Owen Holland

Bio: Owen Holland is an academic researcher from University of Sussex. The author has contributed to research in topics: Robot & Robot control. The author has an hindex of 27, co-authored 104 publications receiving 4184 citations. Previous affiliations of Owen Holland include California Institute of Technology & University of Essex.

Ant-based load balancing in telecommunications networks

Abstract: This article describes a novel method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between nodes; nodes carrying an excess ...

Fom Local Actions to Global Tasks: Stigmergy and Collective Robotics

Abstract: This paper presents a series of experiments where a group of mobile robots gather 81 randomly distributed objects and cluster them into one pile. Coordination of the agents’ movements is achieved through stigmergy. This principle, originally developed for the description of termite building behaviour, allows indirect communication between agents through sensing and modification of the local environment which determines the agents’ behaviour. The efficiency of the work was measured for groups of one to five robots working together. Group size is a critical factor. The mean time to accomplish the task decreases for one, two, and three robots respectively, then increases again for groups of four and five agents, due to an exponential increase in the number of interactions between robots which are time consuming and may eventually result in the destruction of existing clusters. We compare our results with those reported by Deneubourg et al. (1990) where similar clusters are observed in ant colonies, generated by the probabilistic behaviour of workers.

Stigmergy, self-organization, and sorting in collective robotics

Abstract: Many structures built by social insects are the outcome of a process of self-organization, in which the repeated actions of the insects interact over time with the changing physical environment to produce a characteristic end state. A major mediating factor is stigmergy, the elicitation of specific environment-changing behaviors by the sensory effects of local environmental changes produced by previous behavior. A typical task involving stigmergic self-organization is brood sorting: Many ant species sort their brood so that items at similar stages of development are grouped together and separated from items at different stages of development. This article examines the operation of stigmergy and self-organization in a homogeneous group of physical robots, in the context of the task of clustering and sorting Frisbees of two different types. Using a behavioral rule set simpler than any yet proposed for ant sorting, and having no capacity for spatial orientation or memory, the robots are able to achieve effective clustering and sorting showing all the signs of self-organization. It is argued that the success of this demonstration is crucially dependent on the exploitation of real-world physics, and that the use of simulation alone to investigate stigmergy may fail to reveal its power as an evolutionary option for collective life forms.

Ant-like agents for load balancing in telecommunications networks

Abstract: This paper describes a novel method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between arbitrary nodes; nodes carrying an excess of traffic can become congested , causing calls to fail. In addition to calls, the network also supports a population of simple mobile agents with behaviours modelled on the trail laying abilities of ants. The agents move across the network between arbitrary pairs of nodes, selecting their path at each intermediate node according to the distribution of simulated pheromones at each node. As they move they deposit simulated pheromones as a function of their distance from their source node, and the congestion encountered on their journey. Calls between nodes are routed as a function of the pheromone distributions at each intermediate node. The performance of the network is measured by the proportion of calls which fail. The results are compared with those achieved by using fixed shortest-path routes, and also by using an alternative algorithmically-based type of mobile agent. The ant-based system is shown to drop fewer calls than the other methods, while exhibiting many attractive features of distributed control.

MARCUS: a two degree of freedom hand prosthesis with hierarchical grip control

Abstract: An important factor in the acceptance of a prosthesis is the ease with which the wearer can operate the device. Multiple degrees of freedom of a prosthesis are difficult to control independently and require a high level of concentration. If the control is arranged in a hierarchical manner and the lower levels' detailed control is performed by a microprocessor, it is possible to control a number of degrees with little direct intervention by the operator. A two degree of freedom hand has been developed to demonstrate this concept and has been made sufficiently compact to allow users to gain experience with it. >

Ant colony system: a cooperative learning approach to the traveling salesman problem

Abstract: This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Ant Colony Optimization

Abstract: Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications

Being There: Putting Brain, Body, and World Together Again

Abstract: From the Publisher: The old opposition of matter versus mind stubbornly persists in the way we study mind and brain. In treating cognition as problem solving, Andy Clark suggests, we may often abstract too far from the very body and world in which our brains evolved to guide us. Whereas the mental has been treated as a realm that is distinct from the body and the world, Clark forcefully attests that a key to understanding brains is to see them as controllers of embodied activity. From this paradigm shift he advances the construction of a cognitive science of the embodied mind.