Guiding Self-Organization in Systems of Cooperative Mobile Agents

Guiding Self-Organization in Systems of Cooperative Mobile Agents

Alejandro Rodríguez (University of Maryland, USA), Alexander Grushin (University of Maryland, USA) and James A. Reggia (University of Maryland, USA)
DOI: 10.4018/978-1-59904-996-0.ch015
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Drawing inspiration from social interactions in nature, swarm intelligence has presented a promising approach to the design of complex systems consisting of numerous, simple parts, to solve a wide variety of problems. Swarm intelligence systems involve highly parallel computations across space, based heavily on the emergence of global behavior through local interactions of components. This has a disadvantage as the desired behavior of a system becomes hard to predict or design. Here we describe how to provide greater control over swarm intelligence systems, and potentially more useful goal-oriented behavior, by introducing hierarchical controllers in the components. This allows each particle-like controller to extend its reactive behavior in a more goal-oriented style, while keeping the locality of the interactions. We present three systems designed using this approach: a competitive foraging system, a system for the collective transport and distribution of goods, and a self-assembly system capable of creating complex 3D structures. Our results show that it is possible to guide the self-organization process at different levels of the designated task, suggesting that self-organizing behavior may be extensible to support problem solving in various contexts.
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The term swarm intelligence, initially introduced by Beni, 1988 in the context of cellular robotics, refers to a collection of techniques inspired in part by the behavior of social insects, such as ants, bees, termites, etc., and of aggregations of animals, such as flocks, herds, schools, and even human groups and economic models (Bonabeau, 1999; Kennedy, 2001). These swarms possess the ability to present remarkably complex and “intelligent” behavior, despite the apparent lack of relative complexity in the individuals that form them. These behaviors can include cooperative synchronized hunting, coordinated raiding, migration, foraging, path finding, bridge construction, allocation of labor, and nest construction. Past discoveries (Deneubourg, 1989) have led investigators to the belief that such behaviors, although in part produced by the genetic and physiological structure of the individuals, are largely caused by the self-organization of the systems they form (Aron, 1990; Bonabeau, 1996). In other words, out of the direct or indirect local interactions between the individuals, the collective behavior emerges in a way that may have the appearance of being globally organized, although no centralized control or global communication actually exists. It is precisely this self-organization that artificial swarm intelligence systems try to achieve, by infusing the components, homogeneous or heterogeneous, of a system with simple rules. Swarm intelligence presents a novel and promising paradigm for the design and engineering of complex systems, increasingly found in many fields of engineering and science, where the number of elements and the nature of the interactions among them make it considerably difficult to model or understand the system’s behavior by traditional methods.

Several methodological approaches to swarm intelligence have been explored, but they often share a common feature: collections of simple entities (simulated birds, ants, vehicles, etc.) move autonomously through space, controlled by forces or interactions exerted locally upon each other, either directly or through the environment. These local interactions are often governed in a simple manner, via small sets of rules or short equations, and in some cases the sets of reactive agents used are best characterized as particle systems where each agent is viewed as a particle. This provides swarming systems with a sets of properties that includes scalability, fault tolerance, and perhaps more importantly, self-organization. Through this latter property, there is no need for a system to be controlled in a hierarchical fashion by one or more central components that determine the required behavior of the system. Instead, collective behavior emerges from the local interactions of all the components, and the global system behaves as a super-organism of loosely connected parts that react “intelligently” to the environment.

In our view, the self-organizing feature of swarm systems represents its main advantage and also its main disadvantage: the resulting global behavior is often hard to predict based solely on the local rules, and in some cases it can be hard to control the system, that is, to obtain a desired behavior by imposing local rules on its components. This not only can require prolonged, trial-and-error style tweaking and fine tuning, but even limits the kinds of problems that can be tackled by these essentially reactive systems.

