@article{25107, abstract = {{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.}}, author = {{Jungmann, Alexander and Mohr, Felix}}, journal = {{Journal of Internet Services and Applications 6(1)}}, pages = {{1--18}}, title = {{{An approach towards adaptive service composition in markets of composed services}}}, year = {{2015}}, } @article{25108, abstract = {{Autonomous adaptation in self-adapting embedded real-time systems introduces novel risks as it may lead to unforeseen system behavior. An anomaly detection framework integrated in a real-time operating system can ease the identification of such suspicious novel behavior and, thereby, offers the potential to enhance the reliability of the considered self-x system. However, anomaly detection is based on knowledge about normal behavior. When dealing with self-reconfiguring applications, normal behavior changes. Hence, knowledge base requires adaptation or even re-construction at runtime. The stringent restrictions of real-time systems considering runtime and memory consumption make this task to a really challenging problem. We present our idea for online construction of application behavior knowledge that does not rely on training phase. The applications' behavior is defined by the application's system call invocations. For the knowledge base, we exploit suffix trees as they offer potentials to represent application behavior patterns and associated information in a compact manner. The online algorithm provided by suffix trees is a basis to construct the knowledge base with low computational effort. Anomaly detection and classification is integrated into the online construction method. New behavioral patterns do not unconditionally update the behavior knowledge base. They are evaluated in a context-related manner inspired by Danger Theory, a special discipline of artificial immune systems. Copyright © 2015 John Wiley & Sons, Ltd.}}, author = {{Rammig, Franz-Josef and Stahl, Katharina}}, journal = {{Concurrency and Computation: Practice and Experience }}, title = {{{Online behavior classification for anomaly detection in self-x real-time systems}}}, year = {{2015}}, } @article{25109, author = {{Sudhakar, Krishna and Zhao, Yuhong and Rammig, Franz-Josef}}, journal = {{Concurrency and Computation: Practice and Experience }}, title = {{{Efficient Integration of Online Model Checking into a Small-Footprint Real-Time Operating System}}}, year = {{2015}}, } @article{25110, author = {{Joy, M. tech. Mabel Mary and Rammig, Franz-Josef}}, journal = {{Int. J. of Embedded Systems}}, title = {{{A hybrid methodology to detect memory leaks in soft real time embedded systems software}}}, year = {{2015}}, } @article{25111, author = {{Khaluf, Yara and Birattari, Mauro and Rammig, Franz-Josef}}, journal = {{Springer Jounal Soft Computing }}, title = {{{Analysis of long-term swarm performance based on short-term experiments}}}, year = {{2015}}, } @inproceedings{252, abstract = {{Video streaming is in high demand by mobile users. In cellular networks, however, the unreliable wireless channel leads to two major problems. Poor channel states degrade video quality and interrupt the playback when a user cannot sufficiently fill its local playout buffer: buffer underruns occur. In contrast, good channel conditions cause common greedy buffering schemes to buffer too much data. Such over-buffering wastes expensive wireless channel capacity. Assuming that we can anticipate future data rates, we plan the quality and download time of video segments ahead. This anticipatory download scheduling avoids buffer underruns by downloading a large number of segments before a drop in available data rate occurs, without wasting wireless capacity by excessive buffering.We developed a practical anticipatory scheduling algorithm for segmented video streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed measurements show that our solution essentially eliminates playback interruptions without significantly decreasing video quality.}}, author = {{Dräxler, Martin and Blobel, Johannes and Dreimann, Philipp and Valentin, Stefan and Karl, Holger}}, booktitle = {{Proceedings of the 2nd International Conference on Networked Systems (NetSys)}}, pages = {{1----8}}, title = {{{SmarterPhones: Anticipatory Download Scheduling for Wireless Video Streaming}}}, doi = {{10.1109/NetSys.2015.7089073}}, year = {{2015}}, } @inproceedings{253, abstract = {{Group signatures, introduced by Chaum and van Heyst [15], are an important primitive in cryptography. In group signature schemes every group member can anonymously sign messages on behalf of the group. In case of disputes a dedicated opening manager is able to trace signatures - he can extract the identity of the producer of a given signature. A formal model for static group signatures schemes and their security is defined by Bellare, Micciancio, and Warinschi [4], the case of dynamic groups is considered by Bellare, Shi, and Zhang [5]. Both models define group signature schemes with a single opening manager. The main difference between these models is that the number of group members in static schemes is fixed, while in dynamic schemes group members can join the group over time.