A scalable genome representation for neural-symbolic networks

Neural networks that are capable of representing symbolic information such as logic programs are said to be neural-symbolic. Because the human mind is composed of interconnected neurons and is capable of storing and processing symbolic information, neural-symbolic networks contribute towards a model of human cognition. Given that natural evolution and development are capable of producing biological networks that are able to process logic, it may be possible to produce their artificial counterparts through evolutionary algorithms that have developmental properties. The first step towards this goal is to design a genome representation of a neural-symbolic network. This paper presents a genome that directs the growth of neural-symbolic networks constructed according to a model known as SHRUTI. The genome is successful in producing SHRUTI networks that learn to represent relations between logical predicates based on observations of sequences of predicate instances. A practical advantage of the genome is that its length is independent of the size of the network it encodes, because rather than explicitly encoding a network topology, it encodes a set of developmental rules. This approach to encoding structure in a genome also has biological grounding

Evolutionary morphogenesis for multi-cellular systems

With a gene required for each phenotypic trait, direct genetic encodings may show poor scalability to increasing phenotype length. Developmental systems may alleviate this problem by providing more efficient indirect genotype to phenotype mappings. A novel classification of multi-cellular developmental systems in evolvable hardware is introduced. It shows a category of developmental systems that up to now has rarely been explored. We argue that this category is where most of the benefits of developmental systems lie (e.g. speed, scalability, robustness, inter-cellular and environmental interactions that allow fault-tolerance or adaptivity). This article describes a very simple genetic encoding and developmental system designed for multi-cellular circuits that belongs to this category. We refer to it as the morphogenetic system. The morphogenetic system is inspired by gene expression and cellular differentiation. It focuses on low computational requirements which allows fast execution and a compact hardware implementation. The morphogenetic system shows better scalability compared to a direct genetic encoding in the evolution of structures of differentiated cells, and its dynamics provides fault-tolerance up to high fault rates. It outperforms a direct genetic encoding when evolving spiking neural networks for pattern recognition and robot navigation. The results obtained with the morphogenetic system indicate that this "minimalist” approach to developmental systems merits further stud

Analog Genetic Encoding for the Evolution of Circuits and Networks

This paper describes a new kind of genetic representation called analog genetic encoding (AGE). The representation is aimed at the evolutionary synthesis and reverse engineering of circuits and networks such as analog electronic circuits, neural networks, and genetic regulatory networks. AGE permits the simultaneous evolution of the topology and sizing of the networks. The establishment of the links between the devices that form the network is based on an implicit definition of the interaction between different parts of the genome. This reduces the amount of information that must be carried by the genome relatively to a direct encoding of the links. The application of AGE is illustrated with examples of analog electronic circuit and neural network synthesis. The performance of the representation and the quality of the results obtained with AGE are compared with those produced by genetic programming

Estudio de un modelo de desarrollo embriológico

Este proyecto desarrolla un modelo de vida artificial que sirve como marco para avanzar en la comprensión del desarrollo embriológico, es decir, la forma en que un ser multicelular se desarrolla desde el cigoto. En particular, se pretende que sea una herramienta para explorar la relación entre el plan corporal expresado en el crecimiento del embrión y las instrucciones codificadas en el genoma. El sistema desarrollado comprende un modelo de simulación física basado en mallas de muelles, un modelo del citoesqueleto celular esquematizado y un sistema de reglas basado en la interpretación de un genoma, cuyos genes expresan reglas que determinan el comportamiento de los modelos celulares durante la simulación. También comprende un sistema de visualización gráfica de los resultados de las simulaciones

Development of an Intelligent Robotic Manipulator

The presence of hazards to human health in chemical process plant and nuclear waste stores leads to the use of robots and more specifically manipulators in unmanned spaces. Rapid and accurate performance of robotic arm movement and positioning, coupled with a reliable manipulator gripping mechanism for variable orientation and a range of deformable and/or geometric and coloured products, will lead to smarter/intelligent operation of high precision equipment. The aim of the research is to design a more effective robot arm manipulator for use in a glovebox environment utilising control kinematics together with image processing / object recognition algorithms and in particular the work is aimed at improving the movement of the robot arm in the case of unresolved kinematics, seeking improved speed and performance of object recognition along with improved sensitivity of the manipulator gripper mechanism A virtual robot arm and associated workspace was designed within the LabView 2009 environment and prototype gripper arms were designed and analysed within the Solidworks 2009 environment. Visual information was acquired by barrel cameras. Field research determines the location of identically shaped objects, and the object recognition algorithms establish the difference between them. A touch/feel device installed within the gripper arm housing ensures that the applied force is adequate to securely grasp the object without damage, but also to adapt to any slippage whilst the manipulator moves within the robot workspace. The research demonstrates that complex operations can be achieved without the expense of specialised parts/components; and that implementation of control algorithms can compensate for any ambiguous signals or fault conditions that occur through the operation of the manipulator. The results show that system performance is determined by the trade-off between speed and accuracy. The designed system can be further utilised for control of multi-functional robots connected within a production line. The Graphic User Interface illustrated within the thesis can be customised by the supervisor to suit operational needs

