Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

An evolutionary approach to the identification of Cellular Automata based on partial observations

In this paper we consider the identification problem of Cellular Automata (CAs). The problem is defined and solved in the context of partial observations with time gaps of unknown length, i.e. pre-recorded, partial configurations of the system at certain, unknown time steps. A solution method based on a modified variant of a Genetic Algorithm (GA) is proposed and illustrated with brief experimental results.Comment: IEEE CEC 201

Identification of cellular automata based on incomplete observations with bounded time gaps

In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed

Spatio-temporal patterns act as computational mechanisms governing emergent behavior in robotic swarms

Our goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarm's inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm's emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Application of a cellular automata model to the metropolitan area of Barcelona

Cellular Automata (CA) models are among the most popular models for simulating land use change/growth in urban areas around the world and have experienced a significant development over the last twenty years. These models have their origins on the efforts of devising mathematical rules for biological systems and for evolution developed by mathematicians von Neumann and Stanislaw Ulam in the 1940s. Two main features made CA interesting for urban studies, to which they were introduced by Waldo Tobler in the early 1970s. First, their intrinsic spatiality, which is suitable for the simulation of a variety of geographic phenomena. Second, the possibility of simulating complex patterns of, for example, land use starting from a simple conceptual framework that includes the definition of a cell space, a neighborhood, and a finite set of transition rules applied to a finite set of cell states. These models have been developed for different urban contexts and are mainly based on the use of regular cells derived from remote sensing imagery. This is a simplification in the representation of urban areas: on the one hand, regular cells do not represent common urban form and, on the other hand, they do not held information of any type other than land use, obtained from automatic classification. These issues suggested the consideration of irregular cells linked to reliable information, that is, census blocks. Census blocks are drawn considering the form of urban areas and they are the most reliable source of data on a wide variety of subjects, being a natural choice for the design of CA cells. We present in this paper an application of a CA model to simulate urban change in the Metropolitan Area of Barcelona, in a prospective analysis of 20 years. The model uses irregular cells designed considering census blocks.We describe the main features of the model and the calibration process, as well as the simulation results. We also discuss some new features that are the core of our current research on CA.Peer Reviewe

Multi-agent simulation: new approaches to exploring space-time dynamics in GIS

As part of the long term quest to develop more disaggregate, temporally dynamic models of spatial behaviour, micro-simulation has evolved to the point where the actions of many individuals can be computed. These multi-agent systems/simulation(MAS) models are a consequence of much better micro data, more powerful and user-friendly computer environments often based on parallel processing, and the generally recognised need in spatial science for modelling temporal process. In this paper, we develop a series of multi-agent models which operate in cellular space.These demonstrate the well-known principle that local action can give rise to global pattern but also how such pattern emerges as the consequence of positive feedback and learned behaviour. We first summarise the way cellular representation is important in adding new process functionality to GIS, and the way this is effected through ideas from cellular automata (CA) modelling. We then outline the key ideas of multi-agent simulation and this sets the scene for three applications to problems involving the use of agents to explore geographic space. We first illustrate how agents can be programmed to search route networks, finding shortest routes in adhoc as well as structured ways equivalent to the operation of the Bellman-Dijkstra algorithm. We then demonstrate how the agent-based approach can be used to simulate the dynamics of water flow, implying that such models can be used to effectively model the evolution of river systems. Finally we show how agents can detect the geometric properties of space, generating powerful results that are notpossible using conventional geometry, and we illustrate these ideas by computing the visual fields or isovists associated with different viewpoints within the Tate Gallery.Our forays into MAS are all based on developing reactive agent models with minimal interaction and we conclude with suggestions for how these models might incorporate cognition, planning, and stronger positive feedbacks between agents

Application of a cellular automata model to the metropolitan area of Barcelona

Cellular Automata (CA) models are among the most popular models for simulating land use change/growth in urban areas around the world and have experienced a significant development over the last twenty years. These models have their origins on the efforts of devising mathematical rules for biological systems and for evolution developed by mathematicians von Neumann and Stanislaw Ulam in the 1940s. Two main features made CA interesting for urban studies, to which they were introduced by Waldo Tobler in the early 1970s. First, their intrinsic spatiality, which is suitable for the simulation of a variety of geographic phenomena. Second, the possibility of simulating complex patterns of, for example, land use starting from a simple conceptual framework that includes the definition of a cell space, a neighborhood, and a finite set of transition rules applied to a finite set of cell states. These models have been developed for different urban contexts and are mainly based on the use of regular cells derived from remote sensing imagery. This is a simplification in the representation of urban areas: on the one hand, regular cells do not represent common urban form and, on the other hand, they do not held information of any type other than land use, obtained from automatic classification. These issues suggested the consideration of irregular cells linked to reliable information, that is, census blocks. Census blocks are drawn considering the form of urban areas and they are the most reliable source of data on a wide variety of subjects, being a natural choice for the design of CA cells. We present in this paper an application of a CA model to simulate urban change in the Metropolitan Area of Barcelona, in a prospective analysis of 20 years. The model uses irregular cells designed considering census blocks.We describe the main features of the model and the calibration process, as well as the simulation results. We also discuss some new features that are the core of our current research on CA.Peer Reviewe

Application of a cellular automata model to the metropolitan area of Barcelona

Cellular Automata (CA) models are among the most popular models for simulating land use change/growth in urban areas around the world and have experienced a significant development over the last twenty years. These models have their origins on the efforts of devising mathematical rules for biological systems and for evolution developed by mathematicians von Neumann and Stanislaw Ulam in the 1940s. Two main features made CA interesting for urban studies, to which they were introduced by Waldo Tobler in the early 1970s. First, their intrinsic spatiality, which is suitable for the simulation of a variety of geographic phenomena. Second, the possibility of simulating complex patterns of, for example, land use starting from a simple conceptual framework that includes the definition of a cell space, a neighborhood, and a finite set of transition rules applied to a finite set of cell states. These models have been developed for different urban contexts and are mainly based on the use of regular cells derived from remote sensing imagery. This is a simplification in the representation of urban areas: on the one hand, regular cells do not represent common urban form and, on the other hand, they do not held information of any type other than land use, obtained from automatic classification. These issues suggested the consideration of irregular cells linked to reliable information, that is, census blocks. Census blocks are drawn considering the form of urban areas and they are the most reliable source of data on a wide variety of subjects, being a natural choice for the design of CA cells. We present in this paper an application of a CA model to simulate urban change in the Metropolitan Area of Barcelona, in a prospective analysis of 20 years. The model uses irregular cells designed considering census blocks. We describe the main features of the model and the calibration process, as well as the simulation results. We also discuss some new features that are the core of our current research on CA.Peer ReviewedPostprint (published version