}}, author = {{Blömer, Johannes and Juhnke, Jakob and Löken, Nils}}, booktitle = {{Proceedings of the Sixth International Conference on Mathematical Aspects of Computer and Information Sciences (MACIS)}}, pages = {{166--180}}, title = {{{Short Group Signatures with Distributed Traceability}}}, doi = {{10.1007/978-3-319-32859-1_14}}, year = {{2015}}, } @inproceedings{19959, author = {{Wahby, Mostafa and Hamann, Heiko}}, booktitle = {{Applications of Evolutionary Computation (EvoApplications 2015)}}, title = {{{On the Tradeoff between Hardware Protection and Optimization Success: A Case Study in Onboard Evolutionary Robotics for Autonomous Parallel Parking}}}, doi = {{10.1007/978-3-319-16549-3_61}}, year = {{2015}}, } @inproceedings{19960, abstract = {{Besides the life-as-it-could-be driver of artificial life research there is also the concept of extending natural life by creating hybrids or mixed societies that are built from natural and artificial components. In this paper we motivate and present the research program of the project flora robotica. Our objective is to develop and to investigate closely linked symbiotic relationships between robots and natural plants and to explore the potentials of a plant-robot society able to produce architectural artifacts and living spaces. These robot-plant bio-hybrids create synergies that allow for new functions of plants and robots. They also create novel design opportunities for an architecture that fuses the design and construction phase. The bio-hybrid is an example of mixed societies between 'hard' artificial and 'wet' natural life, which enables an interaction between natural and artificial ecologies. They form an embodied, self-organizing, and distributed cognitive system which is supposed to grow and develop over long periods of time resulting in the creation of meaningful architectural structures. A key idea is to assign equal roles to robots and plants in order to create a highly integrated, symbiotic system. Besides the gain of knowledge, this project has the objective to create a bio-hybrid system with a defined function and application -- growing architectural artifacts.}}, author = {{Hamann, Heiko and Wahby, Mostafa and Schmickl, Thomas and Zahadat, Payam and Hofstadler, Daniel and Stoy, Kasper and Risi, Sebastian and Faina, Andres and Veenstra, Frank and Kernbach, Serge and Kuksin, Igor and Kernbach, Olga and Ayres, Phil and Wojtaszek, Przemyslaw}}, booktitle = {{Proceedings of the 2015 IEEE Symposium on Artificial Life (IEEE ALIFE'15)}}, isbn = {{9781479975600}}, title = {{{Flora Robotica - Mixed Societies of Symbiotic Robot-Plant Bio-Hybrids}}}, doi = {{10.1109/ssci.2015.158}}, year = {{2015}}, } @article{19962, abstract = {{Recent approaches in evolutionary robotics (ER) propose to generate behavioral diversity in order to evolve desired behaviors more easily. These approaches require the definition of a behavioral distance, which often includes task-specific features and hence a priori knowledge. Alternative methods, which do not explicitly force selective pressure towards diversity (SPTD) but still generate it, are known from the field of artificial life, such as in artificial ecologies (AEs). In this study, we investigate how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge and detect how methods of generating SPTD can be transferred from the domain of AE to ER. A promising finding is that in both types of systems, in systems from ER that generate behavioral diversity and also in the investigated speciation model, selective pressure is generated towards unpopulated regions of search space. In a simple case study we investigate the practical implications of these findings and point to options for transferring the idea of self-organizing SPTD in AEs to the domain of ER.}}, author = {{Hamann, Heiko}}, issn = {{1064-5462}}, journal = {{Artificial Life}}, pages = {{464--480}}, title = {{{Lessons from Speciation Dynamics: How to Generate Selective Pressure Towards Diversity}}}, doi = {{10.1162/artl_a_00186}}, year = {{2015}}, } @inproceedings{19966, abstract = {{Aggregation is a crucial task in swarm robotics to ensure cooperation. We investigate the task of aggregation on an area specified indirectly by certain environmental features, here it is a light distribution. We extend the original BEECLUST algorithm, that implements an aggregation behavior, to an adaptive variant that automatically adapts to any light conditions. We compare these two control algorithms in a number of swarm robot experiments with different light conditions. The improved, adaptive variant is found to be significantly better in the tested setup.