An artificial development model for cell pattern generation

La formation de structures cellulaires a un rôle crucial dans le développement tant artificiel que naturel. Cette thèse présente un modèle de développement artificiel pour la génération de structures cellulaires basé sur le paradigme des automates cellulaires (AC). La croissance cellulaire est contrôlée par un génome comportant un réseau de régulation artificiel (RRA) et une série de gènes structurels. Ce génome a subi une évolution par algorithme génétique (AG) afin de produire des structures cellulaires en 2D grâce à l'activation et inhibition sélective des gènes. De plus des gradients morphogénétiques ont été utilisés pour fournir aux cellules une information de position permettant de contraindre leur reproduction. Après évolution d'un génome par algorithme génétique, une cellule unique est placée au milieu de la grille de l’AC où sa reproduction, contrôlée par le RRA, produit une structure cellulaire cible. Le modèle a été appliqué avec succès au problème classique de génération de la structure d’un drapeau français (French flag problem).Cell pattern formation has a crucial role in both artificial and natural development. This thesis presents an artificial development model for cell pattern generation based on the cellular automata (CA) paradigm. Cellular growth is controlled by a genome consisting of an artificial regulatory network (ARN) and a series of structural genes. The genome was evolved by a genetic algorithm (GA) in order to produce 2D cell patterns through the selective activation and inhibition of genes. Morphogenetic gradients were used to provide cells with positional information that constrained cellular replication. After a genome was evolved, a single cell in the middle of the CA lattice was allowed to reproduce controlled by the ARN until a cell pattern was formed. The model was applied to the canonical problem of growing a French flag pattern.

A gene regulatory network model for control

The activity of a biological cell is regulated by interactions between genes and proteins. In artificial intelligence, this has led to the creation of developmental gene regulatory network (GRN) models which aim to exploit these mechanisms to algorithmically build complex designs. The emerging field of GRNs for control aims to instead exploit these natural mechanisms and this ability to encode a large variety of behaviours within a single evolvable genetic program for the solution of control problems. This work aims to extend the application domain of GRN models to previously unsolved control problems; the focus will here be on reinforcement learning problems, in which the dynamics of the system controlled are kept from the controller and only sparse feedback is given to it. This category of problems closely matches the challenges faced by natural evolution in generating biological GRNs. Starting with an existing GRN model, the fractal GRN (FGRN) model, a successful application to a standard control problem will be presented, followed by multiple improvements to the FGRN model and its associated genetic algorithm, resulting in better performances in terms of both reliability and speed. Limitations will be identified in the FGRN model, leading to the introduction of the Input-Merge- Regulate-Output (IMRO) architecture for GRN models, an implementation of which will show both quantitative and qualitative improvements over the FGRN model, solving harder control problems. The resulting model also displays useful features which should facilitate further extension and real-world use of the system

Evolutionary synthesis of analog networks

The significant increase in the available computational power that took place in recent decades has been accompanied by a growing interest in the application of the evolutionary approach to the synthesis of many kinds of systems and, in particular, to the synthesis of systems like analog electronic circuits, neural networks, and, more generally, autonomous systems, for which no satisfying systematic and general design methodology has been found to date. Despite some interesting results in the evolutionary synthesis of these kinds of systems, the endowment of an artificial evolutionary process with the potential for an appreciable increase of complexity of the systems thus generated appears still an open issue. In this thesis the problem of the evolutionary growth of complexity is addressed taking as starting point the insights contained in the published material reporting the unfinished work done in the late 1940s and early 1950s by John von Neumann on the theory of self-reproducing automata. The evolutionary complexity-growth conditions suggested in that work are complemented here with a series of auxiliary conditions inspired by what has been discovered since then relatively to the structure of biological systems, with a particular emphasis on the workings of genetic regulatory networks seen as the most elementary, full-fledged level of organization of existing living organisms. In this perspective, the first chapter is devoted to the formulation of the problem of the evolutionary growth of complexity, going from the description of von Neumann's complexity-growth conditions to the specification of a set of auxiliary complexity-growth conditions derived from the analysis of the operation of genetic regulatory networks. This leads to the definition of a particular structure for the kind of systems that will be evolved and to the specification of the genetic representation for them. A system with the required structure — for which the name analog network is suggested — corresponds to a collection of devices whose terminals are connected by links characterized by a scalar value of interaction strength. One of the specificities of the evolutionary system defined in this thesis is the way these values of interaction strength are determined. This is done by associating with each device terminal of the evolving analog network a sequence of characters extracted from the sequences that constitute the genome representing the network, and by defining a map from pairs of sequences of characters to values of interaction strength. Whereas the first chapter gives general prescriptions for the definition of an evolutionary system endowed with the desired complexity-growth potential, the second chapter is devoted to the specification of all the details of an actual implementation of those prescriptions. In this chapter the structure of the genome and of the corresponding genetic operators are defined. A technique for the genetic encoding of the devices constituting the analog network is described, along with a way to implement the map that specifies the interaction between the devices of the evolved system, and between them and the devices constituting the external environment of the evolved system. The proposed implementation of the interaction map is based on the local alignment of sequences of characters. It is shown how the parameters defining the local alignment can be chosen, and what strategies can be adopted to prevent the proliferation of unwanted interactions. The third chapter is devoted to the application of the evolutionary system defined in the second chapter to problems aimed at assessing the suitability in an evolutionary context of the local alignment technique and to problems aimed at assessing the evolutionary potential of the complete evolutionary system when applied to the synthesis of analog networks. Finally, the fourth chapter briefly considers some further questions that are relevant to the proposed approach but could not be addressed in the context of this thesis. A series of appendixes is devoted to some complementary issues: the definition of a measure of diversity for an evolutionary population employing the genetic description introduced in this thesis; the choice of the quantizer for the values of interaction strength between the devices constituting the evolved analog network; the modifications required to use the analog electronic circuit simulator SPICE as a simulation engine for an evolutionary or an optimization process