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This project develops a framework for computational general intelligence that can be used to build computational models, or world models, for machine intelligence and agent decision making. The framework proposes a novel definition of information and proposes virtual neurons as a method to enable longer term prediction and planning for agent decision making. Virtual neurons function as the most fundamental building blocks of an agent's cognitive model of the world and are the mediating particles of an agent's interaction with the proposed information field, and represent the set of an agent's available receptive fields that span multiple layers of abstraction. Layers of virtual neurons are computationally represented by graph structures to enable path planning, task navigation, and enable decision making and coordination with other agents through the creation of a mutual goal space. The framework bears particular applications to developing reinforcement learning agents that operate in the real world. Additionally, this framework enables a mapping between machine intelligence and natural intelligence, which serves as a tool for improving human-AI alignment.

This project develops a framework for computational general intelligence that can be used to build computational models of decision making for reinforcement learning agents that operate in the real world. The framework proposes a novel definition of information and proposes virtual neurons as a method to enable longer term prediction and planning for agent decision making. Virtual neurons function as the most fundamental building blocks of an agent's cognitive model of the world and are the mediating particles of an agent's interaction with the proposed information field, and represent the set of an agent's available receptive fields that span multiple layers of abstraction. Layers of virtual neurons are computationally represented by graph structures to enable path planning, task navigation, and enable decision making and coordination with other agents through the creation of a mutual goal space.

This article analyzes the emerging behavior of a multi-robot system managed by an architecture structured in three layers: the first provides local support to the robot, manages its processes of action, perception and communication, as well as its behavioral aspect, which considers the reactive, cognitive and social aspects of the robot. In addition, it introduces an affective component that influences its behavior and the way it relates to the environment and to the other individuals in the system, based on an emotional model that takes into account fourArticle history:Received 12 September 2018Accepted 08 November 2018A Gil, pertenece al Laboratorio de Prototipos en la Universidad Nacional Experimental del Táchira y a Tepuy R+D Group. Artificial Intelligence Software Development. Mérida, Venezuela (email: agil@unet.edu.ve)basic emotions. The second provides support to the collective processes of the system, based on the concept of emerging coordination. The latter is responsible for knowledge management and learning processes, both individually and collectively, in the system. In this article the metrics are defined to verify the emergency in the system, by means of the use of a method of verification of emergent behaviors based on Fuzzy Cognitive Maps.

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]

This article aims to present complexity as an intangible, non-linear, large, and unstable reality of the environment, and theoretically explained by their axes. It shows the evolution of the organisation from its beginning to the new organisational forms, and outlines what kinds of behaviours are manifested against changing environments. In view of this, a study is conducted on the skills of CEOs and how they handle the organisational dynamic scenarios. It concludes that both leadership and motivation are core competencies for self-regulation of complex environments within the organisation by preventing its reductionism, and at the same time it helps in promoting the professional growth of employees, establishing bonds of trust and commitment to the organisation.

Intelligent systems are composed of autonomous components that interact each others, with and through the environment, in order to give intelligent support for reaching specific objectives. In such kind of systems the environment is an active part of the system itself and provides input for runtime changing and adaptation. Modeling and representing systems like this is a hard task. In this paper we propose a biologically inspired approach that combined with the use of Agent-Based Modeling allows to create a means for analyzing emergent needs of the system at runtime and converting them into useful intelligent services to be provided. The experiment proposed for validating and illustrating the approach refers to the construction of smart university campus. [ABSTRACT FROM AUTHOR]

This work models Wikipedia and Free Software Development through a multiagent architecture for self-organizing and emergent systems called MASOES without mathematically representing the system. In that sense, each component, mechanism and process of MASOES is instanced at individual and collective levels by the observed phenomena at the modeled systems. Thus, this paper proposes a methodology to show how to model real systems using MASOES, in order to study their self-organizing and emergent properties and, later on, to facilitate the verification of these properties, mechanisms, components and social interactions for promoting collaborative work and sharing individual and collective knowledge in these systems. // RESUMEN Este trabajo modela el comportamiento de Wikipedia y el desarrollo de Software Libre, a través de una arquitectura multiagente para sistemas emergentes y auto-organizados llamada MASOES, sin especificar matem aticamente el sistema. En ese sentido, cada componente, mecanismo y proceso de MASOES se instancia a nivel individual y colectivo en cada uno de los sistemas modelados. Así, en este trabajo se propone una metodología para mostrar c omo modelar sistemas reales utilizando MASOES, con el fin de estudiar sus propiedades emergentes y auto-organizadas, y posteriormente, facilitar la verificación de estas propiedades, mecanismos, componentes e interacciones sociales para promover el trabajo colaborativo y el intercambio del conocimiento individual y colectivo en estos sistemas.

