A Theory of Emergence and Entropy in Systems of Systems

Systems of Systems (SOS) meet vital needs in our society by providing capabilities that are not possible by their discrete components or subsystems. Some SOS are engineered to produce predictable results, yet they can still display emergent behavior. These behaviors are often considered negative because they are not a function of the design. However, emergent behavior can also be serendipitous and produce unexpected positive results. The authors formalize a theory of emergence based on entropy. The theory has explanatory value for emergence as an ontological and phenomenological concept in systems of systems. © 2013 The Authors

A Macro-Level Order Metric for Self-Organizing Adaptive Systems

Analyzing how agent interactions affect macro-level self-organized behaviors can yield a deeper understanding of how complex adaptive systems work. The dynamic nature of complex systems makes it difficult to determine if, or when, a system has reached a state of equilibrium or is about to undergo a major transition reflecting the appearance of self-organized states. Using the notion of local neighborhood entropy, this paper presents a metric for evaluating the macro-level order of a system. The metric is tested in two dissimilar complex adaptive systems with self-organizing properties: An autonomous swarm searching for multiple dynamic targets and Conway\u27s Game of Life. In both domains, the proposed metric is able to graphically capture periods of increasing and decreasing self-organization (i.e. changes in macro-level order), equilibrium and points of criticality; displaying its general applicability in identifying these behaviors in complex adaptive systems. Abstract © 2018 IEEE

Learner course recommendation in e-learning based on swarm intelligence

Se dan unas recomendaciones en la enseñanza asistida por ordenador (e-learning) basada en la inteligencia colectiva.This paper analyses aspects about the recommendation process in distributedinformation systems. It extracts similarities and differences between recommendations in estores and the recommendations applied to an e-learning environment. It also explains the phenomena of self-organization and cooperative emergence in complex systems coupled with bio-inspired algorithms to improve knowledge discovery and association rules. Finally, the present recommendation is applied to e-learning by proposing recommendation by emergence in a multi.agent system architecture

Quantifying Spatiotemporal Stability by means of Entropy: Approach and Motivations

Several studies demonstrate that there are critical differences between real wireless networks and simulation models. This finding has permitted to extract spatial and temporal properties for links and to provide efficient methods as biased link sampling to guarantee efficient routing structure. Other works have focused on computing metrics to improve routing, specially the reuse of the measure of entropy. From there, rises the idea of formulating a new measure of entropy that gives an overview of the spatiotemporal stability of a link. This measure will rely on spatial and temporal properties of links and fed with the efficiency of biased link sampling.Comment: 10 pages, Telecom SudParis Research Repor

Observing productivity: what it might mean to be productive when viewed through the lens of Complexity Theory

The paper tries to explore options and preconditions for a theoretically thoroughly grounded conception of productivity that is able to account for its observer-dependency and thereby meets the needs of a dynamic and highly differentiated modern society. It does so in respect to insights from cybernetics and complexity theory, thereby taking up charges about the contradiction of economic productivity and the second law of Thermodynamics. In respect to epistemological consequences of contemporary levels of productivity, a seemingly paradoxical constraint is put forward: the constraint that productivity is conditioned on being observed as such, with the observer in its turn being conditioned on productivity. The assumption is that this paradoxical constitution helps to keep productivity adaptive to the changes it itself incites in economy

Learning Multi-Agent Navigation from Human Crowd Data

The task of safely steering agents amidst static and dynamic obstacles has many applications in robotics, graphics, and traffic engineering. While decentralized solutions are essential for scalability and robustness, achieving globally efficient motions for the entire system of agents is equally important. In a traditional decentralized setting, each agent relies on an underlying local planning algorithm that takes as input a preferred velocity and the current state of the agent\u27s neighborhood and then computes a new velocity for the next time-step that is collision-free and as close as possible to the preferred one. Typically, each agent promotes a goal-oriented preferred velocity, which can result in myopic behaviors as actions that are locally optimal for one agent is not necessarily optimal for the global system of agents. In this thesis, we explore a human-inspired approach for efficient multi-agent navigation that allows each agent to intelligently adapt its preferred velocity based on feedback from the environment. Using supervised learning, we investigate different egocentric representations of the local conditions that the agents face and train various deep neural network architectures on extensive collections of human trajectory datasets to learn corresponding life-like velocities. During simulation, we use the learned velocities as high-level, preferred velocities signals passed as input to the underlying local planning algorithm of the agents. We evaluate our proposed framework using two state-of-the-art local methods, the ORCA method, and the PowerLaw method. Qualitative and quantitative results on a range of scenarios show that adapting the preferred velocity results in more time- and energy-efficient navigation policies, allowing agents to reach their destinations faster as compared to agents simulated with vanilla ORCA and PowerLaw

satin: A Component Model for Mobile Self Organisation

We have recently witnessed a growing interest in self organising systems, both in research and in practice. These systems re-organise in response to new or changing conditions in the environment. The need for self organisation is often found in mobile applications; these applications are typically hosted in resource-constrained environments and may have to dynamically reorganise in response to changes of user needs, to heterogeneity and connectivity challenges, as well as to changes in the execution context and physical environment. We argue that physically mobile applications benefit from the use of self organisation primitives. We show that a component model that incorporates code mobility primitives assists in building self organising mobile systems. We present satin, a lightweight component model, which represents a mobile system as a set of interoperable local components. The model supports reconfiguration, by offering code migration services. We discuss an implementation of the satin middleware, based on the component model and evaluate our work by adapting existing open source software as satin components and by building and testing a system that manages the dynamic update of components on mobile hosts