A comparison of approaches to the evolution of homogeneous multi-robot teams

Quinn, Matt

doi:10.1109/cec.2001.934381

Cited by 28 publications

(70 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Mirolli et al [39] also compared partially heterogeneous teams. Quinn [29] evaluated individuals in different heterogeneous teams to create robust homogeneous teams.…”

Section: Individualmentioning

confidence: 99%

Genetic Team Composition and Level of Selection in the Evolution of Cooperation

Waibel

Keller

Floreano

2009

IEEE Trans. Evol. Computat.

109

View full text Add to dashboard Cite

Abstract-In cooperative multiagent systems, agents interact to solve tasks. Global dynamics of multiagent teams result from local agent interactions, and are complex and difficult to predict. Evolutionary computation has proven a promising approach to the design of such teams. The majority of current studies use teams composed of agents with identical control rules ("genetically homogeneous teams") and select behavior at the team level ("team-level selection"). Here we extend current approaches to include four combinations of genetic team composition and level of selection. We compare the performance of genetically homogeneous teams evolved with individual-level selection, genetically homogeneous teams evolved with team-level selection, genetically heterogeneous teams evolved with individual-level selection, and genetically heterogeneous teams evolved with team-level selection. We use a simulated foraging task to show that the optimal combination depends on the amount of cooperation required by the task. Accordingly, we distinguish between three types of cooperative tasks and suggest guidelines for the optimal choice of genetic team composition and level of selection.

show abstract

“…Mirolli et al [39] also compared partially heterogeneous teams. Quinn [29] evaluated individuals in different heterogeneous teams to create robust homogeneous teams.…”

Section: Individualmentioning

confidence: 99%

Genetic Team Composition and Level of Selection in the Evolution of Cooperation

Waibel

Keller

Floreano

2009

IEEE Trans. Evol. Computat.

109

View full text Add to dashboard Cite

show abstract

“…Results indicated that complementary and specialized roles emerged that enabled the robots to move in a coordinated manner. Similarly, Bull and Holland [10] and Quinn [42] compared methods for the evolution of team behavior in a team of simulated robots. A method that specified the ANN controller of each robot as one genotype (clonal) was compared to a method that specified the controller of each robot as a different genotype (aclonal).…”

Section: Neuro-evolutionmentioning

confidence: 99%

Collective neuro-evolution for evolving specialized sensor resolutions in a multi-rover task

2009

View full text Add to dashboard Cite

This article presents results from an evaluation of the collective neuro-evolution (CONE) controller design method. CONE solves collective behavior tasks, and increases task performance via facilitating emergent behavioral specialization. Emergent specialization is guided by genotype and behavioral specialization difference metrics that regulate genotype recombination. CONE is comparatively tested and evaluated with similar neuro-evolution methods in an extension of the multi-rover task, where behavioral specialization is known to benefit task performance. The task is for multiple simulated autonomous vehicles (rovers) to maximize the detection of points of interest (red rocks) in a virtual environment. Results indicate that CONE is appropriate for deriving sets of specialized rover behaviors that complement each other such that a higher task performance, comparative to related controller design methods, is attained in the multi-rover task.

show abstract

“…If the robots of a group do not share the same controller, it becomes less intuitive to define the criteria for estimating, from the observation of the collective behavior, the effectiveness of each control structure within a group and to compare different controllers associated with different groups [22]. Moreover, the homogeneity of control structures does not preclude the emergence of behavioral specializations.…”

Section: Introductionmentioning

confidence: 99%

Evolving Homogeneous Neurocontrollers for a Group of Heterogeneous Robots: Coordinated Motion, Cooperation, and Acoustic Communication

et al. 2008

View full text Add to dashboard Cite

This article describes a simulation model in which artificial evolution is used to design homogeneous control structures and adaptive communication protocols for a group of three autonomous simulated robots. The agents are required to cooperate in order to approach a light source while avoiding collisions. The robots are morphologically different: Two of them are equipped with infrared sensors, one with light sensors. Thus, the two morphologically identical robots should take care of obstacle avoidance; the other one should take care of phototaxis. Since all of the agents can emit and perceive sound, the group's coordination of actions is based on acoustic communication. The results of this study are a proof of concept: They show that dynamic artificial neural networks can be successfully synthesized by artificial evolution to design the neural mechanisms required to underpin the behavioral strategies and adaptive communication capabilities demanded by this task. Postevaluation analyses unveil operational aspects of the best evolved behavior. Our results suggest that the building blocks and the evolutionary machinery detailed in the article should be considered in future research work dealing with the design of homogeneous controllers for groups of heterogeneous cooperating and communicating robots.

show abstract

A comparison of approaches to the evolution of homogeneous multi-robot teams

Cited by 28 publications

References 6 publications

Genetic Team Composition and Level of Selection in the Evolution of Cooperation

Genetic Team Composition and Level of Selection in the Evolution of Cooperation

Collective neuro-evolution for evolving specialized sensor resolutions in a multi-rover task

Evolving Homogeneous Neurocontrollers for a Group of Heterogeneous Robots: Coordinated Motion, Cooperation, and Acoustic Communication

Contact Info

Product

Resources

About