Improving the Scalability of XCS-Based Learning Classifier Systems

Using evolutionary intelligence and machine learning techniques, a broad range of intelligent machines have been designed to perform different tasks. An intelligent machine learns by perceiving its environmental status and taking an action that maximizes its chances of success. Human beings have the ability to apply knowledge learned from a smaller problem to more complex, large-scale problems of the same or a related domain, but currently the vast majority of evolutionary machine learning techniques lack this ability. This lack of ability to apply the already learned knowledge of a domain results in consuming more than the necessary resources and time to solve complex, large-scale problems of the domain. As the problem increases in size, it becomes difficult and even sometimes impractical (if not impossible) to solve due to the needed resources and time. Therefore, in order to scale in a problem domain, a systemis needed that has the ability to reuse the learned knowledge of the domain and/or encapsulate the underlying patterns in the domain. To extract and reuse building blocks of knowledge or to encapsulate the underlying patterns in a problem domain, a rich encoding is needed, but the search space could then expand undesirably and cause bloat, e.g. as in some forms of genetic programming (GP). Learning classifier systems (LCSs) are a well-structured evolutionary computation based learning technique that have pressures to implicitly avoid bloat, such as fitness sharing through niche based reproduction. The proposed thesis is that an LCS can scale to complex problems in a domain by reusing the learnt knowledge from simpler problems of the domain and/or encapsulating the underlying patterns in the domain. Wilson’s XCS is used to implement and test the proposed systems, which is a well-tested, online learning and accuracy based LCS model. To extract the reusable building blocks of knowledge, GP-tree like, code-fragments are introduced, which are more than simply another representation (e.g. ternary or real-valued alphabets). This thesis is extended to capture the underlying patterns in a problemusing a cyclic representation. Hard problems are experimented to test the newly developed scalable systems and compare them with benchmark techniques. Specifically, this work develops four systems to improve the scalability of XCS-based classifier systems. (1) Building blocks of knowledge are extracted fromsmaller problems of a Boolean domain and reused in learning more complex, large-scale problems in the domain, for the first time. By utilizing the learnt knowledge from small-scale problems, the developed XCSCFC (i.e. XCS with Code-Fragment Conditions) system readily solves problems of a scale that existing LCS and GP approaches cannot, e.g. the 135-bitMUX problem. (2) The introduction of the code fragments in classifier actions in XCSCFA (i.e. XCS with Code-Fragment Actions) enables the rich representation of GP, which when couples with the divide and conquer approach of LCS, to successfully solve various complex, overlapping and niche imbalance Boolean problems that are difficult to solve using numeric action based XCS. (3) The underlying patterns in a problem domain are encapsulated in classifier rules encoded by a cyclic representation. The developed XCSSMA system produces general solutions of any scale n for a number of important Boolean problems, for the first time in the field of LCS, e.g. parity problems. (4) Optimal solutions for various real-valued problems are evolved by extending the existing real-valued XCSR system with code-fragment actions to XCSRCFA. Exploiting the combined power of GP and LCS techniques, XCSRCFA successfully learns various continuous action and function approximation problems that are difficult to learn using the base techniques. This research work has shown that LCSs can scale to complex, largescale problems through reusing learnt knowledge. The messy nature, disassociation of message to condition order, masking, feature construction, and reuse of extracted knowledge add additional abilities to the XCS family of LCSs. The ability to use rich encoding in antecedent GP-like codefragments or consequent cyclic representation leads to the evolution of accurate, maximally general and compact solutions in learning various complex Boolean as well as real-valued problems. Effectively exploiting the combined power of GP and LCS techniques, various continuous action and function approximation problems are solved in a simple and straight forward manner. The analysis of the evolved rules reveals, for the first time in XCS, that no matter how specific or general the initial classifiers are, all the optimal classifiers are converged through the mechanism ‘be specific then generalize’ near the final stages of evolution. Also that standard XCS does not use all available information or all available genetic operators to evolve optimal rules, whereas the developed code-fragment action based systems effectively use figure and ground information during the training process. Thiswork has created a platformto explore the reuse of learnt functionality, not just terminal knowledge as present, which is needed to replicate human capabilities

The CSR-Firm Performance Missing Link: Complementarity Between Environmental, Social and Business Behavior Criteria?

