The misbehavior of value and the discipline of the will.

Most reinforcement learning models of animal conditioning operate under the convenient, though fictive, assumption that Pavlovian conditioning concerns prediction learning whereas instrumental conditioning concerns action learning. However, it is only through Pavlovian responses that Pavlovian prediction learning is evident, and these responses can act against the instrumental interests of the subjects. This can be seen in both experimental and natural circumstances. In this paper we study the consequences of importing this competition into a reinforcement learning context, and demonstrate the resulting effects in an omission schedule and a maze navigation task. The misbehavior created by Pavlovian values can be quite debilitating; we discuss how it may be disciplined.

Visualization Summit 2007: Ten research goals for 2010

At the first international Visualization Summit, more than 100 international researchers and practitioners defined and assessed nine original and important research goals in the context of Visualization Science, and proposed methods for achieving these goals by 2010. The synthesis of the whole event is presented in the 10th research goal. This article contributes a building block for systemizing visualization research by proposing mutually elaborated research goals with defined milestones. Such a consensus on where to go together is only one step toward establishing visualization science in the long-term perspective as a discipline with comparable relevance to chemistry, mathematics, language, or history. First, this article introduces the conference setting. Second, it describes the research goals and findings from the nine workshops. Third, a survey among 62 participants about the originality and importance of each research goal is presented and discussed. Finally, the article presents a synthesis of the nine research goals in the form of a 10th research goal, namely Visualizing Future Cities. The article is relevant for visualization researchers, trend scouts, research programme directors who define the topics that get funds. © 2007 Palgr aveMacmillan Ltd. All rights reserved.

Architectural research and its enemies

Like the Research Assessment Exercise (RAE) that preceded it, the UK government's proposed Research Excellence Framework (REF) is a means of allocating funding in higher education to support research. As with any method for the competitive allocation of funds it creates winners and losers and inevitably generates a lot of emotion among those rewarded or penalised. More specifically, the 'winners' tend to approve of the method of allocation and the 'losers' denigrate it as biased against their activities and generally unfair. An extraordinary press campaign has been consistently waged against research assessment and its methods by those involved in architectural education, which I will track over a decade and a half. What follows will question whether this campaign demonstrates the sophistication and superior judgment of those who have gone into print, or conversely whether its mixture of misinformation and disinformation reveals not just disenchantment and prejudice, but a naivety and a depth of ignorance about the fundamentals of research that is deeply damaging to the credibility of architecture as a research-based discipline. With the recent consultation process towards a new cycle of research assessment, the REF, getting under way, I aim to draw attention to the risk of repeating past mistakes. Copyright © Cambridge University Press 2010.

KM your way to CMMI

Purpose - The purpose of this paper is to describe two related fields - knowledge management (KM) and capability maturity model integrated (CMMISM) and highlight their imilarities. Design/methodology/approach - The KM framework used for this comparison is the one established and used at Israel Aircraft Industries, while the CMMISM source of information is none but the original document produced by the CMMISM product team at the Carnegie Mellon University, as well as papers published on the subject. Findings - Knowledge management is a rather young discipline promising to maximize innovation and competitive advantage to organizations that practice knowledge capture, documentation, retrieval and reuse, creation, transfer and share to its knowledge assets in a measurable way, integrated in its operational and business processes. The capability maturity model integrated deals with the ways an organization has to follow, in order to maintain well mapped processes, having well defined stages, because of the assumption that in mature organizations, it is possible to measure and relate between the quality of the process and the quality of the product. Though KM and CMMISM take different approaches to the achievement of competitive advantage, they seem to be supporting as well as dependent of each other. Originality/value - Practitioners as well as researchers in the field of knowledge management and in the implementation of the CMMISM standard will find comfort in realizing how mutually supportive are these two fields. © Emerald Group Publishing Limited.

Requirements-based development of an improved engineering change management method

Engineering changes (ECs) are essential in complex product development, and their management is a crucial discipline for engineering industries. Numerous methods have been developed to support EC management (ECM), of which the change prediction method (CPM) is one of the most established. This article contributes a requirements-based benchmarking approach to assess and improve existing methods. The CPM is selected to be improved. First, based on a comprehensive literature survey and insights from industrial case studies, a set of 25 requirements for change management methods are developed. Second, these requirements are used as benchmarking criteria to assess the CPM in comparison to seven other promising methods. Third, the best-in-class solutions for each requirement are investigated to draw improvement suggestions for the CPM. Finally, an enhanced ECM method which implements these improvements is presented. © 2013 © 2013 The Author(s). Published by Taylor & Francis.

AI in the 21st century - With historical reflections

The discipline of Artificial Intelligence (AI) was born in the summer of 1956 at Dartmouth College in Hanover, New Hampshire. Half of a century has passed, and AI has turned into an important field whose influence on our daily lives can hardly be overestimated. The original view of intelligence as a computer program - a set of algorithms to process symbols - has led to many useful applications now found in internet search engines, voice recognition software, cars, home appliances, and consumer electronics, but it has not yet contributed significantly to our understanding of natural forms of intelligence. Since the 1980s, AI has expanded into a broader study of the interaction between the body, brain, and environment, and how intelligence emerges from such interaction. This advent of embodiment has provided an entirely new way of thinking that goes well beyond artificial intelligence proper, to include the study of intelligent action in agents other than organisms or robots. For example, it supplies powerful metaphors for viewing corporations, groups of agents, and networked embedded devices as intelligent and adaptive systems acting in highly uncertain and unpredictable environments. In addition to giving us a novel outlook on information technology in general, this broader view of AI also offers unexpected perspectives into how to think about ourselves and the world around us. In this chapter, we briefly review the turbulent history of AI research, point to some of its current trends, and to challenges that the AI of the 21st century will have to face. © Springer-Verlag Berlin Heidelberg 2007.

FBS Linkage ontology and technique to support engineering change management

© 2014, Springer-Verlag London. Engineering changes are essential for any product development, and their management has become a crucial discipline. Research in engineering change management has brought about some methods and tools to support dealing with changes. This work extends the change prediction method through incorporation of a function–behaviour–structure (FBS) scheme. These additional levels of detail provide the rationales for change propagation and allow a more proactive management of changes. First, we develop the ontology of this method based on a comprehensive comparison of three seminal functional reasoning schemes. Then, we demonstrate the FBS Linkage technique by applying it to a diesel engine. Finally, we evaluate the method.