Collaborative Foraging Using a new Pheromone and Behavioral Model

International audiencewe consider the problem of foraging with multiple agents, in which agents must collect disseminate resources in an unknown and complex environment. An efficient foraging should benefit from the presence of multiple agents, where cooperation between agents is a key issue for improvements. To do so, we propose a new distributed foraging mechanism. The aim is to adopt a new behavioral model regarding sources' affluence and pheromone's management. Simulations are done by considering agents as autonomous robots with goods transportation capacity, up to swarms that consist of 160 robots. Results demonstrate that the proposed model gives better results than c-marking agent model

Stigmergy in Web 2.0: a model for site dynamics

Building Web 2.0 sites does not necessarily ensure the success of the site. We aim to better understand what improves the success of a site by drawing insight from biologically inspired design patterns. Web 2.0 sites provide a mechanism for human interaction enabling powerful intercommunication between massive volumes of users. Early Web 2.0 site providers that were previously dominant are being succeeded by newer sites providing innovative social interaction mechanisms. Understanding what site traits contribute to this success drives research into Web sites mechanics using models to describe the associated social networking behaviour. Some of these models attempt to show how the volume of users provides a self-organising and self-contextualisation of content. One model describing coordinated environments is called stigmergy, a term originally describing coordinated insect behavior. This paper explores how exploiting stigmergy can provide a valuable mechanism for identifying and analysing online user behavior specifically when considering that user freedom of choice is restricted by the provided web site functionality. This will aid our building better collaborative Web sites improving the collaborative processes

Mean Field Behaviour of Collaborative Multi-Agent Foragers

Collaborative multi-agent robotic systems where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically-inspired robotics rely on simplifying agents and increasing their number to obtain more efficient solutions to such problems, drawing similarities with natural processes. In this work we focus on the problem of a biologically-inspired multi-agent system solving collaborative foraging. We show how mean field techniques can be used to re-formulate such a stochastic multi-agent problem into a deterministic autonomous system. This de-couples agent dynamics, enabling the computation of limit behaviours and the analysis of optimality guarantees. Furthermore, we analyse how having finite number of agents affects the performance when compared to the mean field limit and we discuss the implications of such limit approximations in this multi-agent system, which have impact on more general collaborative stochastic problems

Collective construction of numerical potential fields for the foraging problem

We consider the problem of deploying a team of agents (robots) for the foraging problem. In this problem agents have to collect disseminated resources in an unknown environment. They must therefore be endowed with exploration and path-planning abilities. This paper presents a reactive multiagent system that is able to simultaneously perform the two desired activities~ - exploration and path-planning - in unknown and complex environments. To develop this multiagent system, we have designed a distributed and asynchronous version of Barraquand's algorithm that builds an optimal Artificial Potential Field (APF). Our algorithm relies on agents with very limited perceptions that only mark their environment with integer values. The algorithm does not require any costly mechanism to be present in the environment to manage dynamic phenomena such as evaporation or propagation. We show that the APF built by our algorithm converges to optimal paths. The model is extended to deal with the multi-sources foraging problem. Simulations show that it is more time-efficient than the standard pheromone-based ant algorithm. Moreover, our approach is also able to address the problem in any kind of environment such as mazes

A Decentralized Ant Colony Foraging Model Using Only Stigmergic Communication

International audienceThis paper addresses the problem of foraging by a coordinated team of robots. This coordination is achieved by markers deposited by robots. In this paper, we present a novel decentralized behavioral model for multi robot foraging named cooperative c-marking agent model. In such model, each robot makes a decision according to the affluence of resource locations, either to spread information on a large scale in order to attract more agents or the opposite. Simulation results show that the proposed model outperforms the well-known c-marking agent model

Cooperative c-Marking Agents for the Foraging Problem

International audienceWe consider the problem of foraging with multiple agents, in which agents must collect disseminate resources in an unknown and complex environment. So far, reactive multi-agent systems have been proposed, where agents can perform simultaneously exploration and path planning. In this work, we aim to decrease exploration and foraging time by increasing the level of cooperation between agents; to this end, we present in this paper a novel pheromone modeling in which pheromone's propagation and evaporation are managed by agents. As in c-marking agents, our agents are provided with very limited perceptions, and they can mark their environment. Simulation results demonstrate that the proposed model outperforms the c-marking agent-based systems in a foraging mission

An ACO-Inspired, Probabilistic, Greedy Approach to the Drone Traveling Salesman Problem

In recent years, major companies have done research on using drones for parcel delivery. Research has shown that this can result in significant savings, which has led to the formulation of various truck and drone routing and scheduling optimization problems. This paper explains and analyzes a new approach to the Drone Traveling Salesman Problem (DTSP) based on ant colony optimization (ACO). The ACO-based approach has an acceptance policy that maximizes the usage of the drone. The results reveal that the pheromone causes the algorithm to converge quickly to the best solution. The algorithm performs comparably to the MIP model, CP model, and EA of Rich & Ham (2018), especially in instances with a larger number of stops