Jan D. Gehrke

Traffic Prediction for Agent Route Planning

This paper describes a methodology and initial results of predicting traffic by autonomous agents within a vehicle route planning system. The traffic predictions are made using AQ21, a natural induction system that learns and applies attributional rules. The presented methodology is implemented and experimentally evaluated within a multiagent-based simulation system. Initial results obtained by simulation indicate advantage of agents using AQ21 predictions when compared to naive agents that make no predictions and agents that use only weather-related information.

Towards Ontology-Based Multiagent Simulations: The Plasma Approach.

In multiagent-based simulation systems the agent programming paradigm is adopted for simulation. This simulation approach offers the promise to facilitate the design and development of complex simulations, both regarding the distinct simulation actors and the simulation environment itself. We introduce the simulation middleware PlaSMA which extends the JADE agent framework with a simulation control that ensures synchronization and provides a world model based on a formal ontological description of the respective application domain. We illustrate the benefits of an ontology grounding for simulation design and discuss further gains to be expected from recent advances in ontology engineering, namely the adaption of foundational ontologies and modelling-patterns.

Designing a Simulation Middleware for FIPA Multiagent Systems

Multiagent systems ease the implementation of software systems to control complex processes. Instead of monolithic programs, decision-making is delegated to software agents as local entities. Like in software development in general, testing and evaluation play an important role also for multiagent systems. Particularly, because run-time interactions between agents and their effects cannot always be predicted at design time. Multiagent-based simulation is an adequate means to evaluate agents regarding their applicability in real-world operation. However, general agent development frameworks do not consider simulation-specific issues. Because they provide no means for synchronisation, an additional simulation middleware is required. Temporal criteria that are relevant for middleware design are defined in this paper. Furthermore, the actual implementation and example applications in logistics are presented.

An Agent-based Approach to Autonomous Logistic Processes Collaborative Research Centre 637: Autonomous Cooperating Logistic Processes

This paper presents the research activities of the Collaborative Research Centre (CRC) 637 “Autonomous Cooperating Logistic Processes—A Paradigm Shift and its Limitations” at the University of Bremen. After a motivation of autonomous logistics as an answer to current trends in increasingly dynamic markets, we sketch the structure and aims of the interdisciplinary CRC. We present several interpretations of the central motive of autonomous control, pursued by sub-projects over the course of the first project period, and focus on an agent-based approach to autonomous logistics.

Agent-Based and Discrete Event Simulation of Autonomous Logistic Processes

The current trends and recent changes in logistics lead to new, complex and partially conflicting requirements on logistic planning and control systems. Currently available strategies and methodologies do not address these new requirements sufficiently. The concept of autonomous logistic processes intends to overcome these drawbacks together with latest information and communication technologies. Their analysis and design is subject to simulation studies. Two simulation systems for the analysis of autonomy in logistics with an agent-based and a discrete event approach are presented. Both systems are designed and suitable for different aspects of autonomous logistic processes.

Evaluating situation awareness of autonomous systems

Autonomous systems proved to be very successful in specialized problem domains. But their perception, reasoning, planning and behavior capabilities are generally designed to fit special purposes. For instance, a robotic agent perceives its environment in a way that was defined in advance by a human designer. The agent does not exhibit a certain perception behavior because it actually thinks it would be reasonable to do so. But with an increasing level of autonomy as well as a larger temporal and spatial scope of agent operation higher-level situation analysis and assessment become essential. This paper examines criteria for evaluating situation-awareness of autonomous systems and proposes methods to satisfy them. An example application scenario is presented that provides initial results for evaluating situation-aware systems.

A Framework for distributed knowledge management in autonomous logistic processes

The trends and recent changes in logistics lead to complex and partially conflicting requirements on logistic planning and control systems. Due to the lack of efficiency of currently available strategies and methodologies, a new paradigm for logistics planning and control is required. An emerging approach is the analysis and design of autonomous logistic processes. Agents represent a modern approach for implementing autonomous systems. The challenge for the design of agent systems is to integrate the complex and dynamic knowledge required for reliable decision-making in logistics. To address this problem, we introduce a framework for distributed knowledge management in competitive environments. Our approach combines a general role model enabling distributed, flexible agent-based knowledge management services and a set of general decision parameters for rational agents.

A knowledge-based approach to behavior decision in intelligent vehicles

Recent advances in the field of intelligent vehicles have shown that it is possible nowadays to provide the driver with useful assistance systems, or even letting a car drive autonomously over long distances on highways. Usually these approaches are on a rather quantitative level. A knowledge-based approach as presented here has the advantage of a better comprehensibility and allows for formulating and using common sense knowledge and traffic rules while reasoning. In our approach a knowledge base is the central component for higher-level functionality. A qualitative mapping module abstracts from the quantitative data and stores symbolic facts in the knowledge base. The knowledge-based approach allows for easily integrating and adjusting background knowledge. Higher-level modules can query the knowledge base in order to evaluate the situation and decide what actions to perform. For the evaluation of the approach a prototype was developed in order to simulate traffic scenarios. In experiments behavior decision was applied for controlling the vehicle and its gaze.

Dynamic Decision Making on Embedded Platforms in Transport Logistics – A Case Study

Autonomous logistic processes aim at coping with logistic dynamics and complexity by local decision making to gain flexibility and robustness. This paper discusses resource-bounded logistics decision making using software agents and task decomposition. Simulations show the feasibility of dynamic vehicle routing and quality monitoring on embedded systems for the transport of perishable goods.

Transport scenario for the intelligent container

Previous chapters described among others the application of autonomous cooperation on embedded systems, in sensor networks, transport planning and communication systems. For practical demonstration of the implications of the described studies the prototype of the intelligent container was linked to an agent system for transport coordination including communication gateway and vehicle location. We arranged a demonstration scenario that illustrates the cooperation of these system components by displaying the processes that are related to one selected freight item and one transport vehicle.