Vector bundles on the projective line and finite domination of chain complexes

Finitely dominated chain complexes over a Laurent polynomial ring in one indeterminate are characterised by vanishing of their Novikov homology. We present an algebro-geometric approach to this result, based on extension of chain complexes to sheaves on the projective line. We also discuss the K-theoretical obstruction to extension.Comment: v1: 11 page

Generic model for experimenting and using a family of classifiers systems: description and basic applications.

International audienceClassifiers systems are tools adapted to learn interactions between autonomous agents and their environments. However, there are many kinds of classifiers systems which differ in subtle technical ways. This article presents a generic model (called GEMEAU) that is common to the major kinds of classifiers systems. GEMEAU was developed for different simple applications which are also described

Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of bodies. Special focus is placed on the development of a new hybridization between Beam Search and the Population-based Ant Colony Optimization algorithm. An experimental evaluation shows all algorithms achieving exceptional performance on a hard benchmark problem. It is found that a properly tuned deterministic Beam Search always outperforms the remaining variants. Beam P-ACO, however, demonstrates lower parameter sensitivity, while offering superior worst-case performance. Being an anytime algorithm, it is then found to be the preferable choice for certain practical applications.Comment: Code available at https://github.com/lfsimoes/beam_paco__gtoc

A Deterministic Metaheuristic Approach using "Logistic Ants" for Combinatorial Optimization.

International audienceAnt algorithms are usually derived from a stochastic modeling based on some specific probability laws. We consider in this paper a full deterministic model of “logistic ants” which uses chaotic maps to govern the behavior of the artificial ants. We illustrate and test this approach on a TSP instance, and compare the results with the original Ant System algorithm. This change of paradigm —deterministic versus stochastic— implies a novel view of the internal mechanisms involved during the searching and optimizing process of ants

Ownership and control in a competitive industry

We study a differentiated product market in which an investor initially owns a controlling stake in one of two competing firms and may acquire a non-controlling or a controlling stake in a competitor, either directly using her own assets, or indirectly via the controlled firm. While industry profits are maximized within a symmetric two product monopoly, the investor attains this only in exceptional cases. Instead, she sometimes acquires a noncontrolling stake. Or she invests asymmetrically rather than pursuing a full takeover if she acquires a controlling one. Generally, she invests indirectly if she only wants to affect the product market outcome, and directly if acquiring shares is profitable per se. --differentiated products,separation of ownership and control,private benefits of control

An Agent-Based Approach to Self-Organized Production

The chapter describes the modeling of a material handling system with the production of individual units in a scheduled order. The units represent the agents in the model and are transported in the system which is abstracted as a directed graph. Since the hindrances of units on their path to the destination can lead to inefficiencies in the production, the blockages of units are to be reduced. Therefore, the units operate in the system by means of local interactions in the conveying elements and indirect interactions based on a measure of possible hindrances. If most of the units behave cooperatively ("socially"), the blockings in the system are reduced. A simulation based on the model shows the collective behavior of the units in the system. The transport processes in the simulation can be compared with the processes in a real plant, which gives conclusions about the consequencies for the production based on the superordinate planning.Comment: For related work see http://www.soms.ethz.c

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. 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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

Genome variations: Effects on the robustness of neuroevolved control for swarm robotics systems

Manual design of self-organized behavioral control for swarms of robots is a complex task. Neuroevolution has proved a viable alternative given its capacity to automatically synthesize controllers. In this paper, we introduce the concept of Genome Variations (GV) in the neuroevolution of behavioral control for robotic swarms. In an evolutionary setup with GV, a slight mutation is applied to the evolving neural network parameters before they are copied to the robots in a swarm. The genome variation is individual to each robot, thereby generating a slightly heterogeneous swarm. GV represents a novel approach to the evolution of robust behaviors, expected to generate more stable and robust individual controllers, and bene t swarm behaviors that can deal with small heterogeneities in the behavior of other members in the swarm. We conduct experiments using an aggregation task, and compare the evolved solutions to solutions evolved under ideal, noise-free conditions, and to solutions evolved with traditional sensor noise.info:eu-repo/semantics/acceptedVersio

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics