaby was coined for augmented implementation. Results of a pilot study designed to measure FuzzyBaby's reading comprehension are given. Little research has been conducted that investigates NLP by the fuzzy processing of concrete patterns in language. Consequently, it is proposed that this research contributes to the intellectual disciplines of NLP and AI in general

A Survey of Knowledge Representation in Service Robotics

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modelling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.Comment: Accepted for RAS Special Issue on Semantic Policy and Action Representations for Autonomous Robots - 22 Page