This work models Wikipedia and Free Software Development through a multiagent architecture for self-organizing and emergent systems called MASOES without mathematically representing the system. In that sense, each component, mechanism and process of MASOES is instanced at individual and collective levels by the observed phenomena at the modeled systems. Thus, this paper proposes a methodology to show how to model real systems using MASOES, in order to study their self-organizing and emergent properties and, later on, to facilitate the verification of these properties, mechanisms, components and social interactions for promoting collaborative work and sharing individual and collective knowledge in these systems.

An overview of one anthropological view of culture, including how it works and what it buys us, takes culture as a set of collective -- differentially distributed -- cognitive structures. Pragmatics is distinguished from semantics, and shown to seamlessly extend to non-linguistic knowledge. "Culture" is (flexibly and variably applied) shared differentially distributed pragmatic knowledge. Next come the role culture plays in regard to society and social living, and the role social groups play in culture. Our social universe is shown to be made up of a multiplicity of overlapping social groups. Prototype-extension is offered as the basis of the application of shared concepts to the experienced and imagined world. Types of cultural knowledge systems include: cultural modes of thought, cultural conceptual systems, and cultural models of action. The paper concludes with the approach's practical implications for analysis, including two concrete examples: Old and Middle English watercourses, and alternative Fanti kinship terminological systems. [ABSTRACT FROM AUTHOR]

Drawing upon five years of experience with an interdisciplinary initiative, colleagues in biology, literary studies, and physics offer a framework by which to understand the nature and value of interdisciplinary work. Effective interdisciplinary exchange depends on a dynamic and mutual interplay that challenges normally unexamined disciplinary assumptions. Effective interdisciplinary exchange can not only reinvigorate the disciplines but also engage them more effectively in a common intellectual enterprise, one that in turn is able to engage more effectively with a wide range of human concerns beyond the academy.

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]

Three principles guide natural pattern formation in both biological and non-living systems: (1) patterns form from interactions of numerous individual particles, or "agents," such as sand grains, molecules, cells or organisms; (2) assemblages of agents can adopt combinatorially large numbers of different configurations; (3) observed patterns emerge through the selection of highly functional configurations. These three principles apply to numerous natural processes, including the origin of life and its subsequent evolution. The formalism of "functional information," which relates the information content of a complex system to its degree of function, provides a quantitative approach to modeling the origin and evolution of patterning in living and nonliving systems. [ABSTRACT FROM AUTHOR]

Learning communities can emerge spontaneously when people find common learning goals and pursue projects and tasks together in pursuit of those goals. Bounded learning communities (BLCs) are groups that form within a structured teaching or training setting, typically a course. Unlike spontaneous communities, BLCs develop in direct response to guidance provided by an instructor, supported by a cumulative resource base. This article presents strategies that help learning communities develop within bounded frameworks, particularly online environments. Seven distinguishing features of learning communities are presented. When developing supports for BLCs, teachers should consider their developmental arc, from initial acquaintance and trust-building, through project work and skill development, and concluding with wind-down and dissolution of the community. Teachers contribute to BLCs by establishing a sense of teaching presence, including an atmosphere of trust and reciprocal concern. The article concludes with a discussion of assessment issues and the need for continuing research.

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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]

Learning communities can emerge spontaneously when people find common learning goals and pursue projects and tasks together in pursuit of those goals. Bounded learning communities (BLCs) are groups that form within a structured teaching or training setting, typically a course. Unlike spontaneous communities, BLCs develop in direct response to guidance provided by an instructor, supported by a cumulative resource base. This article presents strategies that help learning communities develop within bounded frameworks, particularly online environments. Seven distinguishing features of learning communities are presented. When developing supports for BLCs, teachers should consider their developmental arc, from initial acquaintance and trust-building, through project work and skill development, and concluding with wind-down and dissolution of the community. Teachers contribute to BLCs by establishing a sense of teaching presence, including an atmosphere of trust and reciprocal concern. The article concludes with a discussion of assessment issues and the need for continuing research. [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]