Multi-agent Systems

Today sensor data processing and information mining become more and more complex concerning the amount of sensor data to be processed, the data dimension, the data quality, and the relationship between derived information and input data.

This is the case especially in large-scale sensing and measuring processes embedded in Cloud environments. Measuring uncertainties, calibration errors, and unreliability of sensors have a significant impact on the derivation quality of suitable information. In the technical and industrial context the raising complexity and distribution of data processing is a special issue.

Commonly, information is derived from raw input data by using some kind of mathematical model and functions, but often being incomplete or unknown. If reasoning of statements is primarily desired, Machine Learning can be an alternative.

Traditionally, sensor data is acquired and delivered to and processed by a central processing unit. The deployment of distributed Machine Learning using mobile Agents forming self-organizing and self-adaptive systems (self-X) pose the benefit for the enhancement of the sensor and data processing in technical and industrial systems.

This also addresses the quality of the computed statements, e.g., an accurate prediction of run-time parameters like mechanical loads or health conditions, the efficiency, and the reliability in the presence of partial system failures.

Agent-based methods are established for modelling and studying of complex dynamic systems and for implementing distributed intelligent data processing systems. Therefore, agent-based methods can be divided into three main classes:

  1. Agent-based Modelling - Modelling of complex dynamic systems by using the agent behaviour and interaction model
  2. Agent-based Computing - Distributed and parallel computing using mobile agents related to mobile software processes
  3. Agent-based Simulation - Simulation of agents or by using agents

abm Union of Agent-based Computing (ABC), Agent-based Modelling (ABM), Agent-based Simulation (ABS), and Agent-based Modelling and Simulation (ABMS)


Agent-based simulation is suitable for modelling complex social systems with respect to interaction between individual entities, manipulation of the world, spatial movement, and emergence effects of groups of entities. The main advantage is the bottom-up modelling approach composing large-scale complex systems by simple entity models. The main disadvantage of ABM is the (over-) simplified entity behaviour and simplification of the world the entities acting in.

Commonly, simulation bases on synthetic data or data retrieved by field studies. Many simulations and models lacking of diversity existing in real world. Commonly, sensor and model data (parameters) used in simulations (virtual world) is retrieved from experiments or field studies (real world). But there is neither a feedback from the virtual to the real world nor an interaction of the real world with the virtual world.

Mobile agents as mobile computational software processes can close this gap and provide sensor and information mining in distributed and mobile environments, including crowd sensing.

Crowd Sensing

Mobile devices like smart phones are valuable sources for social data, either by participatory crowd sensing with explicit participation of users providing first class data (e.g.,, performing surveys or polls) or implicitly by opportunistic crowd sensing collecting secondary class data, i.e., traces of device sensor data delivering, e.g., actual position, ambient conditions, network connectivity, digital media interaction, and so on.

Crowd sensing and Social Data Mining as a data source contribute more and more to investigations of digital traces in large-scale machine-human environments characterised by complex interactions and causalities between perception and action (decision making).

Distributed Computing

Mobile multi-agent systems are used for robust and adaptive information processing in highly heterogeneous environments. A central research topic is interaction and the implementation of self-* properties: self-organization, self-adaptivity, self-configuration. The core idea is the composition of complex systems consisting of simple loosely coupled unit cells. Efficient and scalable agent platforms (including hardware implementations) with ML modules are a key technology.

Pervasive & Ubiquitous Computing, Internet of Things, Structure Monitoring

Crowd and things sensing with data mining of inherent sensor data through scalable and efficient information processing. Special focus on distribution, algorithms, communication, and platforms for limited embedded / mobile computing systems (in terms of computing power, storage capacity, energy) represent an important field of research that is addressed and solved with the agent paradigma.

Further Readings

S. Bosse, D. Lehmhus, Material-integrated cluster computing in self-adaptive robotic materials using mobile multi-agent systems, Cluster Computing, doi 10.1007/s10586-018-02894-x, Volume 22, Number 3, pp. 1017-1037, 2019 ISSN 1386-7857
Publisher Paper Online Paper PDF
S. Bosse, U. Engel, Real-time Human-in-the-loop Simulation with Mobile Agents, Chat Bots, and Crowd Sensing for Smart Cities, Sensors (MDPI), 2019, doi: 10.3390/s19204356
Publisher Paper Online Paper PDF
S. Bosse, U. Engel, Combining Crowd Sensing and Social Data Mining with Agent-based Simulation using Mobile Agents towards Augmented Virtuality, Proc. of the Social Simulation Conference, 24-27.9.2019, Mainz, Germany
Paper PDF Presentation HTML The Agent Laboratory: Demonstration
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems: Models, Platforms, and Technological Aspects, ISBN 9783746752228 (Hardcover), ISBN 9783746759470 (Softcover), epubli, 2018
Sample PDF
S. Bosse, Smart Micro-scale Energy Management and Energy Distribution in Decentralized Self-Powered Networks Using Multi-Agent Systems, FedCSIS Conference, 6th International Workshop on Smart Energy Networks & Multi-Agent Systems, 9-12.9.2018, Posznan, Poland, 2018,
Paper PDF
S. Bosse, Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring, International Journal of Distributed Systems and Technologies (IJDST), (2017), IGI-Global, Vol. 8, Issue 4, DOI: 10.4018/IJDST.2017100103
Paper PDF Publisher
S. Bosse, E. Pournaras, An Ubiquitous Multi-Agent Mobile Platform for Distributed Crowd Sensing and Social Mining, FiCloud 2017: The 5th International Conference on Future Internet of Things and Cloud, Aug 21, 2017 - Aug 23, 2017, Prague, Czech Republic
Paper PDF Publisher
Stefan Bosse, Distributed Agent-based Computing in Material-Embedded Sensor Network Systems with the Agent-on-Chip Architecture, IEEE Sensors Journal, Special Issue MIS, 2014, DOI: 10.1109/JSEN.2014.2301938.
Paper PDF Paper Online