Identifying confidentiality violations in architectural design using palladio

Meeting confidentiality requirements in software systems is vital for organizations. Considering confidentiality in early development phases such as the architectural design phase is beneficial compared to late phases such as the implementation because fixing design issues is more cost-efficient in early phases. This tutorial introduces an approach for modeling and statically analyzing confidentiality in software architectures within the Palladio tool suite. Besides foundational knowledge, the tutorial provides a practical hands-on session using the tool. The goal is to show that it is already possible to consider confidentiality in the early design process and that this consideration can be integrated into existing architectural design tools

Augmented Collective Digital Twins for Self-Organising Cyber-Physical Systems

Context. Self-organising and collective computing approaches are increasingly applied to large-scale cyber-physical systems (CPS), enabling them to adapt and cooperate in dynamic environments. Also, in CPS engineering, digital twins are often leveraged to provide synchronised logical counterparts of physical entities, whereas in sensor networks the different-but-related concept of virtual device is used e.g. to abstract groups of sensors. Vision. We envision the design concept of 'augmented collective digital twin' that captures digital twins at a collective level extended with purely virtual devices. We argue that this concept can foster the engineering of self-organising CPS by providing a holistic, declarative, and integrated system view. Method. From a review and proposed taxonomy of logical devices comprehending both digital twins and virtual devices, we reinterpret a meta-model for self-organising CPSs and discuss how it can support augmented collective digital twins. We illustrate the approach in a crowd-aware navigation scenario, where virtual devices are opportunistically integrated into the system to enhance spatial coverage, improving navigation capabilities. Conclusion. By integrating physical and virtual devices, the novel notion of augmented collective digital twin paves the way to self-improving system functionality and intelligent use of resources in self-organising CPSs. Conclusion. By integrating physical and virtual devices, the novel notion of augmented collective digital twin paves the way to self-improving system functionality and intelligent use of resources in self-organising CPSs

A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.Comment: Presented at 3th International Workshop on Multi-Agent Based Simulation, Valencia, Spain, 5th June 201

Decentralized Self-adaptation in Large-scale Distributed Systems

The evolution of technology is leading to a world where computational systems are made of a huge number of components spread over a logical network: these components operate in a highly dynamic and unpredictable environment, joining or leaving the system and creating connections between them at runtime. This scenario poses new challenges to software engineers that have to design and implement such complex systems. We want to address this problem, designing and developing an infrastructure, GRU, that uses self-adaptive decentralized techniques to manage large-scale distributed systems. GRU will help developers to focus on the functional part of their application instead of the needed self-adaptive infrastructure. We aim to evaluate our project with concrete case studies, providing evidence on the validity of our approach, and with the feedback provided by developers that will test our system. We believe this approach can contribute to fill the gap between the theoretical study of self-adaptive systems and their application in a production context

