We study the emergence of shared representations in a population of agents engaged in a supervised classification task, using a model called the classification game. We connect languages with tasks by treating the agents’ classification hypothesis space as an information channel. We show that by learning through the classification game, agents can implicitly perform complexity regularisation, which improves generalisation. Improved generalisation also means that the languages that emerge are well adapted to the given task. The improved language-task fit springs from the interplay of two opposing forces: the dynamics of collective learning impose a preference for simple representations, while the intricacy of the classification task imposes a pressure towards representations that are more complex. The push–pull of these two forces results in the emergence of a shared representation that is simple but not too simple. Our agents use artificial neural networks to solve the classification tasks they face, and a simple counting algorithm to learn a language as a form-meaning mapping. We present several experiments to demonstrate that both compositional and holistic languages can emerge in our system. We also demonstrate that the agents avoid overfitting on noisy data, and can learn some very difficult tasks through interaction, which they are unable to learn individually. Further, when the agents use simple recurrent networks to solve temporal classification tasks, we see the emergence of a rudimentary grammar, which does not have to be explicitly learned.

Abstract The classic problem within the information quality (IQ) research and practice community has been the problem of defining IQ. It has been found repeatedly that IQ is context sensitive and cannot be described, measured, and assured with a single model. There is a need for empirical case studies of IQ work in different systems to develop a systematic knowledge that can then inform and guide the construction of context‐specific IQ models. This article analyzes the organization of IQ assurance work in a large‐scale, open, collaborative encyclopedia—Wikipedia. What is special about Wikipedia as a resource is that the quality discussions and processes are strongly connected to the data itself and are accessible to the general public. This openness makes it particularly easy for researchers to study a particular kind of collaborative work that is highly distributed and that has a particularly substantial focus, not just on error detection but also on error correction. We believe that the study of those evolving debates and processes and of the IQ assurance model as a whole has useful implications for the improvement of quality in other more conventional databases.

Abstract One cannot manage information quality (IQ) without first being able to measure it meaningfully and establishing a causal connection between the source of IQ change, the IQ problem types, the types of activities affected, and their implications. In this article we propose a general IQ assessment framework. In contrast to context‐specific IQ assessment models, which usually focus on a few variables determined by local needs, our framework consists of comprehensive typologies of IQ problems, related activities, and a taxonomy of IQ dimensions organized in a systematic way based on sound theories and practices. The framework can be used as a knowledge resource and as a guide for developing IQ measurement models for many different settings. The framework was validated and refined by developing specific IQ measurement models for two large‐scale collections of two large classes of information objects: Simple Dublin Core records and online encyclopedia articles.

Publicly accessible bug report repositories maintained by free / open source development communities provide vast stores of data about distributed software problem management (SWPM). Qualitative analysis of individual bug reports, texts that record community responses to reported software problems, shows how this distributed community uses its SWPM process to manage software quality. We focus on the role of one basic social process, negotiation, in SWPM. We report on the varieties and frequencies of negotiation practices and demonstrate how instances of negotiation in different contexts affect the organization of information, the allocation of community resources, and the disposition of software problems.

One problem with existing clustering methods is that the interpretation of clusters may be difficult. Two different approaches have been used to solve this problem: conceptual clustering in machine learning and clustering visualization in statistics and graphics. The purpose of this paper is to investigate the benefits of combining clustering visualization and conceptual clustering to obtain better cluster interpretations. In our research we have combined concept trees for conceptual clustering with shaded similarity matrices for visualization. Experimentation shows that the two interpretation approaches can complement each other to help us understand data better.

An updated version of a large bibliography of articles and books related to distributed artificial intelligence (DAI) is presented. It is intended to be a complete and comprehensive list of research on DAI. It does not contain references to research on distributed computing, parallel processing for AI, connectionism, general AI, or research in social science disciplines that are important to DAI such as sociology, symbolic interactionism, or economics.>

Most computing serves as a resource or tool to support other work: performing complex analyses for engineering projects, preparing documents, or sending electronic mail using office automation equipment, etc. To improve the character, quality, and ease of computing work, we must understand how automated systems actually are integrated into the work they support. How do people actually adapt to computing as a resource? How do they deal with the unreliability in hardware, software, or operations; data inaccuracy; system changes; poor documentation; inappropriate designs; etc.; which are present in almost every computing milieu, even where computing is widely used and considered highly successful? This paper presents some results of a detailed empirical study of routine computer use in several organizations. We present a theoretical account of computing work and use it to explain a number of observed phenomena, such as: How people knowingly use “false” data to obtain desired analytical results by tricking their systems. How organizations come to rely upon complex, critical computer systems despite significant, recurrent, known errors and inaccurate data. How people work around inadequate computing systems by using manual or duplicate systems, rather than changing their systems via maintenance or enhancement. In addition, the framework for analyzing computing and routine work presented here proves useful for representing and reasoning about activity in multiactor systems in general, and in understanding how better to integrate organizations of people and computers in which work is coordinated.