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Table of Contents
Alfonso Araque Almendros
Ana B. Porto Pazos, Alejandro Pazos Sierra, Washington Buño Buceta
Chapter 1
Eduardo Malmierca, Nazareth P. Castellanos, Valeri A. Makarov, Angel Nuñez
It is well know the temporal structure of spike discharges is crucial to elicit different types of neuronal plasticity. Also, precise and... Sample PDF
Corticofugal Modulation of Tactile Responses of Neurons in the Spinal Trigeminal Nucleus: A Wavelet Coherence Study
Chapter 2
Didier Le Ray, Morgane Le Bon-Jego, Daniel Cattaert
Computational neuroscience has a lot to gain from invertebrate research. In this chapter focusing on the sensory-motor network that controls leg... Sample PDF
Neural Mechanisms of Leg Motor Control in Crayfish: Insights for Neurobiologically-Inspired Autonomous Systems
Chapter 3
Oscar Herreras, Julia Makarova, José Manuel Ibarz
Neurons send trains of action potentials to communicate each other. Different messages are issued according to varying inputs, but they can also mix... Sample PDF
Forward Dendritic Spikes: A Mechanism for Parallel Processing in Dendritic Subunits and Shifting Output Codes
Chapter 4
Gheorghe Paun, Mario J. Perez-Jimenez
This chapter is a quick survey of spiking neural P systems, a branch of membrane computing which was recently introduced with motivation from neural... Sample PDF
Spiking Neural P Systems: An Overview
Chapter 5
Juan Ramón Rabuñal Dopico, Javier Pereira Loureiro, Mónica Miguélez Rico
In this chapter, we state an evolution of the Recurrent ANN (RANN) to enforce the persistence of activations within the neurons to create activation... Sample PDF
Simulation of the Action Potential in the Neuron's Membrane in Artificial Neural Networks
Chapter 6
Ana B. Porto Pazos, Alberto Alvarellos González, Alejandro Pazos Sierra
The Artificial NeuroGlial Networks, which try to imitate the neuroglial brain networks, appeared in order to process the information by means of... Sample PDF
Recent Methodology in Connectionist Systems
Chapter 7
José A. Fernández-León, Gerardo G. Acosta, Miguel A. Mayosky, Oscar C. Ibáñez
This work is intended to give an overview of technologies, developed from an artificial intelligence standpoint, devised to face the different... Sample PDF
A Biologically Inspired Autonomous Robot Control Based on Behavioural Coordination in Evolutionary Robotics
Chapter 8
Enrique Mérida-Casermeiro, Domingo López-Rodríguez, J.M. Ortiz-de-Lazcano-Lobato
In this chapter, two important issues concerning associative memory by neural networks are studied: a new model of hebbian learning, as well as the... Sample PDF
An Approach to Artificial Concept Learning Based on Human Concept Learning by Using Artificial Neural Networks
Chapter 9
Enrique Fernández-Blanco, Julian Dorado, Nieves Pedreira
The artificial embryogeny term overlaps all the models that try to adapt cellular properties into artificial models. This chapter presents a new... Sample PDF
Artificial Cell Systems Based in Gene Expression Protein Effects
Chapter 10
Computing vs. Genetics  (pages 165-181)
José M. Barreiro, Juan Pazos
This chapter first presents the interrelations between computing and genetics, which both are based on information and, particularly... Sample PDF
Computing vs. Genetics
Chapter 11
Iara Moema Oberg Vilela
This chapter discusses guidelines and models of Mind from Cognitive Sciences in order to generate an integrated architecture for an artificial mind... Sample PDF
Artificial Mind for Virtual Characters
Chapter 12
Zhijun Yang, Felipe M.G. França
As an engine of almost all life phenomena, the motor information generated by the central nervous system (CNS) plays a critical role in the... Sample PDF
A General Rhythmic Pattern Generation Architecture for Legged Locomotion
Chapter 13
Marcos Gestal, José Manuel Vázquez Naya, Norberto Ezquerra
Traditionally, the Evolutionary Computation (EC) techniques, and more specifically the Genetic Algorithms (GAs), have proved to be efficient when... Sample PDF
Genetic Algorithms and Multimodal Search
Chapter 14
Jesús M. Miró, Alfonso Rodríguez-Patón
Synthetic biology and biomolecular computation are disciplines that fuse when it comes to designing and building information processing devices. In... Sample PDF
Biomolecular Computing Devices in Synthetic Biology
Chapter 15
Alejandro Rodríguez, Alexander Grushin, James A. Reggia
Drawing inspiration from social interactions in nature, swarm intelligence has presented a promising approach to the design of complex systems... Sample PDF
Guiding Self-Organization in Systems of Cooperative Mobile Agents
Chapter 16
Agostino Forestiero, Carlo Mastroianni, Fausto Pupo, Giandomenico Spezzano
This chapter proposes a bio-inspired approach for the construction of a self-organizing Grid information system. A dissemination protocol exploits... Sample PDF
Evaluating a Bio-Inspired Approach for the Design of a Grid Information System: The SO-Grid Portal
Chapter 17
Steven M. Corns, Daniel A. Ashlock, Kenneth Mark Bryden
This chapter presents Graph Based Evolutionary Algorithms. Graph Based Evolutionary Algorithms are a generic enhancement and diversity management... Sample PDF
Graph Based Evolutionary Algorithms
Chapter 18
Daniela Danciu
Neural networks—both natural and artificial, are characterized by two kinds of dynamics. The first one is concerned with what we would call... Sample PDF
Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria
Chapter 19
Jianhua Yang, Evor L. Hines, Ian Guymer, Daciana D. Iliescu, Mark S. Leeson, Gregory P. King, XuQuin Li
In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is... Sample PDF
A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers
Chapter 20
Malcolm J. Beynon, Kirsty Park
This chapter employs the fuzzy decision tree classification technique in a series of biological based application problems. With its employment in a... Sample PDF
The Exposition of Fuzzy Decision Trees and Their Application in Biology
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