This article analyses the complementarity between various dimensions of corporate social responsibility (CSR) and financial performance. We hypothesise that the absence of consensus in the empirical literature on the CSR-financial performance relationship may be explained by the existence of synergies (complementarity) and trade-offs (substitutability) between the different CSR components. We investigate such relationships using a sample of 595 firms from 15 European countries over the 2002-2007 period. The results suggest some kind of trade-offs between CSR components. Some CSR combinations appear as relative complements, human resources and business behaviour towards customers and suppliers, suggesting mutual benefits and less conflicts between those stakeholders. Conversely, environment and business behaviour towards customers and suppliers appear as relative substitutes, suggesting more conflict or over investment between such types of stakeholders

On-line distributed hierarchical control and optimisation of large scale systems

This research is concerned with the application of closed-loop coordination techniques for on-line steady state optimisation and control of large scale systems using a micro-computer based system. A two-level hierarchical computer structure consisting of a coordinator at the supremal (upper) level and two local decision units at the infimal (lower) level had been established. Parallel computation were performed at the local decision unit level once the coordination parameters had been received from the supremal level. A steady state system model consisting of two interconnected subprocesses, simulated oy an analogue computer, was used to investigate the coordination methods for closed-loop hierarchical control. First-order time constants were introduced to the interaction inputs and the controls within the simulated subprocesses. Investigations had been carried out to study closed loop control using the Interaction Prediction and Interaction Balance coordination method. Special attention was given to the study of the problems associated with synchronisation of the local decision units for closed-loop control. Stability aspects of both coordination methods when subjected to disturbances in the controls and interconnections had been investigated~ Problems relating to system transient and local. decision asynchronisation, as well as their effects on system stability and convergence of the two tasks, namely the local decision task and the coordinator task had also been investigated. Methods for dealing with these problems had been suggested. The sub-optimality, convergency and robustness properties of each coordination method had been discussed. This research has demonstrated that the Interaction Prediction coordination methods are best suited for on-line distributed optimising control of large scale interconnected systems. Using the local feedback scheme, complete decentralisation at the local decision level operated asynchronou.sly can be achieved with the Interaction Prediction coordination method

A perturbation analysis of stochastic matrix Riccati diffusions

Matrix differential Riccati equations are central in filtering and optimal control theory. The purpose of this article is to develop a perturbation theory for a class of stochastic matrix Riccati diffusions. Diffusions of this type arise, for example, in the analysis of ensemble Kalman-Bucy filters since they describe the flow of certain sample covariance estimates. In this context, the random perturbations come from the fluctuations of a mean field particle interpretation of a class of nonlinear diffusions equipped with an interacting sample covariance matrix functional. The main purpose of this article is to derive non-asymptotic Taylor-type expansions of stochastic matrix Riccati flows with respect to some perturbation parameter. These expansions rely on an original combination of stochastic differential analysis and nonlinear semigroup techniques on matrix spaces. The results here quantify the fluctuation of the stochastic flow around the limiting deterministic Riccati equation, at any order. The convergence of the interacting sample covariance matrices to the deterministic Riccati flow is proven as the number of particles tends to infinity. Also presented are refined moment estimates and sharp bias and variance estimates. These expansions are also used to deduce a functional central limit theorem at the level of the diffusion process in matrix spaces

Probing morphology-dependent aggregation and photocurrent generation in polymer/fullerene photovoltaic devices

