Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

A Computational Model of Creative Design as a Sociocultural Process Involving the Evolution of Language

The aim of this research is to investigate the mechanisms of creative design within the context of an evolving language through computational modelling. Computational Creativity is a subfield of Artificial Intelligence that focuses on modelling creative behaviours. Typically, research in Computational Creativity has treated language as a medium, e.g., poetry, rather than an active component of the creative process. Previous research studying the role of language in creative design has relied on interviewing human participants, limiting opportunities for computational modelling. This thesis explores the potential for language to play an active role in computational creativity by connecting computational models of the evolution of artificial languages and creative design processes. Multi-agent simulations based on the Domain-Individual-Field-Interaction framework are employed to evolve artificial languages with features that may support creative designing including ambiguity, incongruity, exaggeration and elaboration. The simulation process consists of three steps: (1) constructing representations associating topics, meanings and utterances; (2) structured communication of utterances and meanings through the playing of “language games”; and (3) evaluation of design briefs and works. The use of individual agents with different evaluation criteria, preferences and roles enriches the scope and diversity of the simulations. The results of the experiments conducted with artificial creative language systems demonstrate the expansion of design spaces by generating compositional utterances representing novel concepts among design agents using language features and weighted context free grammars. They can be used to computationally explore the roles of language in creative design, and possibly point to computational applications. Understanding the evolution of artificial languages may provide insights into human languages, especially those features that support creativity

DNA as Topological Quantum Computer

This article represents a vision about how DNA might act as a topological quantum computer (tqc). Tqc means that the braidings of braid strands define tqc programs and M-matrix (generalization of S-matrix in zero energy ontology) defining the entanglement between states assignable to the end points of strands define the tqc usually coded as unitary time evolution for Schödinger equation. One can ends up to the model in the following manner. a) Darwinian selection for which the standard theory of self-organization provides a model, should apply also to tqc programs. Tqc programs should correspond to asymptotic self-organization patterns selected by dissipation in the presence of metabolic energy feed. The spatial and temporal pattern of the metabolic energy feed characterizes the tqc program - or equivalently - sub-program call. b) Since braiding characterizes the tqc program, the self-organization pattern should correspond to a hydrodynamical flow or a pattern of magnetic field inducing the braiding. Braid strands must correspond to magnetic flux tubes of the magnetic body of DNA. If each nucleotide is transversal magnetic dipole it gives rise to transversal flux tubes, which can also connect to the genome of another cell. As a matter fact, the flux tubes would correspond to what I call wormhole magnetic fields having pairs of space-time sheets carrying opposite magnetic fluxes. c) The output of tqc sub-program is probability distribution for the outcomes of state function reduction so that the sub-program must be repeated very many times. It is represented as four-dimensional patterns for various rates (chemical rates, nerve pulse patterns, EEG power distributions, ...) having also identification as temporal densities of zero energy states in various scales. By the fractality of TGD Universe there is a hierarchy of tqcs corresponding to p-adic and dark matter hierarchies. Programs (space-time sheets defining coherence regions) call programs in shorter scale. If the self-organizing system has a periodic behavior each tqc module defines a large number of almost copies of itself asymptotically. Generalized EEG could naturally define this periodic pattern and each period of EEG would correspond to an initiation and halting of tqc. This brings in mind the periodically occurring sol-gel phase transition inside cell near the cell membrane. There is also a connection with hologram idea: EEG rhythm corresponds to reference wave and nerve pulse patters to the wave carrying the information and interfering with the reference wave. d) Fluid flow must induce the braiding which requires that the ends of braid strands must be anchored to the fluid flow. Recalling that lipid mono-layers of the cell membrane are liquid crystals and lipids of interior mono-layer have hydrophilic ends pointing towards cell interior, it is easy to guess that DNA nucleotides are connected to lipids by magnetic flux tubes and hydrophilic lipid ends are stuck to the flow. e) The topology of the braid traversing cell membrane cannot be affected by the hydrodynamical flow. Hence braid strands must be split during tqc. This also induces the desired magnetic isolation from the environment. Halting of tqc reconnects them and make possible the communication of the outcome of tqc. The model makes several testable predictions about DNA itself. In particular, matter-antimatter asymmetry and slightly broken isospin symmetry have counterparts at DNA level induced from the breaking of these symmetries for quarks and antiquarks associated with the flux tubes. DNA cell membrane system is not the only possible system that could perform tqc like activities and store memories in braidings: flux tubes could connect biomolecules and the braiding could provide an almost definition for what it is to be living. Even water memory might reduce to braidings. The model leads also to an improved understanding of other roles of the magnetic flux tubes containing dark matter. Phase transitions changing the value of Planck constant for the magnetic flux tubes could be key element of bio-catalysis and electromagnetic long distance communications in living matter. For instance, one ends up to what might be called code for protein folding and bio-catalysis. There is also a fascinating connection with Peter Gariaev's work suggesting that the phase transitions changing Planck constant have been observed and wormhole magnetic flux tubes containing dark matter have been photographed in his experiments

