Dynamic Contact Network Simulation Model Based on Multi-Agent Systems

Welcome to the Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS 2014)! Evolutionary computation (EC) and multi-agent systems and simulation (MASS) both involve populations of agents. EC is a learning technique by which populations of individual agents adapt according to the selection pressures exerted by an environment; MASS seeks to understand how to coordinate the actions of a population of (possibly selfish) autonomous agents that share an environment so that some outcome is achieved. Both EC and MASS have top-down and bottom up features. For example, some aspects of multi-agent system engineering (e.g., mechanism design) are concerned with how top-down structure can constrain or influence individual decisions. Similarly, most work in EC is concerned with how to engineer selective pressures to drive the evolution of individual behavior towards some desired goal. Multi-agent simulation (also called agent-based modeling) addresses the bottom-up issue of how collective behavior emerges from individual action. Likewise, the study of evolutionary dynamics within EC (for example in coevolution) often considers how population-level phenomena emerge from individual-level interactions. Thus, at a high level, we may view EC and MASS as examining and utilizing analogous processes. It is therefore natural to consider how knowledge gained within EC may be relevant to MASS, and vice versa; indeed, applications and techniques from one field have often made use of technologies and algorithms from the other field. Studying EC and MASS in combination is warranted and has the potential to contribute to both fields. The ECoMASS Workshop at GECCO has a successful history as a forum for exploring precisely this intersection, and we are looking forward to another year of stimulating discussion, bringing together experts as well as novices in both areas, to engage in dialogue about their work. This year's participants bring a variety of topics for discussion, including migratory flows, the beating of the heart, embedded systems, pelotons and cyclists, and flocking behavior. This year, we will also be adding a research slam. We will be inviting presenters who are presenting in the general conference to give us a quick presentation about their work. We hope that this will further encourage additional lively discussion about a wide range of topics. We also encourage presenters to demonstrate their software live during a breakout session, enabling additional opportunities for one-on-one dialogue and discussion of actual running systems. Through these additions to the workshop, we hope to continue to innovate, and develop the conversations around the interesting intersection of evolutionary computation and multi-agent systems and simulations!

Multi-Agent System (MAS) is a suitable programming paradigm for simulating and modeling health care systems and applications, where resources, data, control and services are widely distributed. We have developed a multi-agent software prototype to simulate the activities and roles inside a health care system. The prototype is developed using a framework called Role-based Agent Development Environment (RADE). In this chapter, the authors present an integrated approach for modeling, designing and implementing a multi-agent health care simulation system using RADE. They describe the definition of role classes and agent classes, as well as the automatic agent generation process. The authors illustrate the coordination problem and present a rule-based coordination approach. In the end, they present a runtime scenario of this health care simulation system, which demonstrates that dynamic task allocation can be achieved through the creation of role instances and the mapping from role instances to agents. This scenario also explains how agents coordinate their activities given their local constraints and interdependence among distributed tasks.

Urban public space is the main place for human outdoor activities. Simulating human behavior through the Multi-Agent System (MAS) is one of the most important methods for studying public space. However, most of the research on this topic is concerned with the way people behave and the connection to spatial layouts. It ignores the fact that in outdoor spaces, microclimate factors tend to have a more important impact on human behavior. In this study, microclimate factors were narrowed down into two main factors: outdoor thermal perception and sunshine perception. Both of these factors are integrated into the traditional MAS, together with visual perception as the influence factors of agent action. The new MAS was developed in Processing language and can dynamically and visually show agents activities in the space. Taking Gulou Square in Nanjing, China, as a case, the simulations were carried out with three typical meteorological days of spring equinox, summer solstice, and winter solstice. Through comparing the simulation results at different times, we found that the new MAS exhibits a significant impact of microclimate on human behavior. The new MAS can be used reasonably and effectively for the design and evaluation of urban public space.

The U.S. military uses modeling and simulation as a tool to help meet its warfighting needs. A key element within military simulations is the ability to accurately represent human behavior. This is especially true in a simulation's ability to emulate realistic military decisions. However, current decision models fail to provide the variability and flexibility that human decision makers exhibit. Further, most decision models are focused on tactical decisions and ignore the decision process of senior military commanders at the operational level of warfare. In an effort to develop a better decision model that would mimic the decision process of a senior military commander, this research sought to identify an underlying cognitive process and computational techniques that could adequately implement it. Recognition-Primed Decision making (RPD) was identified as one such model that characterized this process. Multiagent system simulation was identified as a computational system that could mimic the cognitive process identified by RPD. The result was a model of RPD called RPDAgent. Using an operational military decision scenario, decisions produced by RPDAgent were compared against decisions made by military officers. It was found that RPDAgent produced decisions that were equivalent to its human counterparts. RPDAgent's decisions were not optimum decisions, but decisions that reflected the variability inherent in those made by humans in an operational military environment.

