Managing Agent Interactions with Context-Driven Dynamic Organizations

Abstract

Organizations are at the heart of multi-agent systems. To deal with the ongoing dynamics and changes in the system, organizations have to adapt. Typically, agents are responsible to deal with the complexity of organization dynamics. In this paper, we present an approach for context-driven dynamic organizations in which the agent environment takes the burden of managing organization dynamics. Driven by the context, the agent environment manages the evolution of organizations and actively advertises roles to the agents, supporting the necessary collaborations between agents needed in the current context. We introduce a conceptual model for context-driven dynamic organizations and present a software architecture that supports the model in a distributed setting. The proposed approach separates the management of dynamic evolution of organizations from the actual functionality provided by the agents playing roles in the organizations. Separating these concerns makes it easier to understand, design, and manage organizations in multi-agent systems. We show how we have applied context-driven dynamic organizations in a concrete case of monitoring traffic jams. In this case, camera agents associated with traffic monitoring cameras collaborate in organizations. Depending on the context, camera agents play different roles, with responsibilities ranging from simple measurement to data aggregation. When a traffic jam covers the viewing range of multiple cameras, organizations are dynamically merged, assuring cameras detecting the same traffic jam can collaborate. Vice versa, when a traffic jam dissolves, the organization is dynamically split up. Test results indicate that contextbased dynamic organizations is a promising approach to support decentralized traffic monitoring.

Modeling Dimensions of Self-Adaptive Software Systems

“…The evaluation of the proposed classification was conducted through applying it to several previously developed self-adaptive software systems. The case studies represent different classes of application domains: (1) Traffic Jam Monitoring Systems [9], (2) Embedded Mobile Systems [16,17,19], and (3) High Performance Computing and Sensor Networks [1]. The feedback from the case studies improved the classification in several iterations.…”

OPPORTUNITY: Towards opportunistic activity and context recognition systems

“…1 left). A number of approaches exist for coordinated emergence of sensing networks [2,15,3,10] and autonomous composition [14,24] and evolution [21,5,20] of services. Autonomous self-organization according to the sensing mission results in a goal-oriented sensing ensemble providing the relevant information to the mobile system from wearable or ambient sensors.…”

Hierarchical Organizations and a Supporting Software Architecture for Floating Car Data

“…The host infrastructure offers access to the operating system and other low level services. As in [6], the coordination infrastructure is responsible for the management of organization dynamics and offers organizations and roles to the agent. The abstractions of organization and role allow to separate the management of organization dynamics from the actual functionality provided by the agents playing roles.…”

“…The organization abstractions presented in this paper build upon earlier work [6]. Agents are grouped in organizations in which they play roles.…”

“…This separation reduces the complexity of the agents, making it easier to understand, design, and manage organizations in multi-agent systems. In contrast with the work in [6], this paper uses a dynamic mobile platform (moving vehicles) instead of static road side cameras. This is reflected in a more lightweight software architecture, and an adapted organization model with support for hierarchical organizations.…”