Cooperative acceleration of task performance: Foraging behavior of interacting multi-robots system

We study the effectiveness of cooperative behavior in a society of interacting agents. After reviewing the problem and defining the concept of swarm intelligence, we examine collective behavior of many-body active clusters through a task to gather pucks in the field. In this study, we used a robot with a simple structure which has a driving system and the simplest interacting means; a light and some sensors. The effectiveness of group behavior was studied under various (homogeneous, localized) puck distributions with real experiment, simulation, and analysis. To evaluate the efficiency of group behavior, we examined the scaling relation between the task completion time and the number of robots, and the relation between the interaction period and the efficiency of group. We found that a cooperation between agents by a simple interaction is very efficient in enhancing the performance of the group compared with independent individuals.     

Lost in transactions: The case of the Boulogne s/mer fish market

Starting from some regularities of the Boulogne s/mer fish market, the model proposed here shows that in many circumstances the collective behavior may be ‘reasonable’ whereas the individuals may not be so. The properties which are empirically clear at the aggregate level are not necessarily derived from similar properties at the individual level. Thus, the macroscopic outcomes of the Boulogne s/mer fish market are not directly derived by any of the individual component involved, but are the self-organized outcomes of the agents’ interaction. The simple interaction of noisy and myopic agents leads the system to stabilize itself.     

A Locust-Inspired Model of Collective Marching on Rings

We study the collective motion of autonomous mobile agents in a ringlike environment. The agents’ dynamics are inspired by known laboratory experiments on the dynamics of locust swarms. In these experiments, locusts placed at arbitrary locations and initial orientations on a ring-shaped arena are observed to eventually all march in the same direction. In this work we ask whether, and how fast, a similar phenomenon occurs in a stochastic swarm of simple locust-inspired agents. The agents are randomly initiated as marching either clockwise or counterclockwise on a discretized, wide ring-shaped region, which we subdivide into k concentric tracks of length n. Collisions cause agents to change their direction of motion. To avoid this, agents may decide to switch tracks to merge with platoons of agents marching in their direction. We prove that such agents must eventually converge to a local consensus about their direction of motion, meaning that all agents on each narrow track must eventually march in the same direction. We give asymptotic bounds for the expected time it takes for such convergence or “stabilization” to occur, which depends on the number of agents, the length of the tracks, and the number of tracks. We show that when agents also have a small probability of “erratic”, random track-jumping behavior, a global consensus on the direction of motion across all tracks will eventually be reached. Finally, we verify our theoretical findings in numerical simulations.     

Dynamics of moving agents with memory

The problem of emergence of collective behavior in a system of two dimensional interacting moving agents is considered. The individual agent architecture is hybrid in the sense that the response is reactive but the agent is capable to choose between two different rules in a probabilistic way: one rule depends on the information from the environment and the other depends on the information imprinted in the agents memory. The dynamics of the system is studied through the characterization of the equilibrium state and the non-equilibrium properties of the system.     

Discontinuous and continuous transitions of collective behaviors in living systems

Living systems are full of astonishing diversity and complexity of life. Despite differences in the length scales and cognitive abilities of these systems, collective motion of large groups of individuals can emerge. It is of great importance to seek for the fundamental principles of collective motion, such as phase transitions and their natures. Via an eigen microstate approach, we have found a discontinuous transition of density and a continuous transition of velocity in the Vicsek models of collective motion, which are identified by the finite-size scaling form of order-parameter. At strong noise, living systems behave like gas. With the decrease of noise, the interactions between the particles of a living system become stronger and make them come closer. The living system experiences then a discontinuous gas-liquid like transition of density. The even stronger interactions at smaller noise make the velocity directions of the particles become ordered and there is a continuous phase transition of collective motion in addition.     

A model of coupled maps for economic dynamics

A deterministic system of coupled maps is proposed as a model for economic activity among interacting agents. The values of the maps represent the wealth of the agents. The dynamics of the system is controlled by two parameters. One parameter expresses the growth capacity of the agents and the other describes the local environmental pressure. For some values of the parameters, the system exhibits nontrivial collective behavior, characterized by macroscopic periodic oscillations of the average wealth of the system, emerging out of local chaos. The probability distribution of wealth in the asymptotic regime shows a power law behavior for some ranges of parameters.     

