Distributed associative memories for high-speed symbolic reasoning

This paper briefly introduces a novel symbolic reasoning system based upon distributed associative memories which are constructed from correlation matrix memories (CMM). The system is aimed at high-speed rule-based symbolic operations. It has the advantage of very fast rule matching without the long training times normally associated with neural-network-based symbolic manipulation systems. In particular, the network is able to perform partial matching on symbolic information at high speed. As such, the system is aimed at the practical use of neural networks in high-speed reasoning systems. The paper describes the advantages and disadvantages of using CMM and shows how the approach overcomes those disadvantages. It then briefly describes a system incorporating CMM

A study of the design and analysis of feed-forward neural networks

This thesis shows that a design and analysis system for feed forward neural networks is desirable, and that the currently available techniques do not work. Methods have been presented that solve the problem of analysis, showing that analysis is possible and desirable for classification networks. The biggest limitation is the size of the network and that the analysis tools are only applicable to properly designed classification systems. A method of reducing the size of classification networks is presented along with a design methodology for non classification systems

Design for novel enhanced weightless neural network and multi-classifier.

Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems. A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN. Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems

Rule-based integrated building management systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The introduction of building management systems in large buildings have improved the control of building services and provided energy savings. However, current building management systems are limited by the physical level of integration of the building's services and the lack of intelligence provided in the control algorithms. This thesis proposes a new approach to the design and operation of building management systems using rule-based artificial intelligence techniques. The main aim of is to manage the services in the building in a more co-ordinated and intelligent manner than is possible by conventional techniques. This approach also aims to reduce the operational cost of the building by automatically tuning the energy consumption in accordance with occupancy profile of the building. A rule-based design methodology is proposed for building management systems. The design adopts the integrated structure made possible by the introduction of a common communications network for building services. The 'intelligence' is coded in the form of rules in such a way that it is both independent of any specific building description and easy to facilitate subsequent modification and addition. This is achieved using an object-oriented approach and classifying the range of data available into defined classes. The rules are divided into two knowledge-bases which are concerned with the building's control and its facilities management respectively. A wide range of rule-based features are proposed to operate on this data structure and are classified in terms of the data classes on which they operate. The concepts presented in this thesis were evaluated using software simulations, mathematical analysis and some hardware implementation. The conclusions of this work are that a rule-based building management system could provide significant enhancements over existing systems in terms of energy savings and improvements for both the building's management staff and its occupants

The Five Tribes of Machine-Learning: A Brief Overview

This paper reviews recent advances in automated computer-based learning capabilities. It briefly describes and examines the strengths and weaknesses of the five principal algorithmic approaches to machine-learning, namely: connectionism; evolutionism; Bayesianism; analogism; and, symbolism. While each of these approaches can demonstrate some degree of learning, a learning capability that is comparable with human learning is still in its infancy and will likely require the combination of multiple algorithmic approaches. However, the current state reached in machine-learning suggests that Artificial General Intelligence and even Artificial Superintelligence may indeed be eventually feasible

Precis of neuroconstructivism: how the brain constructs cognition

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment