Author name cluster

Jürgen Dunkel

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

EUMAS Conference 2020 Conference Paper

Evaluating Crowdshipping Systems with Agent-Based Simulation

Jeremias Dötterl
Ralf Bruns
Jürgen Dunkel
Sascha Ossowski

Abstract Due to e-commerce growth and urbanization, delivery companies are facing a rising demand for home deliveries, which makes it increasingly challenging to provide parcel delivery that is cheap, sustainable, and on time. This challenge has motivated recent interest in crowdshipping. In crowdshipping systems, private citizens are incentivized to contribute to parcel delivery by making small detours in their daily lives. To advance crowdshipping as a new delivery paradigm, new crowdshipping concepts have to be developed, tested, and evaluated. One way to test and evaluate new crowdshipping concepts is agent-based simulation. In this paper, we present a crowdshipping simulator where the crowd workers are modeled as agents who decide autonomously whether they want to accept a delivery task. The agents’ decisions can be modeled based on shipping plans, which allow to easily implement the most common behavior assumptions found in the crowdshipping literature. We perform simulation experiments for different scenarios, which demonstrate the capabilities of our simulator.

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ECAI Conference 2020 Conference Paper

On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data

Jeremias Dötterl
Ralf Bruns
Jürgen Dunkel
Sascha Ossowski

In parcel delivery, the “last mile” from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens (“the crowd”) perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers’ smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach.

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EUMAS Conference 2017 Conference Paper

Event-Driven Agents: Enhanced Perception for Multi-Agent Systems Using Complex Event Processing

Jeremias Dötterl
Ralf Bruns
Jürgen Dunkel
Sascha Ossowski

Abstract With the increase of existing sensor devices grows the data volume that is available to software systems to understand the physical world. The use of this sensor data in Multi-Agent Systems (MAS) could allow agents to improve their comprehension of the environment and provide additional information for their decision making. Unfortunately, conventional BDI agents cannot make sense of low-level sensor data directly due to their limited event comprehension capabilities: The agents react to single, isolated events rather than to multiple, related events and therefore are not able to efficiently detect complex higher-level situations from low-level sensor data. In this paper, we present Event-Driven Agents as a novel concept to enhance the perception of conventional BDI agents with Complex Event Processing. Their intended use is in environments in which percepts arrive with high speed and are too low-level to be efficiently interpreted by conventional agents directly. In a case study, we show how Event-Driven Agents can be used to address the bicycle rebalancing problem, which bike sharing systems face in their daily operations. Without an intelligent and timely intervention, bike stations of bike sharing systems tend to become empty or full quickly, which prevents the rental or return at these stations. We demonstrate how Event-Driven Agents, based on live data, can detect situations occurring in the bike sharing system in order to initiate appropriate rebalancing efforts.

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EUMAS Conference 2016 Conference Paper

A Proposal for Situation-Aware Evacuation Guidance Based on Semantic Technologies

Holger Billhardt
Jürgen Dunkel
Alberto Fernández 0002
Marin Lujak
Ramón Hermoso
Sascha Ossowski

Abstract Smart Cities require reliable means for managing installations that offer essential services to the citizens. In this paper we focus on the problem of evacuation of smart buildings in case of emergencies. In particular, we present a proposal for an evacuation guidance system that provides individualized evacuation support to people in case of emergencies. The system uses sensor technologies and Complex Event Processing to obtain information about the current situation of a building in each moment. Using semantic Web technologies, this information is merged with static knowledge (special user characteristics, building topology, evacuation knowledge) in order to determine (and dynamically update) the most appropriate individualized evacuation routes for each user.

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