Alexander Jungmann

Automated Online Service Composition

Services are self-contained and platform independent software components that aim at maximizing software reuse. The automated composition of services to a target software artifact has been tackled with many AI techniques, but existing approaches make unreasonably strong assumptions such as a predefined data flow, are limited to tiny problem sizes, ignore non-functional properties, or assume offline service repositories. This paper presents an algorithm that automatically composes services without making such assumptions. We employ a backward search algorithm that starts from an empty composition and prep ends service calls to already discovered candidates until a solution is found. Available services are determined during the search process. We implemented our algorithm, performed an experimental evaluation, and compared it to other approaches.

Learning service recommendations

The as a service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilised on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our approach of modelling this service composition and recommendation process as Markov decision process and of solving it by means of reinforcement learning. A case study serves as proof of concept.

An approach towards adaptive service composition in markets of composed services

On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques.

Miniature robot BeBot: Mechatronic test platform for self-x properties

Machines are omnipresent. They produce, they transport. Machines facilitate work and assist. The increasing penetration of mechanical engineering by information technology enables considerable benefits. We refer to such systems as advanced mechatronic systems, which relay on the close interaction of mechanics, electric/electronics, control engineering and software engineering. Hence, the design and production of such systems is an interdisciplinary and complex task. Our ambition is a new school for the design of advanced mechatronic systems. Consequently, we need an avant-garde basic system which can be used to develop and to test future applications. The miniature robot BeBot is such a basic system. This robot constitutes the test bench for the applications, being based on modern approaches, such as self-optimization, self-organization and self-coordination as well as on the use of new manufacturing technologies.

Increasing Learning Speed by Imitation in Multi-robot Societies

The paradigm of imitation provides a powerful means for increasing the overall learning speed in a group of robots. While separately exploring the environment in order to learn how to behave with respect to a pre-defined goal, a robot gathers experience based on its own actions and interactions with the surroundings, respectively. By accumulating additional experience via observing the behaviour of other robots, the learning process can be significantly improved in terms of speed and quality. Within this article we present an approach, that enables robots in a multi-robot society to imitate any other available robot without imposing unnecessary restrictions regarding the robots’ design. Therefore, it benefits not only from its own actions, but also from actions that an observed robot performs. In order to realise the imitation paradigm, we solve three main challenges, namely enabling a robot to decide whom and when to imitate, to interpret and thereby understand the behaviour of an observed robot, and to integrate the experience gathered by observation into its individual learning process.

Increasing motion information by using universal tracking of 2D-features

As long as visual features for recognition are known in advance and remain static due to a controlled environment, object tracking is state-of-the-art. Tracking objects in dynamically changing environments is still a challenge. Even harder is the tracking of moving objects with a moving camera. Our algorithm realizes a deterministic approach to track any 2D-features representable in a general way. Using our algorithm results in a more dense motion flow field and objects that move with a different motion vector than the environment can be clearly identified.

Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services

Automatic service composition is still a challenging task. It is even more challenging when dealing with a dynamic market of services for end users. New services may enter the market while other services are completely removed. Furthermore, end users are typically no experts in the domain in which they formulate a request. As a consequence, ambiguous user requests will inevitably emerge and have to be taken into account. To meet these challenges, we propose a new approach that combines automatic service composition with adaptive service recommendation. A best first backward search algorithm produces solutions that are functional correct with respect to user requests. An adaptive recommendation system supports the search algorithm in decision-making. Reinforcement Learning techniques enable the system to adjust its recommendation strategy over time based on user ratings. The integrated approach is described on a conceptional level and demonstrated by means of an illustrative example from the image processing domain.

Learning Recommendation System for Automated Service Composition

The as a Service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilized on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our general idea of modeling this service composition and recommendation process as Markov Decision Process and of solving it by means of Reinforcement Learning. A case study serves as proof of concept.

Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition

A major goal of the On-The-Fly Computing project is the automated composition of individual services based on services that are available in dynamic markets. Dependent on the granularity of a market, different alternatives that satisfy the requested functional requirements may emerge. In order to select the best solution, services are usually selected with respect to their quality in terms of inherent non-functional properties. In this paper, we describe our idea of how to model this service selection process as a Markov Decision Process, which we in turn intend to solve by means of Reinforcement Learning techniques in order to control the underlying service composition process. In addition, some initial issues with respect to our approach are addressed.

A test bed for investigating self-x properties in multi-robot societies

In this paper, we introduce a test bed for demonstrating and investigating self-x properties, such as self-optimization and self-organization, within the scope of multi-robot societies under realistic conditions. By doing so, we shift the investigation and demonstration of biologically inspired mechanisms from the simulative point of view to a dynamic and more complex realistic environment. For this purpose, we developed a controlled real-world environment to overcome common problems such as self-localization. Furthermore, we elaborated a concept for a descriptive robotic real-world game. By means of this game, we provide an instrument, which is easily accessible for any kind of audience on the one hand, while it still leaves enough space for an extensive scientific investigation on the other hand.