International audience

David Judson Sencer

At the Institute of Computer Science at Silesian University in Opava is developed multi-agent system (MAS) in the form of a swarm of unmanned aerial vehicles (UAV). For the simulation of the multi-agent system was proposed double-layer software environment. As the presentation layer was selected the simulation tool Webots that enables detailed parameter settings of an individual agent (flying device model). For the function of multi-agent layer was selected simulation tool JADE used for testing large multi-agent systems. This document describes the use of the simulation environment and tools in the area of agents communication in developed UAV multi-agent system.

In May of 2022, several cases of mpox were identified in multiple nonendemic countries and on July 23, 2022 the World Health Organization declared mpox a Public Health Emergency of International Concern. During the first six months of the outbreak there was an urgent need to have up-to-date synthesized evidence on mpox to inform public health decision-making. At this point, evidence is changing too quickly for traditional evidence synthesis methods, as systematic reviews were out-of-date before publication. This paper describes the framework developed to manage and maintain a living evidence profile (LEP) to systematically identify, classify and synthesize evidence on a broad range of mpox topics at a rapid pace as the outbreak unfolded. The LEP framework was based on principles of evidence synthesis, risk assessment, priority epidemiological parameters for infectious disease modeling and consultation with experts. The framework consisted of a systematic search conducted twice weekly; study selection; categorization into pre-determined foci and data extraction; integration and synthesis of evidence; internal peer-review and dissemination to stakeholders. Between April 14 and December 15, 2022, 2287 citations were identified, 687 were primary research studies or surveillance reports on the 2022 mpox outbreak and 496 were included in the final LEP. Each study was mapped to one of 32 foci and evidence was narratively synthesized. From June to December 2022, 23 LEPs were produced (approximately weekly) along with a searchable database of extracted data of the mpox literature. They were disseminated globally to public health researchers and decision-makers to inform public health response efforts. The LEP framework is applicable to other public health emergencies when a rapid synthesis cycle is required because the evidence is evolving quickly. This efficient methodology for creating up-to-date summaries of the current evidence during the first few months of an outbreak or emergency supports public health decision-making and response activities.

In Multi-Agent System (MAS), developers concentrate on creating design models and evolving them, from higher level models to lower level models, in several steps. Considerable part of MAS implementations is automatically produced from the design models. If a design model contains faults, they are passed to the generated implementations. Practical model validation techniques are required to discover and delete faults in abstract design models. In this paper, we introduce a formal approach for MAS design testing. It specifies a testing process that complements Multi-agent Systems Engineering (MaSE) methodology and strengthens the mutual relationship between UML and MAS. Besides, it defines a structured and comprehensive testing process for engineering software agents at the design level by providing a systematic way of converting the MAS design models to UML design diagram. Then a Petri Net (PN) diagram is generated from the UML models to simulate the behavior of the MAS system. Finally, because Petri Nets (PNs) are formal models, their analysis techniques can be applied to automatic MAS behavioral testing.

The article proposes a multi-agent information system (MAIS) approach to model the order fulfilment process (OFP) in supply chain networks (SCNs). An order fulfilment process starts with receiving orders from customers and ends with delivery of the finished goods. As manufacturing practice is shifting toward the outsourcing paradigm, the OFP is more likely to be executed throughout SCNs. It becomes imperative to integrate the OFP into SCNs to improve the OFP. The proposed multi-agent information system (MAIS) approach is used for modeling the OFP in SCNs, and evaluating OFP performance by applying the proposed strategies. The objective of reengineering the OFP is to achieve agility of the process in terms of efficiency, flexibility, robustness and adaptability. A multi-agent simulation platform, called Swarm, is enhanced and applied for modeling the MAIS, and experiments are conducted to simulate the OFP in SCNs in a multi-agent environment. Based an the Swarm simulation platform, we model the OFP in SCNs, simulate the OFP, and then evaluate the potential OFP improvement strategies to identify useful strategies for improving the OFP. The results shed light on identifying the main effects of various strategies on OFP performance. The insights of utilizing various strategies in different SCNs help redesign the OFP in SCNs.

