On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists

Author's accepted manuscript.This is a post-peer-review, pre-copyedit version of an article published in Evolving Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s12530-020-09327-4.acceptedVersio

A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme

-Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should move. One can differentiate the SPL from the traditional class of optimization problems by the fact that the former considers the case where the directional information, for example, as inferred from an Oracle (which possibly computes the derivatives), suffices to achieve the optimization—without actually explicitly computing any derivatives. The SPL can be described in terms of a LM (algorithm) attempting to locate a point on a line. The LM interacts with a random environment which essentially informs it, possibly erroneously, if the unknown parameter is on the left or the right of a given point. Given a current estimate of the optimal solution, all the reported solutions to this problem effectively move along the line to yield updated estimates which are in the neighborhood of the current solution.1 This paper proposes a dramatically distinct strategy, namely, that of partitioning the line in a hierarchical tree-like manner, and of moving to relatively distant points, as characterized by those along the path of the tree. We are thus attempting to merge the rich fields of stochastic optimization and data structures. Indeed, as in the original discretized solution to the SPL, in one sense, our solution utilizes the concept of discretization and operates a uni-dimensional controlled random walk (RW) in the discretized space, to locate the unknown parameter. However, by moving to nonneighbor points in the space, our newly proposed hierarchical stochastic searching on the line (HSSL) solution performs such a controlled RW on the discretized space structured on a superimposed binary tree. We demonstrate that the HSSL solution is orders of magnitude faster than the original SPL solution proposed by - ommen. By a rigorous analysis, the HSSL is shown to be optimal if the effectiveness (or credibility) of the environment, given by $p$ , is greater than the golden ratio conjugate. The solution has been both analytically solved and simulated, and the results obtained are extremely fascinating, as this is the first reported use of time reversibility in the analysis of stochastic learning. The learning automata extensions of the scheme are currently being investigated

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Browsing Digital Collections with Reconfigurable Faceted Thesauri

Faceted thesauri group classification terms into hierarchically arranged facets. They enable faceted browsing, a well-known browsing technique that makes it possible to navigate digital collections by recursively choosing terms in the facet hierarchy. In this paper we develop an approach to achieve faceted browsing in live collections, in which not only the contents but also the thesauri can be constantly reorganized. We start by introducing a digital collection model letting users reconfigure facet hierarchies. Then we introduce navigation automata as an efficient way of supporting faceted browsing in these collections. Since, in the worst-case, the number of states in these automata can grow exponentially, we propose two alternative indexing strategies able to bridge this complexity: inverted indexes and navigation dendrograms. Finally, by comparing these strategies in the context of Clavy, a system for managing collections with reconfigurable structures in digital humanities and educational settings, we provide evidence that navigation dendrogram organization outperforms the inverted index-based one

On the Theory and Applications of Hierarchical Learning Automata and Object Migration Automata

Paper III, IV and VIII are excluded due to copyright.The paradigm of Artificial Intelligence (AI) and the sub-group of Machine Learning (ML) have attracted exponential interest in our society in recent years. The domain of ML contains numerous methods, and it is desirable (and in one sense, mandatory) that these methods are applicable and valuable to real-life challenges. Learning Automata (LA) is an intriguing and classical direction within ML. In LA, non-human agents can find optimal solutions to various problems through the concept of learning. The LA instances learn through Agent-Environment interactions, where advantageous behavior is rewarded, and disadvantageous behavior is penalized. Consequently, the agent eventually, and hopefully, learns the optimal action from a set of actions. LA has served as a foundation for Reinforcement Learning (RL), and the field of LA has been studied for decades. However, many improvements can still be made to render these algorithms to be even more convenient and effective. In this dissertation, we record our research contributions to the design and applications within the field of LA. Our research includes novel improvements to the domain of hierarchical LA, major advancements to the family of Object Migration Automata (OMA) algorithms, and a novel application of LA, where it was utilized to solve challenges in a mobile radio communication system. More specifically, we introduced the novel Hierarchical Discrete Pursuit Automaton (HDPA), which significantly improved the state of the art in terms of effectiveness for problems with high accuracy requirements, and we mathematically proved its ϵ-optimality. In addition, we proposed the Action Distribution Enhanced (ADE) approach to hierarchical LA schemes which significantly reduced the number of iterations required before the machines reached convergence. The existing schemes in the OMA paradigm, which are able to solve partitioning problems, could only solve problems with equally-sized partitions. Therefore, we proposed two novel methods that could handle unequally-sized partitions. In addition, we rigorously summarized the OMA domain, outlined its potential benefits to society, and listed further development cases for future researchers in the field. With regard to applications, we proposed an OMA-based approach to the grouping and power allocation in Non-orthogonal Multiple Access (NOMA) systems, demonstrating the applicability of the OMA and its advantage in solving fairly complicated stochastic problems. The details of these contributions and their published scientific impacts will be summarized in this dissertation, before we present some of the research contributions in their entirety.publishedVersio

