Ana B. Sendova-Franks

Bio: Ana B. Sendova-Franks is an academic researcher from University of Bristol. The author has contributed to research in topics: Temnothorax albipennis & Ant colony. The author has an hindex of 24, co-authored 71 publications receiving 2856 citations. Previous affiliations of Ana B. Sendova-Franks include University of the West of England & University of Bath.

The dynamics of collective sorting robot-like ants and ant-like robots

Abstract: A distributed sorting algorithm, inspired by how ant colonies sort their brood is presented for use by robot teams The robots move randomly, do not communicate have no hierarchical organisation, have no global representation can only perceive objects just in front of them, but can distinguish between objects of two or more types with a certain degree of error The probability that they pick up or put down an object is modulated as a function of how many of the same objects they have met in the recent past This generates a positive feed-back that is sufficient to coordinate the robots' activity, resulting in their sorting the objects into common clusters While less efficient than a hierarchically controlled sorting, this decentralised organisation offers the advantages of simplicity, flexibility and robustness

Brood sorting by ants: distributing the workload over the work-surface

Abstract: Leptothorax unifasciatus ant colonies occupy flat crevices in rocks in which their brood is kept in a single cluster. In artificial nests made from two glass plates sandwiched together, designed to mimic the general proportions of their nest sites in the field, such colonies arrange their brood in a distinct pattern. These patterns may influence the priority with which different brood are tended, and may therefore influence both the division of labour and colony demography. Different brood stages are arranged in concentric rings in a single cluster centred around the eggs and micro-larvae. Successively larger larvae are arranged in progressive bands away from the centre of the brood cluster. However, the largest and oldest brood items, the prepupae and pupae, are placed in an intermediate position between the largest and most peripheral larvae and the larvae of medium size. Dirichlet tessellations are used to analyze these patterns and show that the tile areas, the area closer to each item than its neighbours, allocated to each type of item increase with distance from the centre of the brood cluster. There is a significant positive correlation between such tile areas and the estimated metabolic rates of each type of brood item. The ants may be creating a “domain of care” around each brood item proportional to that item's needs. If nurse workers tend to move to the brood item whose tile they happen to be within when they have care to donate, they may apportion such care according to the needs of each type of brood. When colonies emigrate to new nests they rapidly recreate these characteristic brood patterns.

Radio-tagging reveals the roles of corpulence, experience and social information in ant decision making

Abstract: Ant colonies are factories within fortresses (Oster and Wilson 1978). They run on resources foraged from an outside world fraught with danger. On what basis do individual ants decide to leave the safety of the nest? We investigated the relative roles of social information (returning nestmates), individual experience and physiology (lipid stores/corpulence) in predicting which ants leave the nest and when. We monitored Temnothorax albipennis workers individually using passive radio-frequency identification technology, a novel procedure as applied to ants. This method allowed the matching of individual corpulence measurements to activity patterns of large numbers of individuals over several days. Social information and physiology are both good predictors of when an ant leaves the nest. Positive feedback from social information causes bouts of activity at the colony level. When certain social information is removed from the system by preventing ants returning, physiology best predicts which ants leave the nest and when. Individual experience is strongly related to physiology. A small number of lean individuals are responsible for most external trips. An individual’s nutrient status could be a useful cue in division of labour, especially when public information from other ants is unavailable.

Genetic Programming: On the Programming of Computers by Means of Natural Selection

Abstract: Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository

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

Modern Applied Statistics With S

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Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.