One reason why Distributed AI (DAI) technology has been deployed in relatively few real-size applications is that it lacks a clear and implementable model of cooperative problem solving which specifies how agents should operate and interact in complex, dynamic and unpredictable environments. As a consequence of the experience gained whilst building a number of DAI systems for industrial applications, a new principled model of cooperation has been developed. This model, called Joint Responsibility, has the notion of joint intentions at its core. It specifies pre-conditions which must be attained before collaboration can commence and prescribes how individuals should behave both when joint activity is progressing satisfactorily and also when it runs into difficulty. The theoretical model has been used to guide the implementation of a general-purpose cooperation framework and the qualitative and quantitative benefits of this implementation have been assessed through a series of comparativ...

Coordination, the process by which an agent reasons about its local actions and the (anticipated) actions of others to try and ensure the community acts in a coherent manner, is perhaps the key problem of the discipline of Distributed Artificial Intelligence (DAI). In order to make advances it is important that the theories and principles which guide this central activity are uncovered and analysed in a systematic and rigourous manner. To this end, this paper models agent communities using a distributed goal search formalism, and argues that commitments (pledges to undertake a specific course of action) and conventions (means of monitoring commitments in changing circumstances) are the foundation of coordination in all DAI systems. 1. The Coordination Problem Participation in any social situation should be both simultaneously constraining, in that agents must make a contribution to it, and yet enriching, in that participation provides resources and opportunities which would otherwise ...

To understand the economic value of computers, one must broaden the traditional definition of both the technology and its effects. Case studies and firm-level econometric evidence suggest that: 1) organizational “investments ” have a large influence on the value of IT investments; and 2) the benefits of IT investment are often intangible and disproportionately difficult to measure. Our analysis suggests that the link between IT and increased productivity emerged well before the recent surge in the aggregate productivity statistics and that the current macroeconomic productivity revival may in part reflect the contributions of intangible capital accumulated in the past.

This paper presents a high-level framework for analysing and designing intelligent agents. The framework's key abstraction mechanism is a new computer level called the Social Level. The Social Level sits immediately above the Knowledge Level, as defined by Allen Newell, and is concerned with the inherently social aspects of multiple agent systems. To illustrate the working of this framework, an important new class of agent is identified and then specified. Socially responsible agents retain their local autonomy but still draw from, and provide resources to, the larger community. Through empirical evaluation, it is shown that such agents produce both good system-wide performance and good individual performance. 1. INTRODUCTION The number of multi-agent systems being designed and built is rapidly increasing as software agents gain acceptance as a powerful and useful technology for solving complex problems (Chaib-draa, 1995; Jennings, 1994; PAAM, 1996). As applications become more comple...

Computational and mathematical organization theory is an inter-disciplinary scientific area whose research members focus on developing and testing organizational theory using formal models. The community shares a theoretical view of organizations as collections of processes and intelligent adaptive agents that are task oriented, socially situated, technologically bound, and continuously changing. Behavior within the organization is seen to affect and be affected by the organization’s position in the external environment. The community also shares a methodological orientation toward the use of formal models for developing and testing theory. These models are both computational (e.g., simulation, emulation, expert systems, computer-assisted numerical analysis) and mathematical (e.g., formal logic, matrix algebra, network analysis, discrete and continuous equations). Much of the research in this area falls into four areas: organizational design, organizational learning, organizations and information technology, and organizational evolution and change. Historically, much of the work in this area has been focused on the issue how should organizations be designed. The work in this subarea is cumulative and tied to other subfields within organization theory more generally.

The objective of this paper is to examine the crucial area of co-ordination in multi-agent systems. It does not attempt to provide a comprehensive overview of the co-ordination literature; rather, it highlights the necessity for co-ordination in agent systems and overviews briefly various co-ordination techniques. It critiques these techniques and presents some conclusions and challenges drawn from this literature.

In developing information systems for use in an organization, one often needs to understand the reasons that underlie established work patterns and practices. Because organizational actors depend on each other for goals to be achieved, tasks to be performed, and resources to be furnished, reasons for work patterns can be revealed by examining the dependencies among actors. We present a model which characterizes a work organization in terms of the network of dependencies among organizational actors. Actor dependencies are taken to be intentional -- they expand or restrict an actor's ability to pursue goals. The network of actor dependencies constitutes the intentional structure of the organization. We use examples from business process reengineering to motivate and illustrate the model. KEYWORDS Organization model, organization analysis and design, business process reengineering, workflow, requirements engineering. INTRODUCTION To build effective organizational computing systems, one ...

One step towards a more systematic approach to the design of business processes is to develop models that provide appropriate representations of the knowledge that is needed for understanding and for reasoning about business processes. We present a modelling framework which uses goals, rules, and methods to support the systematic analysis and design of business processes. The framework consists of two main components -- an Actor Dependency model that describes a process organization in terms of intentional dependencies among actors, and an Issue Argumentation model that supports reasoning during process redesign. Formal representation of these models allows computer-based tools to be developed as extensions to, and eventually integrated with, other tools for supporting information systems development. 1 Introduction The effectiveness of an information system is ultimately judged according to how well it meets the needs of the people and the organization that it serves. Although softw...

Motivated by the control theoretic distinction between controllable and uncontrollable events, we distinguish between two types of agents within a multi-agent system: controllable agents, which are directly controlled by the system's designer, and uncontrollable agents, which are not under the designer's direct control. We refer to such systems as partially controlled multi-agent systems, and we investigate how one might influence the behavior of the uncontrolled agents through appropriate design of the controlled agents. In particular, we wish to understand which problems are naturally described in these terms? what methods can be applied to influence the uncontrollable agents? what is their effectiveness ? and whether similar methods work across different domains? Using a game-theoretic framework, this paper studies the design of partially controlled multi-agent systems in two contexts: in one context, the uncontrollable agents are expected utility maximizers, while in the other th...

We present a distributed algorithm for task allocation in multiagent systems for settings in which agents and tasks are geographically dispersed in two-dimensional space. We describe a method that enables agents to determine individually how to move so that they are, as a group, efficiently assigned to tasks. The method comprises two algorithms and is especially useful in environments with very large numbers of agent and task nodes. One algorithm adapts computational geometry techniques to determine adjacency information for the agent nodes given the geographical positions of agents and tasks. This adjacency information is used to determine the visible nodes that are most relevant to an agent's decision making process and to eliminate those that it should not consider. The second algorithm uses local heuristics based solely on an agent's adjacent nodes to determine its course of action. This method yields improved task allocations compared to previous algorithms proposed for similar environments. We also present a modification to the second algorithm that improves performance in environments in which multiple agents are required to complete a single task.