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Martyn Amos

Publications and source records attributed to Martyn Amos.

At least 19 recordsLinked to original sources

A dynamic state-based model of crowds

We consider the problem of categorizing and describing the dynamic properties and behaviours of crowds over time. Previous work has tended to focus on a relatively static "typology"-based approach, which does not account for the fact that crowds can change, often quite rapidly. Moreover, the labels attached to crowd behaviours are often subjective and/or value-laden. Here, we present an alternative approach, loosely based on the statechart formalism from computer science. This uses relatively "agnostic" labels, which means that we do not prescribe the behaviour of an individual, but provide a context within which an individual might behave. This naturally describes the time-series evolution of a crowd as "threads" of states, and allows for the dynamic handling of an arbitrary number of "sub-crowds".

cs.MA

Impact of baggage collection behaviour on aircraft evacuation

Recent reports of emergency aircraft evacuations have highlighted an increasing tendency amongst evacuees to ignore clear safety warnings and to collect and carry personal items of baggage during egress. However, relatively little work has so far been done on quantifying the impact of such behaviour on the evacuation process. In this paper, we report the results of validated simulation experiments (using the Boeing 777 wide-body aircraft), which confirm that even a relatively low level of baggage collection can significantly delay evacuation. Our platform provides one possible framework for the investigation of processes and mitigation tactics to minimise the impact of baggage collection behaviour in future.

cs.MA

A Turing Test for Crowds

The realism and believability of crowd simulations underpins computational studies of human collective behaviour, with implications for urban design, policing, security and many other areas. Realism concerns the closeness of the fit between a simulation and observed data, and believability concerns the human perception of plausibility. In this paper, we ask two questions, via a so-called "Turing Test" for crowds: (1) Can human observers distinguish between real and simulated crowds, and (2) Can human observers identify real crowds versus simulated crowds? In a study with student volunteers (n=384), we find convincing evidence that non-specialist individuals are able to reliably distinguish between real and simulated crowds. A rather more surprising result is that such individuals are overwhelmingly unable to identify real crowds. That is, they can tell real from simulated crowds, but are unable to say which is which. Our main conclusion is that (to the lay-person, at least) realistic crowds are not believable (and vice versa).

cs.MA

Solving Sudoku with Ant Colony Optimisation

In this paper we present a new Ant Colony Optimisation-based algorithm for Sudoku, which out-performs existing methods on large instances. Our method includes a novel anti-stagnation operator, which we call Best Value Evaporation.

cs.AI

Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms

The problem of parameterization is often central to the effective deployment of nature-inspired algorithms. However, finding the optimal set of parameter values for a combination of problem instance and solution method is highly challenging, and few concrete guidelines exist on how and when such tuning may be performed. Previous work tends to either focus on a specific algorithm or use benchmark problems, and both of these restrictions limit the applicability of any findings. Here, we examine a number of different algorithms, and study them in a "problem agnostic" fashion (i.e., one that is not tied to specific instances) by considering their performance on fitness landscapes with varying characteristics. Using this approach, we make a number of observations on which algorithms may (or may not) benefit from tuning, and in which specific circumstances.

cs.NE

Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms

A significant challenge in nature-inspired algorithmics is the identification of specific characteristics of problems that make them harder (or easier) to solve using specific methods. The hope is that, by identifying these characteristics, we may more easily predict which algorithms are best-suited to problems sharing certain features. Here, we approach this problem using fitness landscape analysis. Techniques already exist for measuring the "difficulty" of specific landscapes, but these are often designed solely with evolutionary algorithms in mind, and are generally specific to discrete optimisation. In this paper we develop an approach for comparing a wide range of continuous optimisation algorithms. Using a fitness landscape generation technique, we compare six different nature-inspired algorithms and identify which methods perform best on landscapes exhibiting specific features.

