Competitive algorithms for server problems
Journal of Algorithms
Distributed Anonymous Mobile Robots: Formation of Geometric Patterns
SIAM Journal on Computing
Terrain coverage with ant robots: a simulation study
Proceedings of the fifth international conference on Autonomous agents
Introduction to the Theory of Computation
Introduction to the Theory of Computation
Efficient and inefficient ant coverage methods
Annals of Mathematics and Artificial Intelligence
From Ants to A(ge)nts: A Special Issue on Ant-Robotics
Annals of Mathematics and Artificial Intelligence
Vertex-Ant-Walk – A robust method for efficient exploration of faulty graphs
Annals of Mathematics and Artificial Intelligence
Division of labor in a group of robots inspired by ants' foraging behavior
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
International Journal of Robotics Research
Cooperative Cleaners: A Study in Ant Robotics
International Journal of Robotics Research
Robots, insects and swarm intelligence
Artificial Intelligence Review
Covering a continuous domain by distributed, limited robots
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Swarm robotics: from sources of inspiration to domains of application
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 2 - Volume 2
On learning in agent-centered search
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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Investigations of multi-robot systems often make implicit assumptions concerning the computational capabilities of the robots. Despite the lack of explicit attention to the computational capabilities of robots, two computational classes of robots emerge as focal points of recent research: Robot Ants and robot Elephants. Ants have poor memory and communication capabilities, but are able to communicate using pheromones, in effect turning their work area into a shared memory. By comparison, elephants are computationally stronger, have large memory, and are equipped with strong sensing and communication capabilities. Unfortunately, not much is known about the relation between the capabilities of these models in terms of the tasks they can address. In this paper, we present formal models of both ants and elephants, and investigate if one dominates the other. We present two algorithms: AntEater, which allows elephant robots to execute ant algorithms; and ElephantGun, which converts elephant algorithms---specified as Turing machines---into ant algorithms. By exploring the computational capabilities of these algorithms, we reach interesting conclusions regarding the computational power of both models.