GA-optimization for rapid prototype system demonstration

An application of the Genetic Algorithm (GA) is discussed. A novel scheme of Hierarchical GA was developed to solve complicated engineering problems which require optimization of a large number of parameters with high precision. High level GAs search for few parameters which are much more sensitive to the system performance. Low level GAs search in more detail and employ a greater number of parameters for further optimization. Therefore, the complexity of the search is decreased and the computing resources are used more efficiently

Representing intelligent decision making in discrete event simulation : a stochastic neural network approach

The problem of representing decision making behaviour in discrete event simulation was investigated. Of particular interest was modelling variety in the decisions, where different people might make different decisions even where the same circumstances hold. An initial investigation of existing and alternative approaches for representing decision making was carried out. This led to the suggestion of using a neural network to represent the decision making behaviour in the form of a multi-criteria probability distribution based on data of observed decision making. The feasibility of the stochastic neural network approach was investigated. Models were fitted using artificial data from discrete and continuous distributions that included the shape parameters as inputs, and tested against known results from the distributions. Also a bank simulation was used to collect data from volunteers who controlled the queuing decisions of customers inside the bank. Models of their behaviour were created and implemented in the bank simulation to automate the decision making of customers. The investigation established the feasibility of the approach, although it indicated the need for substantial amounts of data showing examples of decision making. A hybrid model that combined the stochastic neural network approach with a rule-based approach allowed the development of more general models of decision making behaviour

Development of an Expert System Based Experimental Frame for Modeling of Manufacturing Systems

Industrial Engineering and Managemen

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence

Analysis and development of the Bees Algorithm for primitive fitting in point cloud models

This work addresses the problem of fitting a geometrical primitive to a point cloud as a numerical optimisation problem. Intelligent Optimisation Techniques like Evolutionary Algorithms and the Bees Algorithm were here adapted to select the most fit primitive out of a population of solutions, and the results compared. The necessity of understanding the dynamics of the Bees Algorithm to improve its performances and applicability led to an in-depth analysis of its key parts. A new mathematical definition of the algorithm led to the discovery and formalisation of several properties, many of which provided a mathematical answer to behaviours so far only observed in empirical tests. The implications of heuristics commonly used in the Bees Algorithm, like site abandonment and neighbourhood shrinking, were statistically analysed. The probability of a premature stalling of the local search at a site has been quantified under certain conditions. The effect of the choice of shape for the local neighbourhood on the exploitative search of the Bees Algorithm was analysed. The study revealed that this commonly overlooked aspect has profound consequences on the effectiveness of the local search, and practical applications have been suggested to address specific search problems. The results of the primitive fitting study, and the analysis of the Bees Algorithm, inspired the creation of a new algorithm for problems where multiple solutions are sought (multi-solution optimisation). This new algorithm is an ex- tension of the Bees Algorithm to multi-solution optimisation. It uses topological information on the search space gathered during the cycles of local search at a site, which is normally discarded, to alter the fitness function. The function is altered to discourage further search in already explored regions of the fitness landscape, and force the algorithm to discover new optima. This new algorithm found immediate application on the multi-shape variant of the primitive fitting problem. In a series of experimental tests, the new algorithm obtained promising results, showing its ability to find many shapes in a point cloud. It also showed its suitability as a general technique for the multi-solution optimisation problem

Portrayals and perceptions of cinematic artificial intelligence: a mixed-method analysis of I, Robot (2004) and Chappie (2015)

This study investigates the portrayal and perception of artificial intelligence (AI) in I, Robot (2004) and Chappie (2015), providing one of the first accounts of the causality between attitudes and expectations in the representation and reception of films about AI. The findings suggest that the level of optimism of a film is likely to be linked to its socio-cultural context. The humanoid representation of each robotic protagonist prevented each film from skewing too far towards the extremes of technological optimism or pessimism. This affected respondents’ attitudes immediately after viewership, but this affect was short-lived. Additionally, while portrayals of the future somewhat aligned to contemporary developments regarding weak AI, they were overly optimistic or pessimistic about the future of strong AI. This had little impact on respondents’ fears and expectations, as respondents used the films as visual aids to mentally depict abstract concepts relating to AI that were arrived at elsewhere.Communication ScienceM.A. (Communication Science

New Trends in Statistical Physics of Complex Systems

A topical research activity in statistical physics concerns the study of complex and disordered systems. Generally, these systems are characterized by an elevated level of interconnection and interaction between the parts so that they give rise to a rich structure in the phase space that self-organizes under the control of internal non-linear dynamics. These emergent collective dynamics confer new behaviours to the whole system that are no longer the direct consequence of the properties of the single parts, but rather characterize the whole system as a new entity with its own features, giving rise to the birth of new phenomenologies. As is highlighted in this collection of papers, the methodologies of statistical physics have become very promising in understanding these new phenomena. This volume groups together 12 research works showing the use of typical tools developed within the framework of statistical mechanics, in non-linear kinetic and information geometry, to investigate emerging features in complex physical and physical-like systems. A topical research activity in statistical physics concerns the study of complex and disordered systems. Generally, these systems are characterized by an elevated level of interconnection and interaction between the parts so that they give rise to a rich structure in the phase space that self-organizes under the control of internal non-linear dynamics. These emergent collective dynamics confer new behaviours to the whole system that are no longer the direct consequence of the properties of the single parts, but rather characterize the whole system as a new entity with its own features, giving rise to the birth of new phenomenologies. As is highlighted in this collection of papers, the methodologies of statistical physics have become very promising in understanding these new phenomena. This volume groups together 12 research works showing the use of typical tools developed within the framework of statistical mechanics, in non-linear kinetic and information geometry, to investigate emerging features in complex physical and physical-like systems