Cooperation and competition among stand-alone swarm agents increase collective fitness of the whole system. A principally new kind of collective systems is demonstrated by some bacteria and fungi, when they build symbiotic organisms. Symbiotic life forms emerge new functional and self-developmental capabilities, which allow better survival of swarm agents in different environments. In this pape...

In this paper we present an experimental setup to model the pheromone trail based foraging behaviour of ants using a special phosphorescent glowing paint. We have built two custom addons for the e-puck robot that allow for trail laying and following on the glowing floor, as well as a way for the robots to mimic the ants capability of using polarization patterns as a means of navigation. Using s...

Robot swarms are collections of simple robots cooperating without centralized control. Control algorithms for swarms are often inspired by decentralised problem-solving systems found in nature. In this paper we conduct a formal analysis of an algorithm inspired by the foraging behaviour of ants, where a swarm of flying vehicles searches for a target at some unknown location. We show how both ex...

A crucial issue in reinforcement learning applications is how to set meta-parameters, such as the learning rate and ”temperature” for exploration, to match the demands of the task and the environment. In this thesis, a method to adjust meta-parameters of reinforcement learning by using a real-number genetic algorithm is proposed. Simulations of foraging tasks show that appropriate settings of m...

The study of trace fossils, the fossilized remains of animal behavior, reveals interesting parallels with recent research in behavior-based robotics. This article reports robot simulations of the meandering foraging trails left by early invertebrates that demonstrate that such trails can be generated by mechanisms similar to those used for robot wall-following. We conclude with the suggestion t...

This paper discusses why traditional reinforcement learning methods, and algorithms applied to those models, result in poor performance in situated domains characterized by multiple goals, noisy state, and inconsistent reinforcement. We propose a methodology for designing reinforcement functions that take advantage of implicit domain knowledge in order to accelerate learning in such domains. Th...

In this paper, we study the application of an opinion dynamics model to swarm robotics. Our main result is that when opinions represent action choices, the opinion associated with the action that is the fastest to execute spreads in the population. Moreover, the spread of the best choice happens even when only a minority is initially advocating for it. The key elements involved in this process ...

Microscopic analysis is a standard approach in the study of robot behaviour. Typically, the approach comprises the analysis of a single (or sometimes a few) robotenvironment system(s) to reveal specific properties of robot behaviour. In contrast to microscopic analysis, macroscopic analysis focuses on averaged properties of systems. The advantage is that such a property is easier to generalise ...

Agents are autonomous entities which can sense from and act on environment. Agents could be programs, robot’s control systems or entire robots. We propose a general agent paradigm, in which learning, communication, knowledge, and other key factors are clearly divided and integrated. The agent paradigm tries to integrate learning, communication, and knowledge in a general way regardless of probl...

This paper discusses the challenges of learning to behave socially in the dynamic, noisy, situated and embodied mobile multi-robot domain. Using the methodology for synthesizing basis behaviors as a substrate for generating a large repertoire of higher-level group !Lnteractions, in this paper we describe how, given the substrate, greedy agents can learn social rules that benefit the group ',as ...