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In this paper, we propose an emotional model for robots in a multi-robot system, in order to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These four universal emotions lie on a spectrum where depending where the emotion of the robot lies, can affect its behavior and of its neighboring robots. The more negative the emotion is, the more individualistic it becomes in its decisions (anger, sadness or disgust). The more positive the robot is in its emotion, the more it will consider the group and global goals (joy). Each robot is able to recognize another robot′s emotion in the system based on their current state, using the AR2P (AR2P for its acronym in Spanish: Algoritmo Recursivo de Reconocimiento de Patrones) recognition algorithm. In this way, it can use this information of the emotions to decide with whom collaborate. Specifically, the paper addresses emotions' influence on the behavior of the system, at the individual and collective levels, and the emotions' effects on the emergent behaviors of the multi-robot system. The paper explores the emerging behavior in two multi-robot scenarios; nectar harvesting and object transportation. The results show that the emotions are important to the emergent behavior in a multi-robot system. [ABSTRACT FROM AUTHOR]

Online discussion communities play an important role in the development of relationships and the transfer of knowledge within and across organizations. Their underlying technologies enhance these processes by providing infrastructures through which group-based communication can occur. Community administrators often make decisions about technologies with the goal of enhancing the user experience, but the impact of such decisions on how a community develops must also be considered. To shed light on this complex and under-researched phenomenon, we offer a model of key latent constructs influenced by technology choices and possible causal paths by which they have dynamic effects on communities. Two important community characteristics that can be impacted are community size (number of members) and community resilience (membership that is willing to remain involved with the community in spite of variability and change in the topics discussed). To model community development, we build on attraction–selection–attrition (ASA) theory, introducing two new concepts: participation costs (how much time and effort are required to engage with content provided in a community) and topic consistency cues (how strongly a community signals that topics that may appear in the future will be consistent with what it has hosted in the past). We use the proposed ASA theory of online communities (OCASA) to develop a simulation model of community size and resilience that affirms some conventional wisdom and also has novel and counterintuitive implications. Analysis of the model leads to testable new propositions about the causal paths by which technology choices affect the emergence of community size and community resilience, and associated implications for community sustainability. [ABSTRACT FROM AUTHOR]

Large organizations usually have difficulties in dealing with the exponential growth of information. Therefore, there is a high demand for innovative solutions to deal with such growth and to integrate such information. This paper proposes a new approach, called Emergent Ontologies (EOs), toward the generation of a single organizational ontology through which it becomes possible to browse all information of an organization. This proposal considers that, typically, an organization's information is distributed in peers, and that in each peer, this information could be represented through a different ontology. As each peer of an organization needs to exchange information, peer-to-peer mappings are created to bridge these ontologies. Based on these mappings, this paper proposes a set of heuristics, which are used to generate the EO. These heuristics have been incorporated into the OntoEmerge system, a prototype developed to facilitate the creation of an initial organization ontology. In order to evaluate such a system and the heuristics behind it, some experiments have been performed. A quantitative and qualitative analysis of these experiments is also presented in this paper. This approach presents encouraging results, and this fact can be considered as a starting point for the creation of organizational ontologies. [ABSTRACT FROM AUTHOR]

In this communication the relationship between emerging patterns generated by using CNN Universal Machine and patterns appearing in visual art masterpieces is discussed. The template based programming tools are outlined and a gallery of impressive examples is presented. The paper remarks the impressive role of Cellular Nonlinear Networks in covering the gap among art, science and information technology. [ABSTRACT FROM AUTHOR]

Complex emergent systems of many interacting components, including complex biological systems, have the potential to perform quantifiable functions. Accordingly, we define "functional information," I(Ex), as a measure of system complexity. For a given system and function, x (e.g., a folded RNA sequence that binds to GTP), and degree of function, Ex (e.g., the RNA-GTP binding energy), I(Ex) = -log2[F(E2)], where F(Ex) is the fraction of all possible configurations of the system that possess a degree of function ⩾ Ex. Functional information, which we illustrate with letter sequences, artificial life, and biopolymers, thus represents the probability that an arbitrary configuration of a system will achieve a specific function to a specified degree. In each case we observe evidence for several distinct solutions with different maximum degrees of function, features that lead to steps in plots of information versus degree of function. [ABSTRACT FROM AUTHOR]

The article tries to reflect on some of the principles of the quantum physics, cognitive sciences and the emergent systems as rubrics to understand some of the transformations of cultural atmospheres of contemporary societies, and to new mass media as a result of the development of the new science and as an important actor in new cultural atmospheres via the media culture. [ABSTRACT FROM AUTHOR]

Many useful manmade systems in this world are extremely complex; a typical example is a large infrastructure. No design team ever invents these artifacts because they are too complex. These artifacts are made by combining existing elements (legacy) and by building new subsystems without explicit and comprehensive up-front coordination. To a large extent, these complex systems emerge and evolve. Experience shows that designers frequently fail to develop artifacts that, when combined, facilitate the emergence of effective and efficient systems. This paper formally elaborates the mechanism behind this phenomenon, and proposes principles for the design of components for emergent systems. The above insights were gained during the development of research prototypes for multi-agent manufacturing control; manufacturing systems are notorious for experiencing integration and interoperability problems as described above. Consequently, the design principles are discussed and illustrated on the design of multi-agent manufacturing control systems but also on work by others. Finally, although this paper mainly discusses the above in a generic fashion, and although the generic statements hold in an application-independent manner, it is important to appreciate that the applicability of the above insights and principles has significant limitations, which are discussed in the paper. [Copyright &y& Elsevier]