Smart Business Networks Design and Business Genetics

With the emergence of smart business networks, agile networks, etc. as important research areas in management, for all the attractiveness of these concepts, a major issue remains around their design and the selection rules. While smart business networks should provide advantages due to the quick connect of business partners for selected functions in a process common to several parties, literature does not provide constructive methods whereby the selection of temporary partners and functions can be done. Most discussions only rely solely on human judgment. This paper introduces both computational geometry, and genetic programming, as systematic methods whereby to display possible partnerships, and also whereby to plan for their effect on the organizations or functions of those involved. The two techniques are also been put in the context of emergence theory. Business maps address the first challenge with the use of Voronoï diagrams. Cellular automata, with genetic algorithms mimicking living bodies, address the second challenge. This paper does not include experimental results, which have been derived in the high tech area to determine especially the adequateness of systems integrators to set up joint ventures with smaller technology suppliers

Intelligent Diagnosis Systems

This paper examines and compares several different approaches to the design of intelligent systems for diagnosis applications. These include expert systems (or knowledge-based systems), truth (or reason) maintenance systems, case-based reasoning systems, and inductive approaches like decision trees, artificial neural networks (or connectionist systems), and statistical pattern classification systems. Each of these approaches is demonstrated through the design of a system for a simple automobile fault diagnosis task. The paper also discusses the domain characteristics and design and performance requirements that influence the choice of a specific technique (or a combination of techniques) for a given application

Comments on the cybernetics of stability and regulation in social systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The methods and principles of cybernetics are applied to a discussion of stability and regulation in social systems taking a global viewpoint. The fundamental but still classical notion of stability as applied to homeostatic and ultrastable systems is discussed, with a particular reference to a specific well-studied example of a closed social group (the Tsembaga studied by Roy Rappaport in New Guinea). The discussion extends to the problem of evolution in large systems and the question of regulating evolution is addressed without special qualifications. A more comprehensive idea of stability is introduced as the argument turns to the problem of evolution for viability in general. Concepts pertaining to the problem of evolution are exemplified by a computer simulation model of an abstractly defined ecosystem in which various dynamic processes occur allowing the study of adaptive and evolutionary behaviour. In particular, the role of coalition formation and cooperative behaviour is stressed as a key factor in the evolution of complexity. The model consists of a population of several species of dimensionless automata inhabiting a geometrically defined environment in which a commodity essential for metabolic requirements (food) appears. Automata can sense properties of their environment, move about it, compete for food, reproduce or combine into coalitions thus forming new and more complex species. Each species is associated with a specific genotype from which the species’ behavioural characteristics (its phenotype) are derived. Complexity and survival efficiency of species increases through coalition formation, an event which occurs when automata are faced with an “undecidable” situation that is resolvable only by forming a new and more complex organization. Exogenous manipulation of the food distribution pattern and other critical factors produces different environmental conditions resulting in different behaviour patterns of automata and in different evolutionary “pathways.” Eve-1, the computer program developed to implement this model, accepts a high-level command language which allows for the setting of parameters, definition of initial configurations, and control of output formats. Results of simulation are produced graphically and include various pertinent tables. The program was given a modular hierarchical structure which allows easy generation of new versions incorporating different sets of rules. The model strives to capture the essence of the evolution of complexity viewed as a general process rather than to describe the evolution of a particular “real” system. In this respect it is not context-specific, and the behaviours which are observable in different runs can receive various interpretation depending on specific identifications. Of these, biological, ecological, and sociological interpretations are the most obvious and the latter, in particular, is stressed.J. M. Kaplan Fund in New Yor

Computability and Evolutionary Complexity: Markets As Complex Adaptive Systems (CAS)

The purpose of this Feature is to critically examine and to contribute to the burgeoning multi disciplinary literature on markets as complex adaptive systems (CAS). Three economists, Robert Axtell, Steven Durlauf and Arthur Robson who have distinguished themselves as pioneers in different aspects of how the thesis of evolutionary complexity pertains to market environments have contributed to this special issue. Axtell is concerned about the procedural aspects of attaining market equilibria in a decentralized setting and argues that principles on the complexity of feasible computation should rule in or out widely held models such as the Walrasian one. Robson puts forward the hypothesis called the Red Queen principle, well known from evolutionary biology, as a possible explanation for the evolution of complexity itself. Durlauf examines some of the claims that have been made in the name of complex systems theory to see whether these present testable hypothesis for economic models. My overview aims to use the wider literature on complex systems to provide a conceptual framework within which to discuss the issues raised for Economics in the above contributions and elsewhere. In particular, some assessment will be made on the extent to which modern complex systems theory and its application to markets as CAS constitutes a paradigm shift from more mainstream economic analysis