Dynamical quorum sensing and synchronization in collections of excitable and oscillatory catalytic particles

We present experimental studies of interacting excitable and oscillatory catalytic particles in well-stirred and spatially distributed systems. A number of distinct paths to synchronized oscillatory behavior are described. We present an example of a Kuramoto type transition in a well-stirred system with a collective rhythm emerging on increasing the number density of oscillatory particles. Groups of spatially distributed oscillatory particles become entrained to a common frequency by organizing centers. Quorum sensing type transitions are found in populations of globally and locally coupled excitable particles, with a sharp transition from steady state to fully synchronized behavior at a critical density or group size.     

Toy models and stylized realities

I discuss the role of toy models as theoretical tools for understanding complex systems of interacting agents. I review some concrete examples, in order to illustrate how this approach is able to capture, though in an admittedly stylized way, the interactions and non-linearities which are responsible for the rich phenomenology observed in reality. This allows one to interpret the system's behavior in terms of phase transitions and critical phenomena.     

A Bayesian Surprise Approach in Designing Cognitive Radar for Autonomous Driving

This article proposes the Bayesian surprise as the main methodology that drives the cognitive radar to estimate a target’s future state (i.e., velocity, distance) from noisy measurements and execute a decision to minimize the estimation error over time. The research aims to demonstrate whether the cognitive radar as an autonomous system can modify its internal model (i.e., waveform parameters) to gain consecutive informative measurements based on the Bayesian surprise. By assuming that the radar measurements are constructed from linear Gaussian state-space models, the paper applies Kalman filtering to perform state estimation for a simple vehicle-following scenario. According to the filter’s estimate, the sensor measures the contribution of prospective waveforms—which are available from the sensor profile library—to state estimation and selects the one that maximizes the expectation of Bayesian surprise. Numerous experiments examine the estimation performance of the proposed cognitive radar for single-target tracking in practical highway and urban driving environments. The robustness of the proposed method is compared to the state-of-the-art for various error measures. Results indicate that the Bayesian surprise outperforms its competitors with respect to the mean square relative error when one-step and multiple-step planning is considered.     

人工智能研究的物理学途径

近年来,人工智能研究的物理学途径已越来越受到人们的关注,这一途径的实现方式主要有两种：一是把已有的物理理论和方法直接用于研究计算系统,以达到对计算系统的性质和规律的认识,另一是依据某些计算系统与物理的相似性,把计算问题转换为对应的物理问题,再用物理方法建构有效的算法加以求解,文章着重介绍运用物理学途径研究计算系统的相变和解决多自主体系统的目标满足问题所取得的进展,并指出了这一途径的优点和应该注意的问题。     

Structure and dynamics of charged macromolecules: Minimal representation of biological systems

Structures and functions of various biological macromolecules at cellular levels are controlled by electrostatic, excluded-volume, macromolecular topological connectivity, and hydrodynamic forces. Some aspects of these challenging issues will be addressed. Specifically we will focus our discussions on (a) pattern recognition by macromolecules and complexation, (b) coupling between conformational transitions and phase transitions, (c) chromosomal condensation, (d) collective behavior of charged macromolecules in crowded environments, (e) coupled dynamics of macromolecular assemblies in charged solutions, and (f) polymer transport through pores. Even the simpler synthetic systems exhibit many puzzles which will be resolved using our theoretical formulation. In addition to exploring an understanding of biological processes, the context of fabrication of new synthetic materials will be remarked.     

Parametric collective phenomena during the propagation of polychromatic laser pulses in an optically dense resonant medium without population inversion

We experimentally and theoretically study the interaction of broadband polychromatic laser pulses with an optically dense resonant extended medium without population inversion. Experimental (probe field-pumping beam) measurements of the transmission and amplification spectra were carried out in the plasma of a positive neon glow-discharge column containing a large number of metastable atoms. The strong coupling in the field-matter system and the collective behavior of the atomic system in a resonant field were attributable to a high (～10¹² cm^?3) density of atoms at the lower (metastable) level of the optical transitions under consideration and to a relatively low intensity of the interacting laser beams. We observed a broadband weakening of the probe field in the absence of pumping and its strengthening in the line wings in the presence of a strong field. We develop a theoretical model for the parametric amplification of collective interactions in dense extended media based on the solution of the semiclassical Maxwell-Bloch equations for conditions under which the pumping field does not destroy the dipole interaction between atoms through probe-field photons.     

