Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

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Reinforcement Learning: An Introduction

Basics of qualitative research: Grounded theory procedures and techniques

Data Mining - Concepts and Techniques.

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Mitsuru Kodama Published by Edward Elgar, Cheltenham, U.K. and Northampton, MA, 2007 ISBN: 978 1 84542 929 4, 244+xiii pp, Price £59.95Mitsuru Kodama has moved into academia relatively recently, af...

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.kmrp.8500148.pdf

Knowledge innovation: strategic management as practice

Knowledge elicitation is a well-known bottleneck in the production of knowledge-based systems (KBS). Past research has shown that visual interactive simulation (VIS) could effectively be used to elicit episodic knowledge that is appropriate for machine learning purposes, with a view to building a KBS. Nonetheless, the VIS-based elicitation process still has much room for improvement. Based in the Ford Dagenham Engine Assembly Plant, a research project is being undertaken to investigate the individual/joint effects of visual display level and mode of problem case generation on the elicitation process. This paper looks at the methodology employed and some issues that have been encountered to date. Copyright © 2007 Inderscience Enterprises Ltd.

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Improving the use of Visual Interactive Simulation as a knowledge elicitation tool

Studies of Britain's accounting history date from the second half of the nineteenth century. Up to 1970 contributions to what became labelled the ‘traditional’ accounting history literature came from authors with diverse backgrounds – for example, archivists, civil servants, economists, (non-accounting) historians, lawyers, librarians, government employees and accounting practitioners. Accounting remained on the periphery of academe, through to the 1960s, thereby explaining the lack of historiographical contributions from accounting faculty. The theoretical underpinning of historical studies was increasingly recognised, post-1970, with investigations explicitly grounded in economics by the so-called neoclassicists. The second half of the 1980s, in Britain, saw a methodological revolution in the study of accounting's past accelerate through a movement christened ‘The new accounting history’. Key venues for the circulation and publication of studies informed by theories taken from numerous other disciplines included the Interdisciplinary Perspectives on Accounting Conference, first held in Manchester in 1985, and support provided by Accounting, Organizations and Society and other high-ranking academic journals for the publication of historical studies into the behavioural, organisational and sociological positioning of accounting. Participation in accounting history research by academics from other sectors of academe has featured prominently in the growth in interdisciplinarity.

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British traditions and contributions to accounting history research

Are the Knowledge Management Times “A-Changin’”?

This chapter discusses the creation of artificial intelligence (AI) systems to support the management of strategic risk in smart cities. The use of AI in smart cities is growing and the applications to several sectors are booming: from analyzing data from sensors in bridges for the maintenance process to health care at home or security and safety of the citizenry. The presents examples of various events affecting a city’s performance and show how, with AI use, it is possible to provide a better understanding of the impact of decisions in managing strategic risk. The organization of the performance measurement and strategic objectives set is based on an adaptation of the balanced scorecard (BSC) to municipal administrations. The measurement process is crucial for managing the city and keeping stakeholders satisfied. Measurement generates metrics and indicators. These are potentially affected by uncertainty, and the variation of the expected results is what strategic risk management is looking to assess and control.

John S. Edwards

Papers

Knowledge innovation: strategic management as practice

Improving the use of Visual Interactive Simulation as a knowledge elicitation tool

British traditions and contributions to accounting history research

Are the Knowledge Management Times “A-Changin’”?

AI in Smart Cities Development: A Perspective of Strategic Risk Management