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Foundations for the Modeling and Simulation of Emergent Behavior Systems
Published in Larry B. Rainey, Mo Jamshidi, Engineering Emergence, 2018
Stigmergy can be loosely defined as communication through the environment. The simulation examined here is that of trail forming ants and demonstrates the traits of Type 3 emergence. In this case, a spatiotemporal structure in the form of an ant trail emerges rapidly from positive feedback as ants deposit pheromone when they are carrying food. It then is moderated with negative feedback as the chemical trail disperses and evaporates. About 10% of ants are carrying food since they quickly deposit it at the nest as shown in the top image of Figure 10.24. Subsequent images in Figure 10.24 show the effect of varying the amount of pheromone an ant has available to deposit on the patches. As pheromone is increased, ants have a greater likelihood of finding a path to the food. The intensity of constraint, IC for this experiment is simply,
Artificial Agentss
Published in Mariam Kiran, X-Machines for Agent-Based Modeling, 2017
A model is an approximate representation of a system, showing only basic functionalities or just parts being investigated. Various systems in nature are observed and adopted to create self-organizing systems. Insect colonies, cells and human societies are all examples of these using stigmergy or similar communication mechanisms to form patterns. Stigmergy is a communication mechanism insects use to interact with each other using the environment. For example, ants use pheromones to communicate pathways with other ants. Human societies use messages to communicate information to each other. Similarly, multi-agent systems use communication for coordination in a selforganizing system.
Ant Colony Optimization
Published in A Vasuki, Nature-Inspired Optimization Algorithms, 2020
Stigmergy is the technique of indirect communication between agents or insects by means of changes in local environment. It is a non-symbolic form of local communication where the agents make a change in the environment by leaving a trace that can be accessed by other agents to perform some action. This reinforces the activity by multiple agents, leading to good systematic outcomes. The communication is only within the neighborhood of the agent that is leaving a trace by releasing some chemical. This leads to self-organized behavior and collective intelligence among the group of agents that can accomplish complex tasks with simple collaboration and without much elaborate planning and control.
A swarm intelligent approach to data ferrying in sparse disconnected networks
Published in International Journal of Parallel, Emergent and Distributed Systems, 2021
Bradley Fraser, Robert Hunjet, Andrew Coyle
Stigmergy, introduced by Grassé [59], was described as the ‘stimulation of workers by the performance they have achieved’. Workers in this case were a particular caste in termite colonies under study. The term is now used to describe indirect communications between agents (biological or otherwise) facilitated by the external environment and is a common characteristic of swarming systems. For example, termites make use of stigmergy for pillar building inside nests [50]. Some species of ants also use pheromones while foraging to create shortest path routes between a food source and nests [50,51,60]. In both cases, pheromones act as the stigmergic variable. Physical, man-made pheromone systems have been implemented with robots equipped with instrumentation sensitive to alcohol [61]; however pheromones can exist digitally as well. For example, [62,63] argue the superiority of digital pheromones over potential fields and simulate their use for UAV swarm path planning. The swarm is dynamically attracted to its target while being repelled from threats. These algorithms were subsequently tested in physical experiments by the U.S. Army Space and Missile Defense Battlelab for surveillance and base protection [64]. A variety of pheromones were used including search, target tracking and no-go pheromones.
A framework for the analysis and synthesis of Swarm Intelligence algorithms
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2021
Dávila Patrícia Ferreira Cruz, Renato Dourado Maia, Leandro Nunes de Castro
Much research has shown the ability of ants to exploit rich food sources without losing the ability to explore the environment. In nature, many species of ants communicate through a chemical called pheromone as they search for food (Yew & Chung, 2015). When an ant finds a productive food source it returns to the nest depositing pheromone along the path, forming a trail. Other ants identify the pheromone trail and follow it with the purpose of exploring the food source found. These, in turn, when returning to the nest also deposit pheromone in the trail, reinforcing it. The pheromone evaporates with time and provides information about the quality and location of food sources found to other ants. The collective behaviour of building and following the pheromone trail is an example of stigmergy, and is the main inspiration for the development of optimisation algorithms based on ant colonies to solve complex problem (Bonabeau et al., 1999; Dorigo et al., 2006).
A stigmergetic method based on vector pheromone for target search with swarm robots
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2020
Qirong Tang, Fangchao Yu, Yuan Zhang, Jingtao Zhang
Stigmergy, a concept in biology derived from the behavioural mechanisms of social creatures, was originally proposed to explain the phenomenon that, the insect community cooperates in an organised manner, while the individual insect is independent of each other, and rarely interact with other individuals (Grassé, 1959). The explanation to this phenomenon by stigmergy is that insects interact indirectly: each insect affects the behaviour of other insects by indirect communication through the environment, such as nesting material, or chemical traces (Dorigo, Bonabeau, & Theraulaz, 2000). According to the different ways of changing the environment, the stigmergy mechanism is divided into two types: sematectonic and sign-based stigmergy (Hadeli, Valckenaers, Kollingbaum, & Brussel, 2004). In sematectonic stigmergy, the stimulation comes from the change of the physical properties of the environment, while in sign-based stigmergy, the sign is used to stimulate activities of individuals.