TY - UNPB T1 - Complexity Explained: A Grassroot Collaborative Initiative to Create a Set of Essential Concepts of Complex Systems. Y1 - 2019 A1 - Manlio De Domenico A1 - Chico Camargo A1 - Carlos Gershenson A1 - Daniel Goldsmith A1 - Sabine Jeschonnek A1 - Lorren Kay A1 - Stefano Nichele A1 - José Nicolás A1 - Thomas Schmickl A1 - Massimo Stella A1 - Josh Brandoff A1 - Ángel José Martínez Salinas A1 - Hiroki Sayama AB - Complexity science, also called complex systems science, studies how a large collection of components – locally interacting with each other at small scales – can spontaneously self-organize to exhibit non-trivial global structures and behaviors at larger scales, often without external intervention, central authorities or leaders. The properties of the collection may not be understood or predicted from the full knowledge of its constituents alone. Such a collection is called a complex system and it requires new mathematical frameworks and scientific methodologies for its investigation. UR - https://complexityexplained.github.io N1 - https://complexityexplained.github.io ER - TY - CHAP T1 - Self-organized UAV Traffic in Realistic Environments T2 - Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on Y1 - 2016 A1 - Csaba Virágh A1 - Máté Nagy A1 - Carlos Gershenson A1 - Gábor Vásárhelyi JF - Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on PB - IEEE CY - Daejeon, South Korea ER - TY - JOUR T1 - Living is Information Processing: From Molecules to Global Systems JF - Acta Biotheoretica Y1 - 2013 A1 - Farnsworth, Keith D. A1 - Nelson, John A1 - Gershenson, Carlos AB - We extend the concept that life is an informational phenomenon, at every level of organisation, from molecules to the global ecological system. According to this thesis: (a) living is information processing, in which memory is maintained by both molecular states and ecological states as well as the more obvious nucleic acid coding; (b) this information processing has one overall function–-to perpetuate itself; and (c) the processing method is filtration (cognition) of, and synthesis of, information at lower levels to appear at higher levels in complex systems (emergence). We show how information patterns, are united by the creation of mutual context, generating persistent consequences, to result in `functional information'. This constructive process forms arbitrarily large complexes of information, the combined effects of which include the functions of life. Molecules and simple organisms have already been measured in terms of functional information content; we show how quantification may be extended to each level of organisation up to the ecological. In terms of a computer analogy, life is both the data and the program and its biochemical structure is the way the information is embodied. This idea supports the seamless integration of life at all scales with the physical universe. The innovation reported here is essentially to integrate these ideas, basing information on the `general definition' of information, rather than simply the statistics of information, thereby explaining how functional information operates throughout life. VL - 61 UR - http://arxiv.org/abs/1210.5908 ER - TY - CONF T1 - Action Selection Properties in a Software Simulated Agent T2 - {MICAI} 2000: Advances in Artificial Intelligence Y1 - 2000 A1 - C. Gershenson A1 - P. P. González A1 - J. Negrete ED - {O. Cairó ED - L. E. Súcar, F.J. Cantú AB - This article analyses the properties of the Internal Behaviour network, an action selection mechanism previously proposed by the authors, with the aid of a simulation developed for such ends. A brief review of the Internal Behaviour network is followed by the explanation of the implementation of the simulation. Then, experiments are presented and discussed analysing the properties of the action selection in the proposed model. JF - {MICAI} 2000: Advances in Artificial Intelligence T3 - Lecture Notes in Artificial Intelligence PB - Springer, Verlag CY - Acapulco, México VL - 1793 UR - http://uk.arxiv.org/abs/cs.AI/0211039 ER - TY - CONF T1 - A Model for Combination of External and Internal Stimuli in the Action Selection of an Autonomous Agent T2 - {MICAI} 2000: Advances in Artificial Intelligence Y1 - 2000 A1 - P. P. González A1 - J. Negrete A1 - A. Barreiro A1 - C. Gershenson. ED - {O. Cairó ED - L. E. Súcar, F.J. Cantú AB - This paper proposes a model for combination of external and internal stimuli for the action selection in an autonomous agent, based in an action selection mechanism previously proposed by the authors. This combination model includes additive and multiplicative elements, which allows to incorporate new properties, which enhance the action selection. A given parameter a, which is part of the proposed model, allows to regulate the degree of dependence of the observed external behaviour from the internal states of the entity. JF - {MICAI} 2000: Advances in Artificial Intelligence T3 - Lecture Notes in Artificial Intelligence PB - Springer, Verlag CY - Acapulco, México VL - 1793 UR - http://uk.arxiv.org/abs/cs.AI/0211040 ER - TY - CONF T1 - Thinking Adaptive: Towards a Behaviours Virtual Laboratory T2 - {SAB} 2000 Proceedings Supplement Y1 - 2000 A1 - C. Gershenson A1 - P. P. González A1 - J. Negrete ED - Jean-Arcady Meyer ED - Alain Berthoz ED - Dario Floreano ED - Herbert L. Roitblat ED - Stewart W. Wilson AB - In this paper we name some of the advantages of virtual laboratories; and propose that a Behaviours Virtual Laboratory should be useful for both biologists and AI researchers, offering a new perspective for understanding adaptive behaviour. We present our development of a Behaviours Virtual Laboratory, which at this stage is focused in action selection, and show some experiments to illustrate the properties of our proposal, which can be accessed via Internet. JF - {SAB} 2000 Proceedings Supplement PB - ISAB press CY - Paris, France UR - http://uk.arxiv.org/abs/cs/0211028 ER -