Crowdsourcing and Simulation with Mobile Agents and the JavaScript Agent Machine
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Using Mobile Multi-Agent Systems, this book tackles the problem of unified and distributed computing in robust heterogeneous contexts, spanning from Internet Clouds to Sensor Networks. The operational gap between low-resource data processing units, such as single microchips embedded in materials, mobile devices, and generic computers including servers, should be closed by a unified agent behaviour model, agent processing platform architecture, and programming framework, supporting real-world deployment as well as simulation. Major features include robustness, scalability, self-organization, re...
Using Mobile Multi-Agent Systems, this book tackles the problem of unified and distributed computing in robust heterogeneous contexts, spanning from Internet Clouds to Sensor Networks. The operational gap between low-resource data processing units, such as single microchips embedded in materials, mobile devices, and generic computers including servers, should be closed by a unified agent behaviour model, agent processing platform architecture, and programming framework, supporting real-world deployment as well as simulation. Major features include robustness, scalability, self-organization, reconfiguration, adaptivity, and learning. This book provides a straightforward introduction to creating JavaScript agents using the JavaScript Agent Machine (JAM) requiring only a few lines of code. In a short amount of time, even beginners may develop robust multi-agent systems. This book has a strong practical focus on mobile crowdsensing (MCS) using, e.g., surveys. MCS is a useful tool for data mining because it views people as sensors. In addition, agent-based simulation is addressed, finally coupled to real worlds using MCS and digital twin concepts. Crowdsourcing and crowdsensing are elaborated in detail after a brief introduction to agent-based notions. If you are solely interested in the agent platform and its programming, you can skip this chapter. The platform is described in connection to the agent interaction and behaviour model. AgentsJS, a subset of JavaScript that is described in depth in a separate chapter, is used to program agents. Pre-compiled libraries and programs are added to the core programming interface, including a simulator. The simulator uses JAM and has the ability to connect to other JAM nodes, allowing for augmented simulation that incorporates the real world. Finally, an extended example chapter shows various aspects of agent programming with AgentJS and JAM. The software is freely avaiable from https://github.com/bslab/jam. A lot of exercises provide a practical elaboration of agent-based methods, crowdsourcing, and mobile or Web-based surveys. This book is based on recent scientific work as well as on different lectures I have held at the University of Bremen and the University of Koblenz-Landau. The lectures address the design and deployment of multi-agent systems as well as mobile crowdsensing for bachelor and master students in computer science, production engineering, and social sciences.
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