ZCS redux

Learning classifier systems traditionally use genetic algorithms to facilitate rule discovery, where rule fitness is payoff based. Current research has shifted to the use of accuracy-based fitness. This paper re-examines the use of a particular payoff-based learning classifier system - ZCS. By using simple difference equation models of ZCS, we show that this system is capable of optimal performance subject to appropriate parameter settings. This is demonstrated for both single- and multistep tasks. Optimal performance of ZCS in well-known, multistep maze tasks is then presented to support the findings from the models

Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems

A brief history of learning classifier systems: from CS-1 to XCS and its variants

© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning

GAN-BASED POINT-OF-LOAD CONVERTERS FOR DATA CENTER APPLICATIONS

The growth of the information technology sector has increased demand for high-density, high-efficiency point-of-load (POL) converters. As industry continues to demand an increase in server processing power, high-current operation presents challenges to designing high-efficiency POL converters. Increased conduction and overlap losses induce significant power losses in high-power modes. The introduction of Gallium-Nitride (GaN) switching devices and the implementation of zero-current-switching (ZCS) topologies for POL applications have the potential to improve converter efficiency while maintaining or surpassing the industrial power density standard. This thesis addresses the challenges presented by high-current operation by demonstrating an accurate power loss model of the quasi-resonant zero-current-switching (QR-ZCS) buck converter and presents a comparison between the synchronous buck and QR-ZCS buck in a 5-1.8 V POL application

MILCS: A mutual information learning classifier system

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. MILCS's design draws on an analogy to the structural learning approach of cascade correlation networks. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems. Copyright 2007 ACM

Real estate investment dynamics

This thesis is motivated by the steadily increasing interest in the dynamic relationship between the macro-economy and the real estate sector. One of the main issues in this respect is to study the investment dynamics. Since the bursting of the U.S. housing bubble in 2006 is identified as the point of origin of the so called subprime crises, which led to the collapse of the U.S. financial system, the dynamics of real estate investments is of particular interest. In the first part of my thesis I investigate the dynamics of residential investment and its relationship to the overall economy by the means of a dynamic stochastic general equilibrium (DSGE) model in which a consumption good sector and a housing sector are incorporated. Residential investment is characterized in this model by a time-to-build restriction. The model is brought to U.S. quarterly data - in the period 1970 - 2007 - in order to evaluate whether it can account for stylized facts of the U.S. housing economy as well as the U.S. Macro - economy. Another much talked real estate topic with respect to the subprime crisis is the relationship between bank lending, property prices and economic activity. To that end, the second part of my thesis examines the potential effects of macro-policy and bank lending shocks on the German real estate sector. In particular, the importance of macroeconomic factors like credit to real estate construction, residential investment, and gross domestic product for the dynamics of German commercial real estate prices are analyzed by the means of a structural Vector-Autoregression (SVAR). The SVAR estimation is conduct for both, aggregate Germany and the largest regional states of Bavaria and Nordrhein-Westfalen for the period 1975 to 2004

Learning classifier systems from first principles: A probabilistic reformulation of learning classifier systems from the perspective of machine learning

Learning Classifier Systems (LCS) are a family of rule-based machine learning methods. They aim at the autonomous production of potentially human readable results that are the most compact generalised representation whilst also maintaining high predictive accuracy, with a wide range of application areas, such as autonomous robotics, economics, and multi-agent systems. Their design is mainly approached heuristically and, even though their performance is competitive in regression and classification tasks, they do not meet their expected performance in sequential decision tasks despite being initially designed for such tasks. It is out contention that improvement is hindered by a lack of theoretical understanding of their underlying mechanisms and dynamics.EThOS - Electronic Theses Online ServiceGBUnited Kingdo