From Physical to Virtual: Widening the Perspective on Multi-Agent Environments

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23850-0_9Since more than a decade, the environment is seen as a key element when analyzing, developing or deploying Multi-Agent Systems (MAS) applications. Especially, for the development of multi-agent platforms it has become a key concept, similarly to many application in the area of location-based, distributed systems. An emerging, prominent application area for MAS is related to Virtual Environments. The underlying technology has evolved in a way, that these applications have grown out of science fiction novels till research papers and even real applications. Even more, current technologies enable MAS to be key components of such virtual environments. In this paper, we widen the concept of the environment of a MAS to encompass new and mixed physical, virtual, simulated, etc. forms of environments. We analyze currently most interesting application domains based on three dimensions: the way different "realities" are mixed via the environment, the underlying natures of agents, the possible forms and sophistication of interactions. In addition to this characterization, we discuss how this widened concept of possible environments influences the support it can give for developing applications in the respective domains.Carrascosa Casamayor, C.; Klugl, F.; Ricci, A.; Boissier, O. (2015). From Physical to Virtual: Widening the Perspective on Multi-Agent Environments. En Agent Environments for Multi-Agent Systems IV. 4th International Workshop, E4MAS 2014 - 10 Years Later, Paris, France, May 6, 2014. 133-146. https://doi.org/10.1007/978-3-319-23850-0_9S133146Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. 43(3), 16:1–16:43 (2011)Argente, E., Boissier, O., Carrascosa, C., Fornara, N., McBurney, P., Noriega, P., Ricci, A., Sabater-Mir, J., et al.: The role of the environment in agreement technologies. AI Rev. 39(1), 21–38 (2013)Barreteau, O., et al.: Our companion modelling approach. J. Artif. Soc. Soc. Simul. 6(1), 1–6 (2003)Boissier, O., Bordini, R.H., Hübner, J.F., Ricci, A., Santi, A.: Multi-agent oriented programming with jacamo. Sci. Comput. Program. 78(6), 747–761 (2013)Burdea, G., Coiffet, P.: Virtual Reality Technology. Wiley, New York (2003)Castelfranchi, C., Pezzullo, G., Tummolini, L.: Behavioral implicit communication (BIC): communicating with smart environments via our practical behavior and its traces. Int. J. Ambient Comput. Intell. 2(1), 1–12 (2010)Castelfranchi, C., Piunti, M., Ricci, A., Tummolini, L.: AMI systems as agent-based mirror worlds: bridging humans and agents through stigmergy. In: Bosse, T. (ed.) Agents and Ambient Intelligence, Ambient Intelligence and Smart Environments, pp. 17–31. IOS Press, Amsterdam (2012)Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison Wesley Longman, Harlow (1999)Gelernter, D.: Mirror Worlds - or the Day Software Puts the Universe in a Shoebox: How it Will Happen and What it Will Mean. Oxford University Press, New York (1992)Gibson, W.: Neuromancer. Ace, New York (1984)Klügl, F., Fehler, M., Herrler, R.: About the role of the environment in multi-agent simulations. In: Weyns, D., Van Parunak, H.D., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 127–149. Springer, Heidelberg (2005)Krueger, M.: Artificial Reality II. Addison-Wesley, New York (1991)Luck, M., Aylett, R.: Applying artificial intelligence to virtual reality: intelligent virtual environments. Appl. Artif. Intell. 14(1), 3–32 (2000)Dorigo, M., Floreano, D., Gambardella, L.M., et al.: Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag. 20(4), 60–71 (2013)Milgram, P., Kishino, A.F.: Taxonomy of mixed reality visual displays. IEICE Trans. Inf. Syst. E77–D(12), 1321–1329 (1994)Olsson, T., Salo, M.: Online user survey on current mobile augmented reality applications. In: Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2011, pp. 75–84. IEEE Computer Society, Washington, DC, USA (2011)Saunier, J., Balbo, F., Pinson, S.: A formal model of communication and context awareness in multiagent systems. J. Logic Lang. Inform. 23(2), 219–247 (2014)Stephenson, N.: Snow Crash. Bantam Books, New York (1992)Tummolini, L., Castelfranchi, C.: Trace signals: the meanings of stigmergy. In: Weyns, D., Van Parunak, H.D., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 141–156. Springer, Heidelberg (2007)Weyns, D., Omicini, A., Odell, J.: Environment as a first class abstraction in multiagent systems. Auton. Agent. Multi-Agent Syst. 14(1), 5–30 (2007)Weyns, D., Schelfthout, K., Holvoet, T., Lefever, T.: Decentralized control of e’gv transportation systems. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 67–74. ACM (2005)Weyns, D., Schumacher, M., Ricci, A., Viroli, M., Holvoet, T.: Environments in multiagent systems. Knowl. Eng. Rev. 20(2), 127–141 (2005

The vision of self-evolving computing systems

Algorithms and the Foundations of Software technolog

Exploring quality-aware architectural transformations at run-time: the ENIA case

Adapting software systems at run-time is a key issue, especially when these systems consist of components used as intermediary for human-computer interaction. In this sense, model transformation techniques have a widespread acceptance as a mechanism for adapting and evolving the software architecture of such systems. However, existing model transformations often focus on functional requirements, and quality attributes are only manually considered after the transformations are done. This paper aims to improve the quality of adaptations and evolutions in component-based software systems by taking into account quality attributes within the model transformation process. To this end, we present a quality-aware transformation process using software architecture metrics to select among many alternative model transformations. Such metrics evaluate the quality attributes of an architecture. We validate the presented quality-aware transformation process in ENIA, a geographic information system whose user interfaces are based on coarsegrained components and need to be adapted at run-time

