Rainer Herrler

SeSAm: implementation of agent-based simulation using visual programming

In this paper, we present the most important features of SeSAm, a modeling and simulation platform for multi-agent simulations. Based on a declarative, explicit model representation and visual programming, it allows implementing models on specification level. Optimizing compilation allows efficient simulation of the explicit model representation. It was successfully applied in different areas, like biology, traffic or logistics simulation.

About the role of the environment in multi-agent simulations

Multi-agent Simulation can be seen as simulated multi-agent systems situated in a simulated environment. Thus, in simulations the modelled environment should always be a first order object that is as carefully developed as the agents themselves. This is especially true for evolutionary simulation and simulation of adaptive multi-agent systems, as the agents environment guides the selection and adaptation process. Also, for the simulation of realistic agent behavior complex and valid environmental models have to be tackled. Therefore, a modelling and simulation system should provide appropriate means for representing the environmental status, including spatial representations, and dynamics. On the other side, simulation infrastructure should be as simple as possible, as a modeler with domain expertise is usually no computer scientist. He might neither be trained in dealing with data structures and efficient algorithms, nor in traditional programming. After going into the details of simulated environments for multi-agent simulations, this paper shows how environments with different characteristics can be represented in a particular modelling and simulation system, named SeSAm, without asking too much from its users.

From Simulated to Real Environments: How to Use SeSAm for Software Development

In this paper we want to show the possibilities to use agent-based modeling and simulation for software development. Therefore we present the integrated environment SeSam and recent extensions, that allow creating simulated environments for agent based software as well as actually developing and deploying software agents.

OntHoS — an Ontology for Hospital Scenarios

The goal of this paper is to present an ontology for hospital scenarios (called OntHoS). It was developed by a couple of research projects dealing with the application of agent systems in hospital scenarios. The aim of OntHoS is to establish a basis for a description of these scenarios and to facilitate their inter-operability. Therefore, we classify types of ontologies and approaches to ontology design, we focus on constructing a domain ontology and discuss the proceeding as well as the encountered problems. Finally, an overview about the future work is given, focussing especially on opening the scope to task ontologies for special project-specific purposes

UML for Behavior-Oriented Multi-agent Simulations

Developing multi-agent simulations seems to be rather straight forward, as active entities in the original correspond to active agents in the model. Thus plausible behaviors can be produced rather easily. However, for real world applications they must satisfy some requirements concerning verification, validation and reproducibility. Using a standard framework for designing a multi-agent model one can gain further advantages like fast learnability, wide understandability and possible transfer.In this paper we show how UML can be used to specify behavior-oriented multi-agent models. Therefore we focus on activity graphs and the representation of different forms of interactions in these graphs.

Coupling GIS and Multi-agent Simulation – Towards Infrastructure for Realistic Simulation

Based on the agent paradigm highly realistic simulation models can be formulated. However, the richness of the environment and its spatial representation are a basic ingredient for such models that cannot be easily modelled with the simple spatial representations usually used in Multi-Agent Simulation. An interesting solution is coupling a Multi-Agent Simulation tool and a geographical information system. Based on such a coupling existing map data can be used for realistic Multi-Agent Simulations.

adapt@Agent.Hospital: Agent-Based Organization & Management of Clinical Processes

There are several continuing challenges within the health care domain. On the one hand, there is a greater need for individualized, patient-oriented processes in diagnostics, therapy, nursing, and administration, and on the other hand, hospitals have extremely distributed decision processes and strong local (individual) autonomy with a high degree of situational dynamics. This research focuses on developing an information system that can substantially increase the efficiency of hospital process management. We present a system for agent-based simulation and support for clinical processes. In particular, we describe the architecture of the system and its functionalities and the integration of existing Foundation for Intelligent Physical Agents (FIPA), DICOM, and HL7 standards. We discuss an example scenario for clinical trials to illustrate how the system supports distributed clinical process management. This system interacts with other multiagent systems within the Agent.Hospital framework and hospital information systems (HISs) in the eHealth Lab. This research is part of the German Priority Research Program (SPP) 1083, Intelligent Agents and Their Application in Business Scenarios.

Agent.Hospital — a Framework for Clinical Applications in Agentcities

Agent.Hospital is an open agent-based (software) framework for distributed applications in the healthcare domain. Previous appropriation of the Agent.Hospital development is the application and examination of agent technology in a realistic business scenarios and the identification of further research needs. This paper introduces the framework developed by the German Priority Research Program 1083. We describe the initial system concept, currently implemented or specified functionalities and the integration of FIPA standardization activities. The example scenario, “clinical trials”, illustrates how Agent.Hospital supports distributed clinical processes as well as further research of agent technology

Agent.Hospital — Agentenbasiertes offenes Framework für klinische Anwendungen

Agent.Hospital ist ein offenes, agentenbasiertes (Modell- und Software-)Framework fur stark verteilte Anwendungen im Gesundheitswesen. Hauptanliegen der Entwicklung von Agent.Hospital sind die Erprobung der Agententechnologie in einem realitatsnahen Anwendungsszenario und die Bearbeitung des dabei identifizierten weiterfuhrenden Forschungsbedarfs. Der vorliegende Beitrag stellt das im Rahmen des DFG-Schwerpunktprogramms 1083 entwickelte Framework erstmals einer breiteren Offentlichkeit vor. Nach Einfuhrung in Systemkonzeption und derzeit verfugbare Funktionalitaten wird die Verknupfung der Arbeiten mit den Standardisierungsaktivitaten der FIPA sowie dem Forschungsnetzwerk Agentcities vorgestellt. Anhand des Beispielszenarios „Klinische Studien “ wird schlieβlich gezeigt, auf welche Weise Agent.Hospital stark verteilte klinische Prozesse ebenso wie die weitere Erforschung der Agententechnologie zu unterstutzen vermag.

Adaptivity and Scheduling

The structures in health care are currently changing. Clinical management and physicians have the obligation to both ensure quality of care and to work more cost effectively. The optimization of the system respecting these contrary goals is a big challenge. New information technology and computer applications like adaptive agent based assistance agents may be one way to optimize the system. Additionally organizational changes regarding resources or processes may also enhance the system. In many cases the effects of optimization ideas are difficult to foresee. This chapter describes the possibilities of multiagent simulation for experimentation and optimization of adaptive scheduling in hospitals. It presents a specialized agent based construction kit for hospital simulation and describes the results of realized example scenarios.