Memetic reproduction and protolanguage evolution

An investigation into the evolution of protolanguages is described, exploring the tension between faithful reproduction of memes to ensure consistency and hence comprehensibility in a language and the need to generate new memes to encourage the diversity observed in evolution of new languages and dialects. Agents were given different genetic strategies for interpreting, memorising and imitating memes. The process of memetic reproduction over 50 generations was simulated with competing fitness criteria to optimise consistency of reproduction on the one hand, and diversity in the meme set on the other, while simulating variations in the complexity of seed meme sets. The results showed that selection among interpreting and memorising strategies is more critical for faithful reproduction of memes; in contrast, there was little difference between imitation strategies that selected memes either randomly, or based on frequency and similarity of the memes received in previous exchanges. More complex meme sets produced more variation favouring diversity, while simpler meme sets favoured consistency. Shorter memes were more stable than longer memes over time, but increasing the number of seed memes had less effect. The implications for the evolution of memetic reproduction and protolanguages are discussed.     

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They should be able to recognize human beings and each other, and to engage in social interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction, behavior control and learning from environment. Learning processes described on Science of Behavior Analysis may lead to the development of promising methods and structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation, are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction.     

Structural Learning: Attraction and Conformity in Task-Oriented Groups

This study extends previous research that showed how informal social sanctions can backfire when members prefer friendship over enforcement of group norms. We use a type of neural network to model the coordination of informal social control in a small group of adaptive agents confronted with a social dilemma. This model incorporates two mechanisms of social influence, informal sanctions and imitation. Both mechanisms vary with the strength of the social tie between source and target. Previous research focused on the effects of social sanctions. Here, we demonstrate a curvilinear effect of imitation on compliance with prosocial norms. Moderate doses of imitation reduce the coordination complexity of self-organized collective action and help the network achieve satisfactory levels of cooperation. High doses, however, undermine the agent-based learning required to find cooperative solutions. Increasing group size also diminishes compliance due to increased complexity, with larger groups requiring more imitation to overcome the coordination problem.     

A unified term for directed and undirected motility in collective cell invasion

In this paper, we develop mathematical models for collective cell motility. Initially we develop a model using a linear diffusion–advection type equation and fit the parameters to data from cell motility assays. This approach is helpful in classifying the results of certain cell motility assay experiments. In particular, this model can determine degrees of directed versus undirected collective cell motility. Next we develop a model using a nonlinear diffusion term that is able to capture in a unified way directed and undirected collective cell motility. One goal of this work is to demonstrate that the forms of collective cell motility seen in the scratch assays and possibly other systems of interest need not reference external and more complicated migratory signals such as chemotaxis, but rather could be based on quorum sensing alone, collectively represented as density-dependent diffusivity. As an application we apply the nonlinear diffusion approach to a problem in tumor cell invasion, noting that neither chemotaxis or haptotaxis are present in the system under consideration in this article.     

A social approach for target localization: simulation and implementation in the marXbot robot

Foraging is a common benchmark problem in collective robotics in which a robot (the forager) explores a given environment while collecting items for further deposition at specific locations. A typical real-world application of foraging is garbage collection where robots collect garbage for further disposal in pre-defined locations. This work proposes a method to cooperatively perform the task of finding such locations: instead of using local or global localization strategies relying on pre-installed infrastructure, the proposed approach takes advantage of the knowledge gathered by a population about the localization of the targets. In our approach, robots communicate in an intrinsic way the estimation about how near they are from a target; these estimations are used by neighbour robots for estimating their proximity, and for guiding the navigation of the whole population when looking for these specific areas. We performed several tests in a simulator, and we validated our approach on a population of real robots. For the validation tests we used a mobile robot called marXbot. In both cases (i.e., simulation and implementation on real robots), we found that the proposed approach efficiently guides the robots towards the pre-specified targets while allowing the modulation of their speed.     

Neuro-fuzzy technique for navigation of multiple mobile robots

In this paper, navigation techniques for several mobile robots are investigated in a totally unknown environment. In the beginning, Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of sensors for measuring the distances of obstacles around it and an image sensor for detecting the bearing of the target. It is found that the FLC having Gaussian membership function is best suitable for navigation of multiple mobile robots. Then a hybrid neuro-fuzzy technique has been designed for the same problem. The neuro-fuzzy technique being used here comprises a neural network, which is acting as a pre processor for a fuzzy controller. The neural network considered for neuro-fuzzy technique is a multi-layer perceptron, with two hidden layers. These techniques have been demonstrated in simulation mode, which depicts that the robots are able to avoid obstacles and reach the targets efficiently. Amongst the techniques developed neuro-fuzzy technique is found to be most efficient for mobile robots navigation. Experimental verifications have been done with the simulation results to prove the authenticity of the developed neuro-fuzzy technique.     