In this dissertation, new spectroscopic and electrical imaging approaches were developed to map morphology-dependent aggregation properties of polymer chains in model solar cell devices. These techniques reveal new correlations between local structure and material performance on sub-micron size scales which are not accessible by other techniques. Resonance Raman spectroscopic imaging was developed as a physical probe to identify and spatially map morphology-dependent variations of intra- and interchain interactions and order in poly-3-hexylthiophene (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) photovoltaic blend thin films. The C=C band of P3HT backbone was decomposed into aggregated and unaggregated component contribution at ~1450 cm-1 and ~1470 cm-1, respectively. The ratio, R, is used to report on the relative densities of states (DOS) of aggregated and unaggregated species. From both R and frequency dispersion resonance Raman images of these individual species, four distinct types of P3HT chains are identified and mapped in annealed P3HT/PCBM blend thin films: i) highly aggregated/crystalline; ii) partially aggregated; iii) interfacial; and iv) unaggregated/PCBM-rich. Secondly, the effect of aggregated and unaggregated species of P3HT on photocurrent is explored by a combined resonance Raman-photocurrent imaging (RRPI) approach. Maps of R values and photocurrents are generated for both as-cast and annealed P3HT/PCBM devices that permit direct spatial correlations between the P3HT aggregation state and local photocurrent generation efficiency. Regions of increased P3HT aggregation are observed at both P3HT/PCBM interfaces and in P3HT-rich areas that result in decreased photocurrent generation. Voltage-dependent RRPI studies are also performed at several applied bias levels that reveal distinct changes in photocurrents due to morphology-dependent charge mobility characteristics. Thirdly, the effect of composition of P3HT: PCBM on aggregation and of P3HT and corresponding solar cells is studied. P3HT: PCBM thin film solar cells of variable weight/weight (w/w) compositions (i.e., 1:1 to 1:4) were fabricated to systematically perturb polymer packing (aggregation) properties. On average, increasing the PCBM weight fraction, Raman spectra in the dominant P3HT C=C stretching mode region (~1450—1470 cm-1) whereas symmetric stretching C-C modes show decreased intensities and red shifts. Raman bands of P3HT C=C modes can likewise be decomposed into contributions from both aggregated and unaggregated chains and, R values decrease with increased PCBM content. Most aggregated (ordered) P3HT chains reside primarily outside PCBM-rich regions but, reverses for \u3e1:1 PCBM w/w loadings where all aggregated P3HT chains reside within PCBM-rich regions. This effect is attributed to a change in the type of P3HT aggregation from inter- to primarily intra-chain. The results reveal that the polymer aggregation state and its spatial location in the film that together have a large impact on charge transport properties and material performance. Lastly, intensity modulated photocurrent spectroscopy (IMPS) and imaging is used to study the aggregation effect on charge transport and recombination processes in P3HT/PCBM devices. This frequency-domain technique provides access to both bulk and interfacial charge transport and correlations between frequency-dependent photocurrent and local structure are revealed. Maps of the photocurrent and phase shift were recorded at several modulation frequencies spanning ~100 Hz up to 10 KHz. It was found that recombination processes involving trapped charge dominates the IMPS profiles. Temperature- and color-dependent IMPS are now being performed to better understand charge transport mechanisms

Periodic control of the individual-blade-control helicopter rotor

Results of an investigation into methods of controller design for an individual helicopter rotor blade in the high forward-flight speed regime are described. This operating condition poses a unique control problem in that the perturbation equations of motion are linear with coefficients that vary periodically with time. The design of a control law was based on extensions to modern multivariate synthesis techniques and incorporated a novel approach to the reconstruction of the missing system state variables. The controller was tested on both an electronic analog computer simulation of the out-of-plane flapping dynamics, and on a four foot diameter single-bladed model helicopter rotor in the M.I.T. 5x7 subsonic wind tunnel at high levels of advance ratio. It is shown that modal control using the IBC concept is possible over a large range of advance ratios with only a modest amount of computational power required