cs.NE

Removing Barriers to Interdisciplinary Research

A significant amount of high-impact contemporary scientific research occurs where biology, computer science, engineering and chemistry converge. Although programmes have been put in place to support such work, the complex dynamics of interdisciplinarity are still poorly understood. In this paper we interrogate the nature of interdisciplinary research and how we might measure its "success", identify potential barriers to its implementation, and suggest possible mechanisms for removing these impediments.

cs.GL

NanoInfoBio: A case-study in interdisciplinary research

A significant amount of high-impact contemporary scientific research occurs where biology, computer science, engineering and chemistry converge. Although programmes have been put in place to support such work, the complex dynamics of interdisciplinarity are still poorly understood. In this paper we highlight potential barriers to effective research across disciplines, and suggest, using a case study, possible mechanisms for removing these impediments.

cs.GL

Discrete modelling of bacterial conjugation dynamics

In bacterial populations, cells are able to cooperate in order to yield complex collective functionalities. Interest in population-level cellular behaviour is increasing, due to both our expanding knowledge of the underlying biological principles, and the growing range of possible applications for engineered microbial consortia. Researchers in the field of synthetic biology - the application of engineering principles to living systems - have, for example, recently shown how useful decision-making circuits may be distributed across a bacterial population. The ability of cells to interact through small signalling molecules (a mechanism known as it quorum sensing) is the basis for the majority of existing engineered systems. However, horizontal gene transfer (or conjugation) offers the possibility of cells exchanging messages (using DNA) that are much more information-rich. The potential of engineering this conjugation mechanism to suit specific goals will guide future developments in this area. Motivated by a lack of computational models for examining the specific dynamics of conjugation, we present a simulation framework for its further study. We present an agent-based model for conjugation dynamics, with realistic handling of physical forces. Our framework combines the management of intercellular interactions together with simulation of intracellular genetic networks, to provide a general-purpose platform. We validate our simulations against existing experimental data, and then demonstrate how the emergent mixing patterns of multi-strain populations can affect conjugation dynamics. Our model of conjugation, based on a probability distribution, may be easily tuned to correspond to the behaviour of different cell types. Simulation code and movies are available at http://code.google.com/p/discus/.

cs.MA

Zen Puzzle Garden is NP-complete

Zen Puzzle Garden (ZPG) is a one-player puzzle game. In this paper, we prove that deciding the solvability of ZPG is NP-complete.

cs.CC

Parallelization Strategies for Ant Colony Optimisation on GPUs

Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic for the solution of a wide variety of problems. As a population-based algorithm, its computation is intrinsically massively parallel, and it is there- fore theoretically well-suited for implementation on Graphics Processing Units (GPUs). The ACO algorithm comprises two main stages: Tour construction and Pheromone update. The former has been previously implemented on the GPU, using a task-based parallelism approach. However, up until now, the latter has always been implemented on the CPU. In this paper, we discuss several parallelisation strategies for both stages of the ACO algorithm on the GPU. We propose an alternative data-based parallelism scheme for Tour construction, which fits better on the GPU architecture. We also describe novel GPU programming strategies for the Pheromone update stage. Our results show a total speed-up exceeding 28x for the Tour construction stage, and 20x for Pheromone update, and suggest that ACO is a potentially fruitful area for future research in the GPU domain.

cs.DC

An early warning method for crush

Fatal crush conditions occur in crowds with tragic frequency. Event organisers and architects are often criticised for failing to consider the causes and implications of crush, but the reality is that the prediction and mitigation of such conditions offers a significant technical challenge. Full treatment of physical force within crowd simulations is precise but computationally expensive; the more common method of human interpretation of results is computationally "cheap" but subjective and time-consuming. In this paper we propose an alternative method for the analysis of crowd behaviour, which uses information theory to measure crowd disorder. We show how this technique may be easily incorporated into an existing simulation framework, and validate it against an historical event. Our results show that this method offers an effective and efficient route towards automatic detection of crush.

