Ingo J. Timm

An Agent Based Parallel Particle Swarm Optimization - APPSO

As the complexity of optimization problems increases, new scalable architectures for variable problem complexitys are needed. In this paper we introduce an agent based framework for distributing and managing a particle swarm on several interconnected computers. Agent Based Parallel Particle Swarm Optimization (APPSO) accelerates the optimization through parallelization and strategical niching, offers dynamic scalability at runtime, and fault tolerance. Due to its load balancing feature APPSO runs efficient on heterogeneuos system. Two experiment series on a prototype implementation demonstrate the performance gain achieved by APPSO.

Large scale multiagent simulation on the grid

Simulating large scale, distributed systems of autonomous decision-makers, e.g. global logistics supply networks, is a huge challenge for hardware and software infrastructure as well as managing the process of simulation. Facing this challenge, we are going to introduce an architecture for large scale grid-based simulation infrastructure to enable high performance multiagent simulation experiments that deal with a huge amount of software agents, i.e. more than ten thousands of agents.

Ontology-Based Capability Management for Distributed Problem Solving in the Manufacturing Domain

Providing services within multiagent systems, an agent has to register itself with a distinct description of its main capabilities in yellow page services. If another agent is requesting to solve a specific task, it has to be decided whether or not the requested agent is capable of performing the task successfully. We are assuming that task requirements as well as capabilities are specified using ontologies. Decision is easy if the concepts of requested task requirements are directly mapping to concepts of provided capabilities. However, concept inequality may occur. Especially in production engineering with its increasing concern of knowledge about sophisticated manufacturing processes, relying on simple concept equality is not suitable to fulfill demands of current industrial applications. Thus, enhanced methods like ontology-based capability management presented in this paper have to be established to address this problem. For the case of indifferent concepts we are introducing a conflict-based approach for capability negotiation as well as an application scenario for this approach in the manufacturing domain.

Implications of mass customisation on business information systems

Modern information and communication technologies like the internet or mobile computing are enforcing changes of Business Information Systems (BIS) in the context of an evolving e-business and global economy. This paper analyses the impact of mass customisation on future BIS, with a focus on globally distributed value chains. Consequently, we address the question of how to scale mass customisation in existing supply webs. New and innovative concepts are needed to keep transaction costs low and information logistics transparent. Using a case study from the footwear industry, we present an innovative multiagent approach, which uses information represented with explicit machine-understandable semantics for coordinating and negotiating activities throughout the supply web.

Knowledge-based risk assessment for intelligent vehicles

In order to set up assistance systems in intelligent vehicles or to control an autonomous vehicle a number of cognitive functions has to be realized in an integrated architecture. In this paper we propose a knowledge-based risk assessment procedure in order to identify objects which might be dangerous for the own vehicle. Having an advanced vision system with gaze control in mind a knowledge-based risk assessment can determine where to concentrate the attention. The approach is evaluated by simulating different traffic scenes.

Don't Go With the Ant Flow: Ant-Inspired Traffic Routing in Urban Environments

Traffic routing is a well-established optimization problem in traffic management. Here, we address dynamic routing problems where the load of roads is taken into account dynamically, aiming at the optimization of required travel times. We investigate ant-based algorithms that can handle dynamic routing problems, but suffer from negative emergent effects like road congestions. These negative effects are inherent in the design of ant-based algorithms. In this article we propose an inverse ant-based routing algorithm to (a) maintain the positive features of ant-based algorithms for dynamic routing problems, while (b) avoiding the occurrence of negative emerging effects, like road congestion. We evaluated the performance of the proposed algorithm by comparing its results with two alternative routing algorithms, namely, A*, which is a static routing algorithm, and an iterative approach. In particular, the iterative approach is used for providing an upper bound, as it uses routing knowledge in a number of calibration runs, to determine the actual load, before the effective routing is done. For the evaluation we used the agent-based traffic simulation system MAINSIM. The evaluation was done with one synthetic and two real-world scenarios, to outline the practical relevance of our findings. Based on these evaluations, we can conclude that the inverse ant-based routing approach is particularly suited for a scenario with a high traffic density, as it can adapt the routing of each vehicle, while avoiding the negative emerging effects of conventional ant-based routing algorithms.

A hybrid time management approach to agent-based simulation

In this paper we describe a time management approach to distributed agent-based simulation. We propose a new time management policy by joining optimistic synchronization techniques and domain-specific knowledge based on agent communication protocols. With respect to our experimental results, we assume that our approach helps to prevent too optimistic event execution. Consequently, the probability of time consuming rollbacks is reduced in comparison to a pure time warp based solutions. The approach has been implemented as a synchronization service for the JADE agent platform SimJade. The paper concludes by the discussion of our experimental results and future improvements.

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.

From Agents to Multiagent Systems

In the previous chapter agents and their properties have been introduced. In real-world business applications, it is assumed that the benefit of agent technology is reached by dynamic interaction of autonomous agents. This interaction and co-operation forms a multiagent system (“MAS”). The organization of agents within such systems is strongly related to organization theory. The specific flexibility of MAS arises from the ability to follow predefined structures or evolve structures from dynamic interaction. In this chapter, fundamental concepts and properties of MAS are introduced with special focus on interaction and communication, roles, and structures.