Topological rewriting and the geometrization of programming

Giavitto, Jean-Louis; Spicher, Antoine

doi:10.1016/j.physd.2008.03.039

Cited by 30 publications

(26 citation statements)

References 17 publications

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“…Situation awareness enters the picture of spatial computing precisely when the situation of interest becomes tightly dependent on (mutual) position of computations/data/events present in the network, and when actions to accordingly take are again expressed in terms of spatial computations. Various approaches surveyed by Beal et al [2013] mostly depend on the language used to describe the spatial computation: functional approaches like Proto [Beal and Bachrach, 2006], rule-based like MGS [Giavitto and Spicher, 2008] and SAPERE [Zambonelli et al, 2015], Java-based like Tota [Mamei and Zambonelli, 2008], and others featuring logic programming and process algebraic ones. A uniform way to view at how they work is to consider the paradigmatic case of the gradient data structure described in the previous section, generalise over it, and consider the notion of computational field as a first-class one.…”

Section: Spatial Computationsmentioning

confidence: 99%

Spatial awareness in pervasive ecosystems

Dobson

Viroli

Fernandez-Marquez

et al. 2016

The Knowledge Engineering Review

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Pervasive systems are intended to make use of services and components that they encounter in their environment. Such systems are naturally spatial in that they can only be understood in terms of the ways in which components meet and interact in space. Rather than treating spatiality separately from system components, researchers are starting to develop computational models in which the entire structure of a pervasive system is modelled and constructed using an explicit spatial model, supporting multi-level spatial reasoning, and adapting autonomously to spatial interactions. In this paper we review current and emerging models of spatial computing for pervasive ecosystems, and highlight some of the trends that will guide future research.

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Section: Spatial Computationsmentioning

confidence: 99%

Spatial awareness in pervasive ecosystems

Dobson

Viroli

Fernandez-Marquez

et al. 2016

The Knowledge Engineering Review

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“…In doing so cells can be understood as creating their own co-ordinate system, allowing development to be influenced by self-generated positional information (PI) [56,57,42,44]. These biological phenomena have inspired computational models of artificial development [21,31,19], novel amorphus and spatial computation paradigms [39,1,25] and research collective robotics [53].…”

Section: Long-range Stimulimentioning

confidence: 99%

Wasps, Termites, and Waspmites: Distinguishing Competence from Performance in Collective Construction

2012

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We introduce a distinction between algorithm performance and algorithm competence and argue that bio-inpsired computing should characterise the former rather than the latter. To exemplify this, we explore and extend a bio-inspired algorithm for collective construction influenced by paper wasp behaviour. Despite being provably general in its competence we demonstrate limitations on the algorithm's performance. We explain these limitations, and extend the algorithm to include pheromone-mediated behaviour typical of termites. The resulting hybrid "waspmite" algorithm shares the generality of the original wasp algorithm, but exhibits improved peroformance and scalability.

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“…Topological collections have been introduced in [6] to describe arbitrary complex spatial structures that appear in biological systems [7] and other dynamical systems with a time varying structure [4], [8]. For the simplicity of the presentation, we consider in this paper that the neighborhood relationships between agents are represented by a partially labeled graph.…”

Section: Integrated Regulatory Network a Intuitive Presentationmentioning

confidence: 99%

State Space Exploration of Spatially Organized Populations of Agents

Dautriche

Giavitto

Klaudel

et al. 2011

2011 Fifth IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops

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Abstract-In this paper, we aim at modeling and analyzing the behavior of a spatial population of agents through an exploration of their state space. Agents are localized on a dynamic graph and they have internal states. They interact with an environment. The evolution of the agents and of the environment is specified by a set of rules. The framework is carefully designed to enable the construction of a global state space that can be automatically build and analyzed.The formalism, called IRNs for integrated regulatory networks, may be seen as an extension of logical regulatory networks (à la Thomas) developed in systems biology with spatial information and generalized to use arbitrary data values and update functions of this values. This thus allows to model systems with multiple agents that may be located on a varying spatial structure, may store and update local information, may depend on varying global information and may communicate in their neighborhood. A model of such a system can be defined as an IRN, and then analyzed using model-checking to asses its properties. This paper sketches the modeling framework and its semantics. We show how IRN may be used for the modeling of a population of simple agents, the automatic analysis of various reachability properties and the use of symmetries to reduce the size of the state space.

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Topological rewriting and the geometrization of programming

Cited by 30 publications

References 17 publications

Spatial awareness in pervasive ecosystems

Spatial awareness in pervasive ecosystems

Wasps, Termites, and Waspmites: Distinguishing Competence from Performance in Collective Construction

State Space Exploration of Spatially Organized Populations of Agents

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