A Unified Framework for Multi-Agent Agreement

Multi-Agent Agreement problems (MAP) - the ability of a population of agents to search out and converge on a common state - are central issues in many multi-agent settings, from distributed sensor networks, to meeting scheduling, to development of norms, conventions, and language. While much work has been done on particular agreement problems, no unifying framework exists for comparing MAPs that vary in, e.g., strategy space complexity, inter-agent accessibility, and solution type, and understanding their relative complexities. We present such a unification, the Distributed Optimal Agreement Framework, and show how it captures a wide variety of agreement problems. To demonstrate DOA and its power, we apply it to two well-known MAPs: convention evolution and language convergence. We demonstrate the insights DOA provides toward improving known approaches to these problems. Using a careful comparative analysis of a range of MAPs and solution approaches via the DOA framework, we identify a single critical differentiating factor: how accurately an agent can discern other agent.s states. To demonstrate how variance in this factor influences solution tractability and complexity we show its effect on the convergence time and quality of Particle Swarm Optimization approach to a generalized MAP

Perspectives on computing and organizational rationalization

It is widely believed that the introduction of computing into organizations contributes to more rationalized decisionmaking and policymaking. Until recently, little empirical work has been done on this topic. This paper defines three perspectives, termed the Determinist, Contingency, and Voluntarist viewpoints, which appear in contemporary analyses of the rationalizing effects of computing. Three particular analyses, each of which employs one of these perspectives, are examined and the explanatory power of each viewpoint is compared. We find that the Deterministic position, which holds that computing's contribution to rationalization is determined by technical effectiveness and processing capacity, doesn't fully explain the dynamics of computing and organizational rationalization. The effects of organizational structure, organizational environment, bureaucratic politics and social interaction often undermine the rationalizing potential of computing

Centers, Peripheries, and Popularity: The Emergence of Norms in Simulated Networks of Linguistic Influence

We simulate the dynamics of diffusion and establishment of norms, variants adopted by the majority of agents, in a large social influence network with scale-free small-world properties. Diffusion is modeled as the probabilistic uptake of one of several competing variants by agents of unequal social standing. We find that novel variants diffuse following an S-curve and stabilize as norms when three conditions are simultaneously satisfied: the network comprises both extremely highly connected agents (centers) and very isolated members (peripheries), and agents pay proportionally more attention to better connected, more “popular”, neighbors. These findings shed light on little known dynamic properties of centers and peripheries in large influence networks. They show that centers, structural equivalents of highly influential leaders in empirical studies of social networks, are propagators of linguistic influence, while certain peripheral individuals, or loners, can act either as repositories of old forms or initiators of new variants depending on the current state of the rest of the population

Concept Tree Based Clustering Visualization with Shaded Similarity Matrices

One of the problems 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

AI on the WWW: Supply and Demand Agents

Agent-based systems simplify user access to the many global virtual enterprises now arriving on the World Wide Web. After discussing the role supply and demand agents play in these developments, this article summarizes useful AI information resources they make available.published or submitted for publicationis peer reviewe

Metadata Quality for Federated Collections

This paper presents early results from our empirical studies of metadata quality in large corpuses of metadata harvested under Open Archives Initiative (OAI) protocols. Along with some discussion of why and how metadata quality is important, an approach to conceptualizing, measuring, and assessing metadata quality is presented. The approach given in this paper is based on a more general model of information quality (IQ) for many kinds of information beyond just metadata. A key feature of the general model is its ability to condition quality assessments by context of information use, such as the types of activities that use the information, and the typified norms and values of relevant information-using communities. The paper presents a number of statistical characterizations of analyzed samples of metadata from a large corpus built as part of the Institute of Museum and Library Services Digital Collections and Contents (IMLS DCC) project containing OAI-harvested metadata and links these statistical assessments to the quality measures, and interprets them. Finally the paper discusses several approaches to quality improvement for metadata based on the study findings.IMLS National Leadership Grant LG-02-02-0281published or submitted for publicationis peer reviewe