Modelling a hormone-inspired controller for individual- and multi-modular robotic systems

For all living organisms, the ability to regulate internal homeostasis is a crucial feature. This ability to control variables around a set point is found frequently in the physiological networks of single cells and of higher organisms. Also, nutrient allocation and task selection in social insect colonies can be interpreted as homeostatic processes of a super-organism. And finally, behaviour can also represent such a control scheme. We show how a simple model of hormone regulation, inspired by simple biological organisms, can be used as a novel method to control the behaviour of autonomous robots. We demonstrate the formulation of such an artificial homeostatic hormone system (AHHS) by a set of linked difference equations and explain how the homeostatic control of behaviour is achieved by homeostatic control of the internal ‘hormonal’ state of the robot. The first task that we used to check the quality of our AHHS controllers was a very simple one, which is often a core functionality in controller programmes that are used in autonomous robots: obstacle avoidance. We demonstrate two implementations of such an AHHS controller that performs this task in differing levels of quality. Both controllers use the concept of homeostatic control of internal variables (hormones) and they extend this concept to also include the outside world of the robots into the controlling feedback loops: As they try to regulate internal hormone levels, they are forced to keep a homeostatic control of sensor values in a way that the desired goal ‘obstacle avoidance’ is achieved. Thus, the created behaviour is also a manifestation of the acts of homeostatic control. The controllers were evaluated using a stock-and-flow model that allowed sensitivity analysis and stability tests. Afterwards, we have also tested both controllers in a multi-agent simulation tool, which allowed us to predict the robots' behaviours in various habitats and group sizes. Finally, we demonstrate how this novel AHHS controller is suitable to control a multi-cellular robotic organism in an evolutionary robotics approach, which is used for self-programming in a gait-learning task. These examples shown in this article represent the first step in our research towards autonomous aggregation and coordination of robots to higher-level modular robotic organisms that consist of several joined autonomous robotic units. Finally, we plan to achieve such aggregation patterns and to control complex-shaped robotic organisms using AHHS controllers, as they are described here.     

Imitation,coordination and the emergence of Nash equilibrium

We consider a learning dynamic in which players imitate and better reply. Sufficient conditions are provided for Nash equilibrium play to emerge over time. The role of imitation in the learning dynamic is discussed through a series of examples. Most interestingly we demonstrate how imitation can ‘help’ the emergence of Nash equilibrium where ‘more rational’ methods do not.     

Nonlinear dynamics of unicycles in leader–follower formation

In this paper, a dynamical systems analysis is presented for characterizing the motion of a group of unicycles in leader–follower formation. The equilibrium formations are characterized along with their local stability analysis. It is demonstrated that with the variation in control gain, the collective dynamics might undergo Andronov–Hopf and Fold–Hopf bifurcations. The vigor of quasi-periodicity in the regime of Andronov–Hopf bifurcation and heteroclinic bursts between quasi-periodic and chaotic behavior in the regime of Fold–Hopf bifurcation increases with the number of unicycles. Numerical simulations also suggest the occurrence of global bifurcations involving the destruction of heteroclinic orbit.     

Advanced mathematics communication beyond modality of sight

This study illustrates how mathematical communication and learning are inherently multimodal and embodied; hence, sight-disabled students are also able to conceptualize visuospatial information and mathematical concepts through tactile and auditory activities. Adapting a perceptuomotor integration approach, the study shows that the lack of access to visual fields in an advanced mathematics course does not obstruct a blind student's ability to visualize, but transforms it. The goal of this study is not to compare the visually impaired student with non-visually impaired students to address the ‘differences’ in understanding; instead, I discuss the challenges that a blind student, named Anthony, has encountered and the ways that we tackled those problems. I also demonstrate how the proper and precisely crafted tactile materials empowered Anthony to learn mathematical functions.     

Global variation of outputs with respect to the variation of inputs in performance analysis; generalized RTS

Returns to scale (RTS) is an important topic in performance analysis, which helps managers to make decisions about the expansion or contraction of the operation of the DMU under assessment. But the RTS classification of DMUs gives only partial information, because it is a local notion. In this paper we extend the concept of RTS and we seek the precise relation between the proportional variation of outputs and the proportional variation of inputs. An approach is provided, which is able to determine this relation based on the parametric analysis and perturbation in linear programming.     

Emergence of coherent motion in aggregates of motile coupled maps

In this paper we study the emergence of coherence in collective motion described by a system of interacting motiles endowed with an inner, adaptative, steering mechanism. By means of a nonlinear parametric coupling, the system elements are able to swing along the route to chaos. Thereby, each motile can display different types of behavior, i.e. from ordered to fully erratic motion, accordingly with its surrounding conditions. The appearance of patterns of collective motion is shown to be related to the emergence of interparticle synchronization and the degree of coherence of motion is quantified by means of a graph representation. The effects related to the density of particles and to interparticle distances are explored. It is shown that the higher degrees of coherence and group cohesion are attained when the system elements display a combination of ordered and chaotic behaviors, which emerges from a collective self-organization process.     