Three Essays on Strategic Human Capital, Managers and Competitive Advantage

In this dissertation, I investigate the interplay between strategic human capital and the role of managers in an organization. In Essay 1, using a natural experiment setting with a dataset on change of interdependence that an organization requires, and unexpected employee exit in a professional sports league for the period 1992 to 2010, I examine the consequence of losing strategically important human resources (HR) and shows that how specific organizational recovering techniques for dealing with HR can help the firm’s strategic renewal process. The data present that the consequential impact of losing employees is depending upon the type of interdependence that organization relies on, which are pooled interdependence and reciprocal interdependence. Furthermore, the results indicate that: (1) during the individual-focused period (pooled interdependence), loss of star employees harms organizational performance, but this harm can be mitigated by strong resource-picking skill, and (2) during the collaboration-focused period (reciprocal interdependence), loss of non-star employees harms organizational performance, but this harm can be mitigated by strong capability-building skill. In Essay 2, I try to answer following question: when promoted to management, do former star performers become superior managers? If so, why? Using performance data from a professional sports league, this study finds that organizational performance is greater under star-performers-turned-managers (SPTM’s) than other managers. Organizational performance is driven by the visibility of the manager’s prior career to employees for SPTM’s only, but driven by managerial competence for other managers only, suggesting a substitution effect between skill and inspirational role modeling. Consistent with social-comparison and self-enhancement theories, this inspirational role-modeling effect of SPTM’s on performance is contingent upon the need for self-enhancement by subordinates, and situational salience of the manager’s stardom. The results are consistent across robustness checks that control for potential selection issues, endogeneity concerns, and outliers. In Essay 3, I assess the causal impact of stakeholder orientation on the impact of corporate social responsibility and CEOs’ wealth and prominence. To obtain exogenous variation in stakeholder orientation, I exploit the enactment of state-level constituency statutes, which allow corporate executives and directors to consider non-shareholders’ interests when making business decisions. Using a cross-section of Texan firms during 2002-2012, I have found that the enactment of constituency statutes leads to significant increases in the quality of a firm’s corporate social responsibility (CSR); however, the effect of CSR does not necessarily lead to superior firm performance or value. I further argue and provide evidence suggesting that the obligated stakeholder orientation decreases the impact of CSR on CEOs’ compensation but increases the impact of CSR on CEOs’ media exposure. Finally, I posit that the impact of non-shareholder orientation on CEOs’ wealth and prominence is salient in non-consumer-focused industries

Program Annual Technology Report: Physics of the Cosmos Program Office

From ancient times, humans have looked up at the night sky and wondered: Are we alone? How did the universe come to be? How does the universe work? PCOS focuses on that last question. Scientists investigating this broad theme use the universe as their laboratory, investigating its fundamental laws and properties. They test Einsteins General Theory of Relativity to see if our current understanding of space-time is borne out by observations. They examine the behavior of the most extreme environments supermassive black holes, active galactic nuclei, and others and the farthest reaches of the universe, to expand our understanding. With instruments sensitive across the spectrum, from radio, through infrared (IR), visible light, ultraviolet (UV), to X rays and gamma rays, as well as gravitational waves (GWs), they peer across billions of light-years, observing echoes of events that occurred instants after the Big Bang. Last year, the LISA Pathfinder (LPF) mission exceeded expectations in proving the maturity of technologies needed for the Laser Interferometer Space Antenna (LISA) mission, and the Laser Interferometer Gravitational-Wave Observatory (LIGO) recorded the first direct measurements of long-theorized GWs. Another surprising recent discovery is that the universe is expanding at an ever-accelerating rate, the first hint of so-called dark energy, estimated to account for 75% of mass-energy in the universe. Dark matter, so called because we can only observe its effects on regular matter, is thought to account for another20%, leaving only 5% for regular matter and energy. Scientists now also search for special polarization in the cosmic microwave background to support the notion that in the split-second after the Big Bang, the universe inflated faster than the speed of light! The most exciting aspect of this grand enterprise today is the extraordinary rate at which we can harness technologies to enable these key discoveries

Матеріали 1-го симпозіуму з передових освітніх технологій - Том 1: AET

Матеріали 1-го симпозіуму з передових освітніх технологій.Proceedings of the 1st Symposium on Advances in Educational Technology

1995 BRAC Commission

Naval Air Warfare Center, China Lake, CA - Data Call #13. Box 125, L-052