Concepts and analogies in cybernetics: Mathematical investigations of the role of analogy in concept formation and problem solving; with emphasis for conflict resolution via object and morphism eliminations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.We address two problematic areas of cybernetics; nam. Analogical Problem Solving (APS) and Analogical Learning (AL). Both these human faculties do unquestionably require Intelligence. In addition, we point out that shifting of representations is the main unified theme underlying these two intellectual tasks. We focus our attention on the formulation and clarification of the notion of analogy, which has been loosely treated and used in the literature; and also on its role in shifting of representations. We describe analogizing situations in a new representational scheme, borrowed from mathematics and modified and extended to cater for our targets. We call it k-structure, closely resembling semantic networks and directed graphs; the main components of it are the so-called objects and morphisms. We argue and substantiate the need for such a representation scheme, by analysing what its constituents stand for and by cataloguing its virtues, the main one being its visual appeal and its mathematical clarity, and by listing its disadvantages when it is compared to other representation systems. Emphasis is also given to its descriptive power and usefulness by implementing it in a number of APS and AL situations. Besides representation issues, attention is paid to intelligence mechanisms which are involved in APS and AL. A cornerstone in APS and a fundamental theme in AL is the 'skeletization of k-structures'. APS is conceived as 'harmonization of skeletons'. The methodology we develop involves techniques which are computer implemented and extensively studied in theoretic terms via a proposed theory for extended k-structures. To name but a few: 1. 'the separation of the context of a concept from the concept itself', based on the ideas of k-opens and k-spaces; 2, 'object and morphism elimination' of a controversial nature; and 3. 'conflict or deadlock or dilemma resolution' which naturally arises in a k-structure interaction. The overall system, is then applied to capture the essence of EVANS' (1963) analogy-type problems and WINSTOM (1970) learning-type situations. In our attempt not to be too informal, we use basic notions and terminology from abstract Algebra, Topology and Category theory. We rather tend to be "non-logical" (analogical) in EVANS' and WINSTON's sense; "non-numeric", in MESAROVIC (1970) terms (we rather deal with abstract conceptual entities); "non-linguistic" (we do not touch natural language); and "non-resolution" oriented, in the sense of BLEDSOE (1977). However, we give hints sometimes about logical deductive axiomatic systems, employing First Order Predicate Calculus (FOPC); and about semiotics, by which we denote syntactic-semantic-pragmatic features of our system and issues of the problem domains it is acting upon. We believe in what we call: shift from the traditional 'Heuristic search paradigm' era to the 'Analogy-paradigm' era underlying Artificial Intelligence and Cybernetics. We justify this merely by listing a number of A. I. works, which employ, in some way or another, the concept of analogy, over the last fifteen years or so, where a noticeable peak is obvious during the last years and especially in 1977. Finally, we hope that if the proposed conceptual framework and techniques developed do not straightforwardly constitute some kind of platform for Artificial Intelligence, at least it would give some insights into and illuminate our understanding of the two most fundamental faculties the human brain is occupied with; namely problem solving and learning

Graph Theory and Universal Grammar

Tese arquivada ao abrigo da Portaria nº 227/2017 de 25 de Julho-Registo de Grau EstrangeiroIn the last few years, Noam Chomsky (1994; 1995; 2000; 2001) has gone quite far in the direction of simplifying syntax, including eliminating X-bar theory and the levels of D-structure and S-structure entirely, as well as reducing movement rules to a combination of the more primitive operations of Copy and Merge. What remain in the Minimalist Program are the operations Merge and Agree and the levels of LF (Logical Form) and PF (Phonological form). My doctoral thesis attempts to offer an economical theory of syntactic structure from a graph-theoretic point of view (cf. Diestel, 2005), with special emphases on the elimination of category and projection labels and the Inclusiveness Condition (Chomsky 1994). The major influences for the development of such a theory have been Chris Collins’ (2002) seminal paper “Eliminating labels”, John Bowers (2001) unpublished manuscript “Syntactic Relations” and the Cartographic Paradigm (see Belletti, Cinque and Rizzi’s volumes on OUP for a starting point regarding this paradigm). A syntactic structure will be regarded here as a graph consisting of the set of lexical items, the set of relations among them and nothing more