cs.MA

A population-based microbial oscillator

Genetic oscillators are a major theme of interest in the emerging field of synthetic biology. Until recently, most work has been carried out using intra-cellular oscillators, but this approach restricts the broader applicability of such systems. Motivated by a desire to develop large-scale, spatially-distributed cell-based computational systems, we present an initial design for a population-level oscillator which uses three different bacterial strains. Our system is based on the client-server model familiar to computer science, and uses quorum sensing for communication between nodes. We present the results of extensive in silico simulation tests, which confirm that our design is both feasible and robust.

q-bio.CB

Genetic algorithms and the art of Zen

In this paper we present a novel genetic algorithm (GA) solution to a simple yet challenging commercial puzzle game known as the Zen Puzzle Garden (ZPG). We describe the game in detail, before presenting a suitable encoding scheme and fitness function for candidate solutions. We then compare the performance of the genetic algorithm with that of the A* algorithm. Our results show that the GA is competitive with informed search in terms of solution quality, and significantly out-performs it in terms of computational resource requirements. We conclude with a brief discussion of the implications of our findings for game solving and other "real world" problems.

cs.NE

Ant Colony Algorithm for the Weighted Item Layout Optimization Problem

This paper discusses the problem of placing weighted items in a circular container in two-dimensional space. This problem is of great practical significance in various mechanical engineering domains, such as the design of communication satellites. Two constructive heuristics are proposed, one for packing circular items and the other for packing rectangular items. These work by first optimizing object placement order, and then optimizing object positioning. Based on these heuristics, an ant colony optimization (ACO) algorithm is described to search first for optimal positioning order, and then for the optimal layout. We describe the results of numerical experiments, in which we test two versions of our ACO algorithm alongside local search methods previously described in the literature. Our results show that the constructive heuristic-based ACO performs better than existing methods on larger problem instances.

cs.NE

Wave propagation in filamental cellular automata

Motivated by questions in biology and distributed computing, we investigate the behaviour of particular cellular automata, modelled as one-dimensional arrays of identical finite automata. We investigate what sort of self-stabilising cooperative behaviour these can induce in terms of waves of cellular state changes along a filament of cells. We discover what the minimum requirements are, in terms of numbers of states and the range of communication between automata, to observe this for individual filaments. We also discover that populations of growing filaments may have useful features that the individual filament does not have, and we give the results of numerical simulations.

cs.FL

Simulated annealing for weighted polygon packing

In this paper we present a new algorithm for a layout optimization problem: this concerns the placement of weighted polygons inside a circular container, the two objectives being to minimize imbalance of mass and to minimize the radius of the container. This problem carries real practical significance in industrial applications (such as the design of satellites), as well as being of significant theoretical interest. Previous work has dealt with circular or rectangular objects, but here we deal with the more realistic case where objects may be represented as polygons and the polygons are allowed to rotate. We present a solution based on simulated annealing and first test it on instances with known optima. Our results show that the algorithm obtains container radii that are close to optimal. We also compare our method with existing algorithms for the (special) rectangular case. Experimental results show that our approach out-performs these methods in terms of solution quality.

cs.CG

DNA Hash Pooling and its Applications

In this paper we describe a new technique for the comparison of populations of DNA strands. Comparison is vital to the study of ecological systems, at both the micro and macro scales. Existing methods make use of DNA sequencing and cloning, which can prove costly and time consuming, even with current sequencing techniques. Our overall objective is to address questions such as: (i) (Genome detection) Is a known genome sequence present, at least in part, in an environmental sample? (ii) (Sequence query) Is a specific fragment sequence present in a sample? (iii) (Similarity discovery) How similar in terms of sequence content are two unsequenced samples? We propose a method involving multiple filtering criteria that result in "pools" of DNA of high or very high purity. Because our method is similar in spirit to hashing in computer science, we call it DNA hash pooling. To illustrate this method, we describe protocols using pairs of restriction enzymes. The in silico empirical results we present reflect a sensitivity to experimental error. Our method will normally be performed as a filtering step prior to sequencing in order to reduce the amount of sequencing required (generally by a factor of 10 or more). Even as sequencing becomes cheaper, an order of magnitude remains important.

q-bio.BM