Abstract: A new method for the optimal solutions is proposed. Originating from the continuous-time dynamics stability theory in the control field, the optimal solution is anticipated to be obtained in an asymptotically evolving way. By introducing a virtual dimension— —the variation time, a dynamic system that describes the variation motion is deduced from the Optimal Control Problem (OCP), and...

In this paper we analyze the evolution of solidarity relations between dissimilar actors by means of a cellular automaton framework. We assume that actors face two types of decisions in the course of an iterated game. First, actors’ solidarity decisions constitute mutual support relations between neighbors. Second, by migrating in a two dimensional world, actors select between potential solidar...

On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass...

It is well-known that Abstract State Machines (ASMs) can simulate “stepby-step” any type of machines (Turing machines, RAMs, etc.). We aim to overcome two facts: 1) simulation is not identification, 2) the ASMs simulating machines of some type do not constitute a natural class among all ASMs. We modify Gurevich’s notion of ASM to that of EMA (“Evolving MultiAlgebra”) by replacing the program (w...

Finding accurate reduced descriptions for large, complex, dynamically evolving networks is a crucial enabler to their simulation, analysis, and ultimately design. Here, we propose and illustrate a systematic and powerful approach to obtaining good collective coarse-grained observables-variables successfully summarizing the detailed state of such networks. Finding such variables can naturally le...

In this work we study the learning dynamics for agents playing games on networks. We propose a model of network formation in repeated games where players strategically adopt actions and connections simultaneously using a reinforcement learning scheme which is called Boltzmann-Q-learning. This adaptation scheme in the continuous time limit has a proven relation to the evolutionary game theory th...

In this paper we present an error analysis of an Eulerian finite element method for solving parabolic partial differential equations (PDEs) posed on evolving hypersurfaces in Rd, d = 2, 3. The method employs discontinuous piecewise linear in time–continuous piecewise linear in space finite elements and is based on a space-time weak formulation of a surface PDE problem. Trial and test surface fi...

Several pathogens use evolvability as a survival strategy against acquired immunity of the host. Despite their high variability in time, some of them exhibit quite low variability within the population at any given time, a somehow paradoxical behavior often called the evolving quasispecies. In this paper we introduce a simplified model of an evolving viral population in which the effects of the...

Considerable concerns exist over privacy on social networks, and huge debates persist about how to extend the artifacts users need to effectively protect their rights to privacy. While many interesting ideas have been proposed, no single approach appears to be comprehensive enough to be the front runner. In this paper, we propose a comprehensive and novel reference conceptual model for privacy ...