}}, author = {{Wahby, Mostafa and Weinhold, Alexander and Hamann, Heiko}}, booktitle = {{Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)}}, isbn = {{9781631901003}}, title = {{{Revisiting BEECLUST: Aggregation of Swarm Robots with Adaptiveness to Different Light Settings}}}, doi = {{10.4108/eai.3-12-2015.2262877}}, year = {{2015}}, } @inproceedings{19967, author = {{Wahby, Mostafa and Divband Soorati, Mohammad and von Mammen, Sebastian and Hamann, Heiko}}, booktitle = {{Proceedings. 25. Computational Intelligence Workshop}}, title = {{{Evolution of Controllers for Robot-Plant Bio-Hybdrids: A Simple Case Study Using a Model of Plant Growth and Motion}}}, year = {{2015}}, } @inproceedings{19980, abstract = {{Fitness function design is known to be a critical feature of the evolutionary-robotics approach. Potentially, the complexity of evolving a successful controller for a given task can be reduced by integrating a priori knowledge into the fitness function which complicates the comparability of studies in evolutionary robotics. Still, there are only few publications that study the actual effects of different fitness functions on the robot's performance. In this paper, we follow the fitness function classification of Nelson et al. (2009) and investigate a selection of four classes of fitness functions that require different degrees of a priori knowledge. The robot controllers are evolved in simulation using NEAT and we investigate different tasks including obstacle avoidance and (periodic) goal homing. The best evolved controllers were then post-evaluated by examining their potential for adaptation, determining their convergence rates, and using cross-comparisons based on the different fitness function classes. The results confirm that the integration of more a priori knowledge can simplify a task and show that more attention should be paid to fitness function classes when comparing different studies.}}, author = {{Hamann, Heiko and Divband Soorati, Mohammad}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)}}, pages = {{153--160}}, publisher = {{ACM}}, title = {{{The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge}}}, doi = {{10.1145/2739480.2754676}}, year = {{2015}}, } @inproceedings{19988, author = {{Hamann, Heiko and Schmickl, Thomas and Zahadat, Payam}}, booktitle = {{13th European Conference on Artificial Life (ECAL 2015)}}, pages = {{174}}, publisher = {{MIT Press}}, title = {{{Evolving Collective Behaviors With Diverse But Predictable Sensor States}}}, doi = {{10.7551/978-0-262-33027-5-ch036}}, year = {{2015}}, } @inbook{19989, author = {{Hamann, Heiko and Correll, Nikolaus and Kacprzyk, Janusz and Pedrycz, Witold}}, booktitle = {{Springer Handbook of Computational Intelligence}}, pages = {{1423--1431}}, publisher = {{Springer}}, title = {{{Probabilistic Modeling of Swarming Systems}}}, doi = {{10.1007/978-3-662-43505-2_74}}, year = {{2015}}, } @inproceedings{19990, author = {{Ding, Hongli and Hamann, Heiko}}, booktitle = {{First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2015)}}, title = {{{Dependability in Swarm Robotics: Error Detection and Correction}}}, year = {{2015}}, } @inproceedings{19991, author = {{Hamann, Heiko and Schmickl, Thomas and Kengyel, Daniela and Zahadat, Payam and Radspieler, Gerald and Wotawa, Franz}}, booktitle = {{Principles and Practice of Multi-Agent Systems (PRIMA 2015)}}, pages = {{201--217}}, title = {{{Potential of Heterogeneity in Collective Behaviors: A Case Study on Heterogeneous Swarms}}}, year = {{2015}}, } @article{19992, author = {{Valentini, Gabriele and Hamann, Heiko}}, issn = {{1935-3812}}, journal = {{Swarm Intelligence}}, pages = {{153--176}}, title = {{{Time-variant feedback processes in collective decision-making systems: influence and effect of dynamic neighborhood sizes}}}, doi = {{10.1007/s11721-015-0108-8}}, year = {{2015}}, } @inproceedings{20005, author = {{Dorigo, Marco and Hamann, Heiko and Valentini, Gabriele}}, booktitle = {{Proceedings of the 14th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2015)}}, title = {{{Efficient Decision-Making in a Self-Organizing Robot Swarm: On the Speed Versus Accuracy Trade-Off}}}, year = {{2015}}, } @inproceedings{20006, author = {{Dorigo, Marco and Hamann, Heiko and Valentini, Gabriele}}, booktitle = {{AAAI-15 Video Proceedings}}, title = {{{Self-organized collective decisions in a robot swarm}}}, year = {{2015}}, }