N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

An Introduction to MultiAgent Systems.

I. the origin of species by means of natural selection

We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

Stochastic Differential Equations

It comes as something ofa surprise to reflect that the New York Review ofBooks is now over twenty years old. Even people of my generation (that is, old enough to remember the revolutionary 196os but not young enough to have taken a very exciting part in them) think of the paper as eternally youthful. In fact, it has gone through years of relatively quiet life, yet, as always in a competitive journalistic market, it is the controversies that attract the interest of the reader and to which the history (especially an admittedly impressionistic survey that tries to include something of the intellectual context in which a journal has operated) must give some attention. Not all the attacks which the New York Review has attracted, both early in its career and more recently, are worth more than a brief summary. What do we now make, for example, of Richard Kostelanetz's forthright accusation that 'The New York Review was from its origins destined to publicize Random House's (and especially [Jason] Epstein's) books and writers'?1 Well, simply that, even if the statistics bear out the charge (and Kostelanetz provides some suggestive evidence to support it, at least with respect to some early issues), there is nothing surprising in a market economy about a publisher trying to push his books through the pages of a journal edited by his friends. True, the New York Review has not had room to review more than around fifteen books in each issue and there could be a bias in the selection of

http://www.jstor.org/stable/pdfplus/3507774.pdf

The New York Review of Books

Swarm Robotics: A Formal Approach

Designing and analyzing self-organizing systems such as robotic swarms is a challenging task even though we have complete knowledge about the robot’s interior. It is difficult to determine the individual robot’s behavior based on the swarm behavior and vice versa due to the high number of agent–agent interactions. A step towards a solution of this problem is the development of appropriate models which accurately predict the swarm behavior based on a specified control algorithm. Such models would reduce the necessary number of time-consuming simulations and experiments during the design process of an algorithm. In this paper we propose a model with focus on an explicit representation of space because the effectiveness of many swarm robotic scenarios depends on spatial inhomogeneity. We use methods of statistical physics to address spatiality. Starting from a description of a single robot we derive an abstract model of swarm motion. The model is then extended to a generic model framework of communicating robots. In two examples we validate models against simulation results. Our experience shows that qualitative correctness is easily achieved, while quantitative correctness is disproportionately more difficult but still possible.

/pdf/a-framework-of-space-time-continuous-models-for-algorithm-2vp0xilq7g.pdf

A framework of space–time continuous models for algorithm design in swarm robotics

Achieving fast and accurate collective decisions with a large number of simple agents without relying on a central planning unit or on global communication is essential for developing complex collective behaviors. In this paper, we investigate the speed versus accuracy trade-off in collective decision-making in the context of a binary discrimination problem--i.e., how a swarm can collectively determine the best of two options. We describe a novel, fully distributed collective decision-making strategy that only requires agents with minimal capabilities and is faster than previous approaches. We evaluate our strategy experimentally, using a swarm of 100 Kilobots, and we study it theoretically, using both continuum and finite-size models. We find that the main factor affecting the speed versus accuracy trade-off of our strategy is the agents' neighborhood size--i.e., the number of agents with whom the current opinion of each agent is shared. The proposed strategy and the associated theoretical framework can be used to design swarms that take collective decisions at a given level of speed and/or accuracy.

/pdf/collective-decision-with-100-kilobots-speed-versus-accuracy-1rhpl7eoz0.pdf

Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems

In order to be effective, collective decision-making strategies need to be not only fast and accurate, but sufficiently general to be ported and reused across different problem domains. In this paper, we propose a novel problem scenario, collective perception, and use it to compare three different strategies: the DMMD, DMVD, and DC strategies. The robots are required to explore their environment, estimate the frequency of certain features, and collectively perceive which feature is the most frequent. We implemented the collective perception scenario in a swarm robotics system composed of 20 e-pucks and performed robot experiments with all considered strategies. Additionally, we also deepened our study by means of physics-based simulations. The results of our performance comparison in the collective perception scenario are in agreement with previous results for a different problem domain and support the generality of the considered strategies.

Collective Perception of Environmental Features in a Robot Swarm

Collective decision making in self-organized systems is challenging because it relies on local perception and local communication. Globally defined qualities such as consensus time and decision accuracy are both difficult to predict and difficult to guarantee. We present the weighted voter model which implements a self-organized collective decision making process. We provide an ODE model, a master equation model (numerically solved by the Gillespie algorithm), and agent-based simulations of the proposed decision-making strategy. This set of models enables us to investigate the system behavior in the thermodynamic limit and to investigate finite-size effects due to random fluctuations. Based on our results, we give minimum requirements to guarantee consensus on the optimal decision, a minimum swarm size to guarantee a certain accuracy, and we show that the proposed approach scales with system size and is robust to noise.

/pdf/self-organized-collective-decision-making-the-weighted-voter-2xj2i2qscu.pdf

Heiko Hamann

Papers

Swarm Robotics: A Formal Approach

A framework of space–time continuous models for algorithm design in swarm robotics

Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems

Collective Perception of Environmental Features in a Robot Swarm

Self-organized collective decision making: the weighted voter model