Minimal agent based model for financial markets I

We introduce a minimal agent based model for financial markets to understand the nature and self-organization of the stylized facts. The model is minimal in the sense that we try to identify the essential ingredients to reproduce the most important deviations of price time series from a random walk behavior. We focus on four essential ingredients: fundamentalist agents which tend to stabilize the market; chartist agents which induce destabilization; analysis of price behavior for the two strategies; herding behavior which governs the possibility of changing strategy. Bubbles and crashes correspond to situations dominated by chartists, while fundamentalists provide a long time stability (on average). The stylized facts are shown to correspond to an intermittent behavior which occurs only for a finite value of the number of agents N. Therefore they correspond to finite size effects which, however, can occur at different time scales. We propose a new mechanism for the self-organization of this state which is linked to the existence of a threshold for the agents to be active or not active. The feedback between price fluctuations and number of active agents represents a crucial element for this state of self-organized intermittency. The model can be easily generalized to consider more realistic variants.     

Cyclic dominance in adaptive networks

The Rock-Paper-Scissors (RPS) game is a paradigmatic model for cyclic dominance in biological systems. Here we consider this game in the social context of competition between opinions in a networked society. In our model, every agent has an opinion which is drawn from the three choices: rock, paper or scissors. In every timestep a link is selected randomly and the game is played between the nodes connected by the link. The loser either adopts the opinion of the winner or rewires the link. These rules define an adaptive network on which the agents’ opinions coevolve with the network topology of social contacts. We show analytically and numerically that nonequilibrium phase transitions occur as a function of the rewiring strength. The transitions separate four distinct phases which differ in the observed dynamics of opinions and topology. In particular, there is one phase where the population settles to an arbitrary consensus opinion. We present a detailed analysis of the corresponding transitions revealing an apparently paradoxical behavior. The system approaches consensus states where they are unstable, whereas other dynamics prevail when the consensus states are stable.     

Magnetic properties of the Ashkin–Teller model on a hexagonal nanotube

A new theoretical model for nanomagnets represented by the Ashkin–Teller model on a core-shell hexagonal nanotube is proposed. The Mean Field Theory from the Bogoliubov inequality is applied to study the magnetizations, phase boundaries and tricritical points. For a positive couplings system (ferromagnetic), first order and continuous phase transitions between the stable, metastable and unstable states are observed. For a negative core-shell coupling system, only continuous phase transitions between the stable and unstable states are observed. The phase diagram is presented to illustrate the different phases and transitions exhibited by the model.     

Phase diagram of a Schelling segregation model

The collective behavior in a variant of Schelling’s segregation model is characterized with methods borrowed from statistical physics, in a context where their relevance was not conspicuous. A measure of segregation based on cluster geometry is defined and several quantities analogous to those used to describe physical lattice models at equilibrium are introduced. This physical approach allows to distinguish quantitatively several regimes and to characterize the transitions between them, leading to the building of a phase diagram. Some of the transitions evoke empirical sudden ethnic turnovers. We also establish links with ‘spin-1’ models in physics. Our approach provides generic tools to analyze the dynamics of other socio-economic systems.     

Interactions between short-term and long-term cardiovascular control mechanisms

The cardiovascular system incorporates several controlling mechanisms acting as feedback loops over different time horizons. Because of their complex interrelationships, information-based methods such as autonomic information flow (AIF) functions promise to be useful in identifying normal and pathological behavior. Optimal adjustment between those controllers is necessary for healthy global behavior of the organism. We investigated the question as to whether there are typical relationships between short-term and long-term AIF by means of a meta-analysis of several of our own clinical studies of the mortality of patients with multiple organ dysfunction syndrome, heart failure, idiopathic dilated cardiomyopathy, and the length of stay in hospital after abdominal aorta surgery. We found a fundamental association of increased short-term randomness (decreased AIF) and decreased long-term randomness (increased AIF) due to pathology. A systems theoretic validation of this fundamental type of association was done by an appropriate mathematical model using a dissipative system with two feedback loops over different time horizons. The systematic simulation of an increasing collapse of the short feedback loop confirmed the inverse association between short-term and long-term information flow as a fundamental, system inherent type of readjustment that occurs under pathological conditions.