With the emerging applications of multi-agent systems, there is always a need for simulation to verify the results before actual implementation. Multi-agent simulation provides a test bed for several soft-computing algorithms like fuzzy logic, learning automata, evolutionary algorithms, etc. In this paper we discuss the fusion of these soft-computing methodologies and existing tools for discrete event simulation (DEVS) for multi-agent simulation. We propose a methodology for combining the agent-based architecture, discrete event system and soft-computing methods in the simulation of multi-agent robotics and network security system. We also define a framework called Virtual Laboratory (V-Lab/spl reg/) for multi-agent simulation using intelligent tools.

Issue It is well accepted that public health decision makers should use the best available research evidence in their decision-making process. However, research evidence alone is insufficient to inform public health decision making. Description of the problem As new challenges to public health emerge, there can be a paucity of high quality research evidence to inform decisions on new topics. Public health decision makers must combine various sources of evidence with their public health expertise to make evidence-informed decisions. The National Collaborating Centre for Methods and Tools (NCCMT) has developed a model which combines research evidence with other critical sources of evidence that can help guide decision makers in evidence-informed decision making. Results The NCCMT's model for evidence-informed public health combines findings from research evidence with local data and context, community and political preferences and actions and evidence on available resources. The model has been widely used across Canada and worldwide, and has been integrated into many public health organizations' decision-making processes. The model is also used for teaching an evidence-informed public health approach in Masters of Public Health programs around the globe. The model provides a structured approach to integrating evidence from several critical sources into public health decision making. Use of the model helps ensure that important research, contextual and preference information is sought and incorporated. Lessons Next steps for the model include development of a tool to facilitate synthesis of evidence across all four domains. Although Indigenous knowledges are relevant for public health decision making and should be considered as part of a complete assessment the current model does not capture Indigenous knowledges. Key messages Decision making in public health requires integrating the best available evidence, including research findings, local data and context, community and political preferences and available resources. The NCCMT’s model for evidence-informed public health provides a structured approach to integrating evidence from several critical sources into public health decision making.

Environmental impact assessment (EIA) is one of the principal indicators for human health evaluation as well as for further issues in occupational medicine, public health and planning. Certain difficulties to evaluate EIA appear due to, on the one hand, the nature of contaminants and health characteristics, and, on the other, the difficulties found in data processing, such as heterogeneity of the information sources, lack of data quality and need to use numerous data mining methods. The application of multi-agent systems (MAS) to EIA helps operating with distributed decentralized information and generating general decisions and solutions. In order to facilitate the workflow of MAS planning and implementation in decision support systems (DSS) applied to EIA, a generalized multi-agent system technology (MAST) is proposed. This workflow is general enough to enable the related MAS to be used in any application area requiring decision making. We demonstrate that by only changing the domain ontology the MAS is easily oriented towards another problem area. The functional organization and the roles executed in the MAS conform to the logical sequence of the data transformation flow, which include information retrieval and fusion, data pre-processing, data mining and modeling, and simulation and decision making.

In multi-agent problem-solving systems in which agents work asynchronously and in parallel on parts of a problem distributed among them, subproblem communication and integration protocols can have a major impact on problem solving efficiency. Transmitting too soon can necessitate having to take back transmitted partial solutions, wasting communication and processing time. Transmitting too late can complicate integration of partial solutions derived independently. The trade-off between these depends on the nature of the subproblem interdependency among the agents. We present a parameterized distributed constraint satisfaction problem (CSP) generator and a parameterized multi-agent problem-solving simulator: Using these tools, we offer some empirical observations of the tradeoff between strategies for subproblem communication timing. Our goal is to derive domain-independent answers to questions of subproblem sharing in multi-agent systems for problems that can be represented within the CSP paradigm.

Economic development is faced with the challenge of global financial crisis currently, and power economy is affected inevitably. To quantitatively evaluate the impact of financial crisis on power economy, a power economy simulation model of econometric model is proposed in this paper, and a dynamic simulation platform based on the proposed model named dynamic power economy simulation system (DPESS) is developed by multi-agent system. Agents DPESS involved consist of four categories: industry agent, government agent, market agent and resident agent. It provides to the policy makers a useful tool to evaluate the influence of economic uncertainty such as global financial crisis and to evaluate the effect of policy regulation impacting on the total electricity consumption. By simulating three different scenarios on electricity consumption, minor and discrete policy regulation is proposed.