A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units

This paper is concerned with the comparison of two popular non-parametric methodologies—data envelopment analysis and artificial neural networks—as tools for assessing performance. Data envelopment analysis has been established since 1978 as a superior alternative to traditional parametric methodologies, such as regression analysis, for assessing performance. Neural networks have recently been proposed as a method for assessing performance. In this paper, we use a simulated production technology of two inputs and one output for testing the success of the two methods for assessing efficiency. The two methods are also compared on their practical use as performance measurement tools on a set of bank branches, having multiple input and output criteria. The results demonstrate that, despite their differences, both methods offer a useful range of information regarding the assessment of performance.     

The uncertain probabilistic OWA distance operator and its application in group decision making

In this paper, we present the uncertain probabilistic ordered weighted averaging distance (UPOWAD) operator. Its main advantage is that it uses distance measures in a unified framework between the probability and the OWA operator that considers the degree of importance of each concept in the aggregation. Moreover, it is able to deal with uncertain environments represented in the form of interval numbers. We study some of its main properties and particular cases such as the uncertain probabilistic distance (UPD) and the uncertain OWA distance (UOWAD) operator. We end the paper by presenting an application to a group decision making problem regarding the selection of robots.     

What Norm of Assertion?

I argue that the debates over which norm constitutes assertion can be abandoned by challenging the three main motivations for a constitutive norm. The first motivation is the alleged analogy between language and games. The second motivation is the intuition that some assertions are worthy of criticism. The third is the discursive responsibilities incurred by asserting. I demonstrate that none of these offer good reasons to believe in a constitutive norm of assertion, as such a norm is understood in the literature. Others who have made similar arguments conclude that assertion does not exist at all—that there is no such thing as assertion. I disagree: we do not have to relinquish the category of assertion just because it is not normatively constituted. There are alternative ways to understand and individuate assertion that do not rely on a constitutive norm.     

Identifying critical financial networks of the DJIA: Toward a network‐based index

The purpose of this article is twofold. First, we introduce a novel definition of financial networks obtained from time series data from the stock market. Second, we demonstrate that these networks can be used as an index with the property to reflect critical states of the market, respectively, crashes sufficiently. Our work aims to advocate a network‐based analysis in the context of the stock market, because such a collective phenomenon can not only be economically described by networks but also analyzed as demonstrated in this article. © 2010 Wiley Periodicals, Inc. Complexity 16: 24–33, 2010     

Discrete operators of sampling type and approximation in ‐variation

The paper deals with approximation results with respect to the φ‐variation by means of a family of discrete operators for φ‐absolutely continuous functions. In particular, for the considered family of operators and for the error of approximation, we first obtain some estimates which are important in order to prove the main result of convergence in φ‐variation. The problem of the rate of approximation is also studied. The discrete operators that we consider are deeply connected to some problems of linear prediction from samples in the past, and therefore have important applications in several fields, such as, for example, in speech processing. Moreover such family of operators coincides, in a particular case, with the generalized sampling‐type series on a subset of the space of the φ‐absolutely continuous functions: therefore we are able to obtain a result of convergence in variation also for the generalized sampling‐type series. Some examples are also discussed.     

Collective Mathematical Understanding as Improvisation

This article explores the phenomenon of mathematical understanding, and offers a response to the question raised by Martin (2001) at the Annual Meeting of the Psychology of Mathematics Education Group (North American Chapter) about the possibility for and nature of collective mathematical understanding. In referring to collective mathematical understanding, we point to the kinds of learning and understanding we may see occurring when a group of learners work together on a piece of mathematics. We characterize the growth of collective mathematical understanding as a creative and emergent improvisational process and illustrate how this can be observed in action. In doing this, we demonstrate how a collective perspective on mathematical understanding can more fully explain its growth. We also discuss how considering the growth of mathematical understanding as a collective process has implications for classroom practice and in particular for the setting of mathematical tasks.     

How to collect balls moving in the Euclidean plane

In this paper, we study how to collect n balls moving with a fixed constant velocity in the Euclidean plane by k robots moving on straight track-lines through the origin. Since all the balls might not be caught by robots, differently from Moving-target TSP, we consider the following 3 problems in various situations: (i) deciding if k robots can collect all n balls; (ii) maximizing the number of the balls collected by k robots; (iii) minimizing the number of the robots to collect all n balls. The situations considered in this paper contain the cases in which track-lines are given (or not), and track-lines are identical (or not). For all problems and situations, we provide polynomial time algorithms or proofs of intractability, which clarify the tractability-intractability frontier in the ball collecting problems in the Euclidean plane.