Bio-IR-M: A Multi-Paradigm Modelling for Bio-Inspired Multi-Agent Systems

Abstract

Nowadays bio-inspired approaches are widely used. Some of them became paradigms in many domains, such as Ant Colony Optimization (ACO) and Genetic Algorithms (GA). Despite the inherent challenges of surviving, in the natural world, biological organisms evolve, self-organize and self-repair with only local knowledge and without any centralized control. The analogy between biological systems and Multi-Agent Systems (MAS) is more than evident. In fact, every entity in real and natural systems is easily identified as an agent. Therefore, it will be more efficient to model them with agents. In a simulation context, MAS has been used to mimic behavioural, functional or structural features of biological systems. In a general context, bio-inspired systems are carried out with ad hoc design models or with a one target feature MAS model. Consequently, these works suffer from two weaknesses. The first is the use of dedicated models for restrictive purposes (such as academic projects). The second one is the lack of a design model. In this paper, our contribution aims to propose a generic multi-paradigms model for bio-inspired systems. This model is agent-based and will integrate different bio-inspired paradigms with respect of their concepts. We investigate to which extent is it possible to preserve the main characteristics of both natural and artificial systems. Therefore, we introduce the influence/reaction principle to deal with these bio-inspired multi-agent systems. Povzetek: Avtorji prispevka analizirajo podobnosti med biološkimi in multiagentnimi sistemi in predlagajo Bio-IR-M, integrirano shemo, ki zajema tako genetske algoritme kot npr. modele, temelječe na mravljah.

Characterisation of Intelligent Autonomous Agents Inspired by Biological Theory in Cognitive Environment

“…With regard to a bioroused plan, we will likely utilize a conventional model to bind together the variety of ideas explicit [8] to the considered biomotivated standards. A recapitulative reflection and examination can be performed on what was introduced in the past segments.…”

A Multi-agent approach for $\textit{in silico}$ simulations of micro-biological systems

“…As a special feature, these models allow to account for intracellular decision processes which are triggered by biochemical cell-cell or cell-matrix interactions [33]. In addition, they provide natural candidates for modeling the evolution and pattern formation of large multi-cellular systems [34] since they tie cellular properties to macroscopic behavior on the population level [35][36][37]. Other than that, the agent-based paradigm allows for a natural management of communication issues among individuals: specifically, we were able to model the cAMP pulses without using an instantaneous point to point messaging [38] nor diffusing waves [39] (as it was done in most of continuous reaction-diffusion models [22,26,28]).…”