Novel applications of discrete mereotopology to mathematical morphology

This paper shows how the Discrete Mereotopology notions of adjacency and neighbourhood between regions can be exploited through Mathematical Morphology to accept or reject changes resulting from traditional morphological operations such as closing and opening. This leads to a set of six morphological operations (here referred to generically as minimal opening and minimal closing ) where minimal changes fulfil specific spatial constraints. We also present an algorithm to compute the RCC5D and RCC8D relation sets across multiple regions resulting in a performance improvement of over three orders of magnitude over our previously published algorithm for Discrete Mereotopology

Human inspired robotic path planning and heterogeneous robotic mapping

One of the biggest challenges facing robotics is the ability for a robot to autonomously navigate real-world unknown environments and is considered by many to be a key prerequisite of truly autonomous robots. Autonomous navigation is a complex problem that requires a robot to solve the three problems of navigation: localisation, goal recognition, and path-planning. Conventional approaches to these problems rely on computational techniques that are inherently rigid and brittle. That is, the underlying models cannot adapt to novel input, nor can they account for all potential external conditions, which could result in erroneous or misleading decision making. In contrast, humans are capable of learning from their prior experiences and adapting to novel situations. Humans are also capable of sharing their experiences and knowledge with other humans to bootstrap their learning. This is widely thought to underlie the success of humanity by allowing high-fidelity transmission of information and skills between individuals, facilitating cumulative knowledge gain. Furthermore, human cognition is influenced by internal emotion states. Historically considered to be a detriment to a person's cognitive process, recent research is regarding emotions as a beneficial mechanism in the decision making process by facilitating the communication of simple, but high-impact information. Human created control approaches are inherently rigid and cannot account for the complexity of behaviours required for autonomous navigation. The proposed thesis is that cognitive inspired mechanisms can address limitations in current robotic navigation techniques by allowing robots to autonomously learn beneficial behaviours from interacting with its environment. The first objective is to enable the sharing of navigation information between heterogeneous robotic platforms. The second objective is to add flexibility to rigid path-planning approaches by utilising emotions as low-level but high-impact behavioural responses. Inspired by cognitive sciences, a novel cognitive mapping approach is presented that functions in conjunction with current localisation techniques. The cognitive mapping stage utilises an Anticipatory Classifier System (ACS) to learn the novel Cognitive Action Map (CAM) of decision points, areas in which a robot must determine its next action (direction of travel). These physical actions provide a shared means of understanding the environment to allow for communicating learned navigation information. The presented cognitive mapping approach has been trained and evaluated on real-world robotic platforms. The results show the successful sharing of navigation information between two heterogeneous robotic platforms with different sensing capabilities. The results have also demonstrated the novel contribution of autonomously sharing navigation information between a range-based (GMapping) and vision-based (RatSLAM) localisation approach for the first time. The advantage of sharing information between localisation techniques allows an individual robotic platform to utilise the best fit localisation approach for its sensors while still being able to provide useful navigation information for robots with different sensor types. Inspired by theories on natural emotions, this work presents a novel emotion model designed to improve a robot's navigation performance through learning to adapt a rigid path-planning approach. The model is based on the concept of a bow-tie structure, linking emotional reinforcers and behavioural modifiers through intermediary emotion states. An important function of the emotions in the model is to provide a compact set of high-impact behaviour adaptations, reducing an otherwise tangled web of stimulus-response patterns. Crucially, the system learns these emotional responses with no human pre-specifying the behaviour of the robot, hence avoiding human bias. The results of training the emotion model demonstrate that it is capable of learning up to three emotion states for robotic navigation without human bias: fear, apprehension, and happiness. The fear and apprehension responses slow the robot's speed and drive the robot away from obstacles when the robot experiences pain, or is uncertain of its current position. The happiness response increases the speed of the robot and reduces the safety margins around obstacles when pain is absent, allowing the robot to drive closer to obstacles. These learned emotion responses have improved the navigation performance of the robot by reducing collisions and navigation times, in both simulated and real-world experiments. The two emotion model (fear and happiness) improved performance the most, indicating that a robot may only require two emotion states (fear and happiness) for navigation in common, static domains

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat

Uncertainty management in connectionist expert system

A connectionist expert system is an expert system whose knowledge base is generated from training examples using an artificial neural network learning technique. Gallant [13] developed a model for a connectionist expert system in which a variable is represented by a node and accepts two values, true or false. This study adopts two approaches to help manage uncertainty in Gallant's model. The first approach is called the random cell method while the second one is the stairstep method.Computer Scienc