Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multi-agent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents. 1.

Abstract—The paper studies average consensus with random topologies (intermittent links) and noisy channels. Consensus with noise in the network links leads to the bias-variance dilemma—running consensus for long reduces the bias of the final average estimate but increases its variance. We present two different compromises to this tradeoff: the algorithm modifies conventional consensus by forcing the weights to satisfy a persistence condition (slowly decaying to zero;) and the algorithm where the weights are constant but consensus is run for a fixed number of iterations, then it is restarted and rerun for a total of runs, and at the end averages the final states of the runs (Monte Carlo averaging). We use controlled Markov processes and stochastic approximation arguments to prove almost sure convergence of to a finite consensus limit and compute explicitly the mean square error (mse) (variance) of the consensus limit. We show that represents the best of both worlds—zero bias and low variance—at the cost of a slow convergence rate; rescaling the weights balances the variance versus the rate of bias reduction (convergence rate). In contrast, , because of its constant weights, converges fast but presents a different bias-variance tradeoff. For the same number of iterations, shorter runs (smaller) lead to high bias but smaller variance (larger number of runs to average over.) For a static nonrandom network with Gaussian noise, we compute the optimal gain for to reach in the shortest number of iterations, with high probability (1), ()-consensus ( residual bias). Our results hold under fairly general assumptions on the random link failures and communication noise. Index Terms—Additive noise, consensus, sensor networks, stochastic approximation, random topology. I.

We have made a robot soccer model using LEGO Mindstorms robots, which was shown at RoboCup98 during the World Cup in soccer in France 1998. We developed the distributed behaviour-based approach in order to make a robust and high performing robot soccer demonstration. Indeed, our robots scored in an average of 75-80constructed a stadium out of LEGO pieces, including stadium light, rolling commercials, moving cameras projecting images to big screens, scoreboard and approximately 1500 small LEGO spectators who made the "Mexican wave" as known from soccer stadiums. These devices were controlled using the LEGO Dacta Control Lab system and the LEGO CodePilot system that allow programming motor reactions which can be based on sensor inputs. The wave of the LEGO spectators was made using the principle of emergent behaviour. There was no central control of the wave, but it emerges from the interaction between small units of spectators with a local feedback control. 1 Introduction Before the LE...

Rapid progress in the modeling of biological structures and simulation of their development has occurred over the last few years. It has been coupled with the visualization of simulation results, which has lead to a better understanding of morphogenesis and given rise to new procedural techniques for realistic image synthesis. This paper characterizes selected models of morphogenesis with a significant visual component. KEYWORDS: developmental models in biology, morphogenesis, simulation and visualization of biological phenomena, realistic image synthesis, reaction-diffusion, diffusionlimited growth, cellular automaton, L-system. How far mathematics will suffice to describe, and physics to explain, the fabric of the body, no man can forsee. D'Arcy Thompson, On Growth and Form [40]

We investigate ways in which an algorithm can improve its expected performance by finetuning itself automatically with respect to an arbitrary, unknown input distribution. We give such self-improving algorithms for sorting and computing Delaunay triangulations. The highlights of this work: (i) an algorithm to sort a list of numbers with optimal expected limiting complexity; and (ii) an algorithm to compute the Delaunay triangulation of a set of points with optimal expected limiting complexity. In both cases, the algorithm begins with a training phase during which it adjusts itself to the input distribution, followed by a stationary regime in which the algorithm settles to its optimized incarnation. 1

In this paper, we describe a swarm-based fuzzy logic control (FLC) mobile sensor network approach for collaboratively locating the hazardous contaminants in an unknown large-scale area. The mobile sensor network is composed of a collection of distributed nodes (robots), each of which has limited sensing, intelligence and communication capabilities. An ad-hoc wireless network is established among all nodes, and each node considers other nodes as extended sensors. By gathering other nodes ’ locations and measurement data, each node’s FLC can independently determine its next optimal deployment location. Simultaneously, by applying the three properties of the swarm behavior: separation, cohesion and alignment, the approach can ensure the sensor network attains wide regional coverage and dynamically stable connectivity. The simulation presented in this paper shows the swarm-based FLC mobile sensor network can achieve better performance and have higher fault tolerance in the event of partial node failures and sensor measurement errors. 1.

This paper reviews various architectures or working principles that enable animats - i.e. simulated animals or animal-like robots - to display adaptive behaviors and that might be useful within the context of computer animation.

ing which actions elicit a positive or negative reward from the environment (Figure 1) (Meyer, 1995, 1996, 1997). The control architecture of a given animat can be innate - because it is programmed or wired in by a human designer - or acquired - because it results from a learning process, which occurs at an individual or at a population level. 1 Subsumption architecture Many animats exhibit adaptive behaviors because they have been purposely programmed or cabled this way. For instance, several robots have been built by Brooks and his students at the Artificial Intelligence Laboratory of MIT, whose sizes, morphologies, and missions vary, but which are all controlled by the same subsumption architecture (Brooks, 1986). Essentially, this architecture consists in superimposing layers of networks of finite-state machines, augmented with various timers and registers. Each layer connects sensors to actuators and implements a c

Abstract. This paper presents a bio-inspired two-layer architecture that employs a context-dependent “structure control ” mechanism for autonomous robotic agents to achieve adaptive behaviors in dynamic physical-social environments. In this architecture, the bottom layer is an asymmetry mutual inhibition behavior network where different behaviors inhibit each other for behavior selection. The top “behavioral context ” layer dynamically sets the behavior selection context by changing the structure of the behavior network, thus modulating an agent’s overall behavior patterns according to different operating conditions. An example of dynamic team formation multi-robot system is presented. Simulation results and properties of this architecture are discussed. 1.

Abstract. This paper makes two contributions to increasing the engagement of users in virtual heritage environments by adding virtual living creatures. This work is carried out on the context of models of the Mayan cities of Palenque and Calakmul. Firstly, it proposes a virtual guide system. The guide navigates a virtual world and tells stories about the locations within it, bringing to them its personality and role, so that differently characterised guides will produce different stories. Secondly, it develops an architecture for adding autonomous animals to virtual heritage. It develops an affective component for such animal agents in order to increase the realism of their flocking behaviour and adds a mechanism for transmitting emotion between animals via virtual pheromones, modelled as particles in a free expansion gas. 1