Evaluating a reinforcement learning algorithm with a general intelligence test

In this paper we apply the recent notion of anytime universal intelligence tests to the evaluation of a popular reinforcement learning algorithm, Q-learning. We show that a general approach to intelligence evaluation of AI algorithms is feasible. This top-down (theory-derived) approach is based on a generation of environments under a Solomonoff universal distribution instead of using a pre-defined set of specific tasks, such as mazes, problem repositories, etc. This first application of a general intelligence test to a reinforcement learning algorithm brings us to the issue of task-specific vs. general AI agents. This, in turn, suggests new avenues for AI agent evaluation and AI competitions, and also conveys some further insights about the performance of specific algorithms. © 2011 Springer-Verlag.We are grateful for the funding from the Spanish MEC and MICINN for projects TIN2009-06078-E/TIN, Consolider-Ingenio CSD2007-00022 and TIN2010-21062-C02, for MEC FPU grant AP2006-02323, and Generalitat Valenciana for Prometeo/2008/051.Insa Cabrera, J.; Dowe, DL.; Hernández Orallo, J. (2011). Evaluating a reinforcement learning algorithm with a general intelligence test. En Advances in Artificial Intelligence. Springer Verlag (Germany). 7023:1-11. https://doi.org/10.1007/978-3-642-25274-7_1S1117023Dowe, D.L., Hajek, A.R.: A non-behavioural, computational extension to the Turing Test. In: Intl. Conf. on Computational Intelligence & multimedia applications (ICCIMA 1998), Gippsland, Australia, pp. 101–106 (1998)Genesereth, M., Love, N., Pell, B.: General game playing: Overview of the AAAI competition. AI Magazine 26(2), 62 (2005)Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000)Hernández-Orallo, J.: A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. In: Hutter, M., et al. (eds.) 3rd Intl. Conf. on Artificial General Intelligence, Atlantis, pp. 182–183 (2010)Hernández-Orallo, J.: On evaluating agent performance in a fixed period of time. In: Hutter, M., et al. (eds.) 3rd Intl. Conf. on Artificial General Intelligence, pp. 25–30. Atlantis Press (2010)Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)Legg, S., Hutter, M.: A universal measure of intelligence for artificial agents. Intl. Joint Conf. on Artificial Intelligence, IJCAI 19, 1509 (2005)Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007)Levin, L.A.: Universal sequential search problems. Problems of Information Transmission 9(3), 265–266 (1973)Li, M., Vitányi, P.: An introduction to Kolmogorov complexity and its applications, 3rd edn. Springer-Verlag New York, Inc. (2008)Sanghi, P., Dowe, D.L.: A computer program capable of passing IQ tests. In: Proc. 4th ICCS International Conference on Cognitive Science (ICCS 2003), Sydney, Australia, pp. 570–575 (2003)Solomonoff, R.J.: A formal theory of inductive inference. Part I. Information and Control 7(1), 1–22 (1964)Strehl, A.L., Li, L., Wiewiora, E., Langford, J., Littman, M.L.: PAC model-free reinforcement learning. In: Proc. of the 23rd Intl. Conf. on Machine Learning, ICML 2006, New York, pp. 881–888 (2006)Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. The MIT press (1998)Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)Veness, J., Ng, K.S., Hutter, M., Silver, D.: Reinforcement learning via AIXI approximation. In: Proc. 24th Conf. on Artificial Intelligence (AAAI 2010), pp. 605–611 (2010)Watkins, C.J.C.H., Dayan, P.: Q-learning. Machine learning 8(3), 279–292 (1992)Weyns, D., Parunak, H.V.D., Michel, F., Holvoet, T., Ferber, J.: Environments for multiagent systems state-of-the-art and research challenges. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 1–47. Springer, Heidelberg (2005)Whiteson, S., Tanner, B., White, A.: The Reinforcement Learning Competitions. The AI magazine 31(2), 81–94 (2010)Woergoetter, F., Porr, B.: Reinforcement learning. Scholarpedia 3(3), 1448 (2008)Zatuchna, Z., Bagnall, A.: Learning mazes with aliasing states: An LCS algorithm with associative perception. Adaptive Behavior 17(1), 28–57 (2009