H. Van Dyke Parunak

Go to the ant: Engineering principles from natural multi-agent systems

Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multi-agent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents.Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multi-agent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents. Copyright Kluwer Academic Publishers 1997

Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide

In many domains, agent-based system modeling competes with equation-based approaches that identify system variables and evaluate or integrate sets of equations relating these variables. The distinction has been of great interest in a project that applies agent-based modeling to industrial supply networks, since virtually all computer-based modeling of such networks up to this point has used system dynamics, an approach based on ordinary differential equations (ODE’s). This paper summarizes the domain of supply networks and illustrates how they can be modeled both with agents and with equations. It summarizes the similarities and differences of these two classes of models, and develops criteria for selecting one or the other approach.

Representing Agent Interaction Protocols in UML

Gaining wide acceptance for the use of agents in industry requires both relating it to the nearest antecedent technology (objectoriented software development) and using artifacts to support the development environment throughout the full system lifecycle. We address both of these requirements using AUML, the Agent UML (Unified Modeling Language)--a set of UML idioms and extensions. This paper illustrates the approach by presenting a three-layer AUML representation for agent interaction protocols: templates and packages to represent the protocol as a whole; sequence and collaboration diagrams to capture inter-agent dynamics; and activity diagrams and state charts to capture both intra-agent and inter-agent dynamics.

Environments for multiagent systems state-of-the-art and research challenges

It is generally accepted that the environment is an essential compound of multiagent systems (MASs). Yet the environment is typically assigned limited responsibilities, or even neglected entirely, overlooking a rich potential for the paradigm of MASs. Opportunities that environments offer, have mostly been researched in the domain of situated MASs. However, the complex principles behind the concepts and responsibilities of the environment and the interplay between agents and environment are not yet fully clarified. In this paper, we first give an overview of the state-of-the-art on environments in MASs. The survey discusses relevant research tracks on environments that have been explored so far. Each track is illustrated with a number of representative contributions by the research community. Based on this study and the results of our own research, we identify a set of core concerns for environments that can be divided in two classes: concerns related to the structure of the environment, and concerns related to the activity in the environment. To conclude, we list a number of research challenges that, in our opinion, are important for further research on environments for MAS.

Environments for Multi-Agent Systems III

Models, Architecture, and Design.- A Reference Architecture for Situated Multiagent Systems.- A Unified Model for Physical and Social Environments.- Exploiting the Environment for Coordinating Agent Intentions.- CArtA gO: A Framework for Prototyping Artifact-Based Environments in MAS.- Mediated Interaction and Stigmery.- Environment as Active Support of Interaction.- Environmental Support for Tag Interactions.- Cognitive Stigmergy: Towards a Framework Based on Agents and Artifacts.- Trace Signals: The Meanings of Stigmergy.- Regulation Function of the Environment in Agent-Based Simulation.- Governing Environment.- Establishing Global Properties of Multi-Agent Systems Via Local Laws.- E4MAS Through Electronic Institutions.- Spatially Distributed Normative Infrastructure.- Enhancing the Environment with a Law-Governed Service for Monitoring and Enforcing Behavior in Open Multi-Agent Systems.- Applications.- Urban Traffic Control with Co-Fields.- Designing Self-organising MAS Environments: The Collective Sort Case.

Representing Social Structures in UML

From a software engineering perspective, agent systems are a specialization of object-oriented (OO) systems, in which individual objects have their own threads of control and their own goals or sense of purpose. Engineering such systems is most naturally approached as an extension of object-oriented systems engineering. In particular, the Unified Modeling Language (UML) can be naturally extended to support the distinctive requirements of multi-agent systems. One such requirement results from the increasing emphasis on the correspondence between multi-agent systems and social systems. Sociological analogies are proving fruitful models for agent-oriented constructions, while sociologists increasingly use agents as a modeling tool for studying social systems. We combine several existing organizational models for agents, including AALAADIN, dependency theory, interaction protocols, and holonics, in a general theoretical framework, and show how UML can be applied and extended to capture constructions in that framework.

Entropy and self-organization in multi-agent systems

Emergent self-organization in multi-agent systems appears to contradict the second law of thermodynamics. This paradox has been explained in terms of a coupling between the macro level that hosts self-organization (and an apparent reduction in entropy), and the micro level (where random processes greatly increase entropy). Metaphorically, the micro level serves as an entropy “sink”, permitting overall system entropy to increase while sequestering this increase from the interactions where selforganization is desired. We make this metaphor precise by constructing a simple example of pheromone-based coordination, defining a way to measure the Shannon entropy at the macro (agent) and micro (pheromone) levels, and exhibiting an entropybased view of the coordination.

DIGITAL PHEROMONES FOR AUTONOMOUS COORDINATION OF SWARMING UAV'S

Modern UAV’s reduce the threat to human operators, but do not decrease the manpower requirements. Each aircraft requires a flight crew of one to three, so deploying large numbers of UAV’s requires committing and coordinating many human warfighters. Insects perform impressive feats of coordination without direct inter-agent coordination, by sensing and depositing pheromones (chemical scent markers) in the environment [14]. We have developed a novel technology for coordinating the movements of multiple UAV’s based on a computational analog of pheromone dynamics. The control logic is simple enough that it can be executed autonomously by a UAV, enabling a single human to monitor an entire swarm of UAV’s. This paper describes the technology, its application to UAV coordination, and the results we have obtained.

Research directions for service-oriented multiagent systems

Todays service-oriented systems realize many ideas from the research conducted a decade or so ago in multiagent systems. Because these two fields are so deeply connected, further advances in multiagent systems could feed into tomorrows successful service-oriented computing approaches. This article describes a 15-year roadmap for service-oriented multiagent system research.

Performance of digital pheromones for swarming vehicle control

The use of digital pheromones for controlling and coordinating swarms of unmanned vehicles is studied under various conditions to determine their effectiveness in multiple military scenarios. The study demonstrates the effectiveness of these pheromone algorithms for surveillance, target acquisition, and tracking. The algorithms were demonstrated on hardware platforms and the results from the demonstration are reported