Walamitien H. Oyenan

A capabilities-based model for adaptive organizations

Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environments make multiagent systems susceptible to individual failures that can significantly reduce its ability to accomplish its overall goal. The problem is that multiagent systems are typically designed to work within a limited set of configurations. Even when the system possesses the resources and computational power to accomplish its goal, it may be constrained by its own structure and knowledge of its member’s capabilities. To overcome these problems, we are developing a framework that allows the system to design its own organization at runtime. This paper presents a key component of that framework, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems. This model defines the requisite knowledge of a system’s organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent’s capabilities.

O-MaSE: a customizable approach to developing multiagent development processes

This paper describes the Organization-based MultiagentSystem Engineering (O-MaSE) Process Framework, which helps processengineers define custom multiagent systems development processes.O-MaSE builds off the MaSE methodology and is adapted from theOPEN Process Framework (OPF). OPF implements a Method Engineeringapproach to process construction. The goal of O-MaSE is to allowdesigners to create customized agent-oriented software development processes.O-MaSE consists of three basic structures: (1) a metamodel, (2)a set of methods fragments, and (3) a set of guidelines. The O-MaSE metamodel defines the key concepts needed to design and implementmultiagent systems. The method fragments are operations or tasks thatare executed to produce a set of work products, which may include models,documents, or code. The guidelines define how the method fragmentsare related to one another. The paper also shows two O-MaSE processexamples.

Design and Evaluation of a Multiagent Autonomic Information System

The goal of an autonomic system is to self-manage itself and adjust its actions in the face of environmental changes. In this paper, we adopt a multiagent approach to developing an autonomic information system. The aim of this autonomic information system (AIS) is to provide an information system that can adjust its processing algorithms and/or information sources to provide required information at various levels of efficiency and effectiveness. Our approach to developing autonomic multiagent systems is based on the organization model for adaptive computational systems. We describe the design of one particular autonomic system, the AIS, and illustrate how this system fulfills certain desired autonomic properties. We also evaluate the performance of our autonomic system by comparing it to a non- autonomic system.

Towards a systematic approach for designing autonomic systems

An autonomic system is a system capable of managing itself and adjusting its actions in the face of environmental changes. Autonomic systems are currently developed using ad-hoc approaches, which do not promote repeatable successes. In this paper, we propose a systematic approach for designing autonomic systems. Our approach adopts a multiagent perspective based on the Organization Model for Adaptive Computational Systems, which defines the knowledge required for the system to be able to self-organize. Furthermore, a customized development process based on the Organization-based Multiagent Systems Engineering framework supports our approach. To illustrate the process, we describe the design of one autonomic system, the Autonomic Information System, and exemplify how this system fulfills desired autonomic properties. We also evaluate the performance of our autonomic system by comparing it to a non-autonomic system. This work was supported by grants from the US National Science Foundation (0347545) and the US Air Force Office of Scientific Research (FA9550-06-1-0058).

An Organizational Design for Adaptive Sensor Networks

As wireless sensor network applications grow in complexity, ad-hoc techniques are no longer adequate. Thus, it is crucial that these systems be adaptive and autonomous to remain functional in the face of unreliable communications, dead nodes, and other unexpected failures. We propose to manage sensor networks based on a rigorous multiagent organizational design, which separates application logic from low-level sensor implementation details. The organizational design allows designers to specify high-level goals that the systems will try to achieve based on sensor capabilities.

Exploiting reusable organizations to reduce complexity in multiagent system design

Organization-based Multiagent Systems are a promising way to develop complex multiagent systems. However, it is still difficult to create large multiagent organizations from scratch. Multiagent organizations created using current AOSE methodologies tend to produce ad-hoc designs that work well for small applications but are not easily reused. In this paper, we provide a conceptual framework for designing reusable multiagent organizations. It allows us to simplify multiagent organization designs and facilitate their reuse. We formalize the concepts required to design reusable organization-based multiagent services and show how we can compose those services to create larger, more complex multiagent systems. We demonstrate the validity of our approach by designing an application from the cooperative robotics field.