2,669 research outputs found
Engineering failure analysis and design optimisation with HiP-HOPS
The scale and complexity of computer-based safety critical systems, like those used in the transport and manufacturing industries, pose significant challenges for failure analysis. Over the last decade, research has focused on automating this task. In one approach, predictive models of system failure are constructed from the topology of the system and local component failure models using a process of composition. An alternative approach employs model-checking of state automata to study the effects of failure and verify system safety properties. In this paper, we discuss these two approaches to failure analysis. We then focus on Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS) - one of the more advanced compositional approaches - and discuss its capabilities for automatic synthesis of fault trees, combinatorial Failure Modes and Effects Analyses, and reliability versus cost optimisation of systems via application of automatic model transformations. We summarise these contributions and demonstrate the application of HiP-HOPS on a simplified fuel oil system for a ship engine. In light of this example, we discuss strengths and limitations of the method in relation to other state-of-the-art techniques. In particular, because HiP-HOPS is deductive in nature, relating system failures back to their causes, it is less prone to combinatorial explosion and can more readily be iterated. For this reason, it enables exhaustive assessment of combinations of failures and design optimisation using computationally expensive meta-heuristics. (C) 2010 Elsevier Ltd. All rights reserved
An Order-based Algorithm for Minimum Dominating Set with Application in Graph Mining
Dominating set is a set of vertices of a graph such that all other vertices
have a neighbour in the dominating set. We propose a new order-based randomised
local search (RLS) algorithm to solve minimum dominating set problem in
large graphs. Experimental evaluation is presented for multiple types of
problem instances. These instances include unit disk graphs, which represent a
model of wireless networks, random scale-free networks, as well as samples from
two social networks and real-world graphs studied in network science. Our
experiments indicate that RLS performs better than both a classical greedy
approximation algorithm and two metaheuristic algorithms based on ant colony
optimisation and local search. The order-based algorithm is able to find small
dominating sets for graphs with tens of thousands of vertices. In addition, we
propose a multi-start variant of RLS that is suitable for solving the
minimum weight dominating set problem. The application of RLS in graph
mining is also briefly demonstrated
Improving Search-Based Schematic Layout by Parameter Manipulation
This paper reports on a method to improve the automated layout of schematic diagrams
by widening the search space examined by the system. In search-based layout methods
there are typically a number of parameters that control the search algorithm which do
not affect the fitness function, but nevertheless have an impact on the final layout. We
explore how varying three parameters (grid spacing, the starting distance of allowed
node movement and the number of iterations) affects the resultant diagram in a hill-
climbing layout system. Using an iterative process, we produce diagram layouts that are
significantly better than those produced by ad-hoc parameter settings
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.Comment: Accepted by 14th International Workshop on Nature Inspired
Distributed Computing (NIDISC 2011), held in conjunction with the 25th
IEEE/ACM International Parallel and Distributed Processing Symposium (IPDPS
2011
Learning Bayesian network equivalence classes using ant colony optimisation
Bayesian networks have become an indispensable tool in the modelling of uncertain
knowledge. Conceptually, they consist of two parts: a directed acyclic graph called the
structure, and conditional probability distributions attached to each node known as the
parameters. As a result of their expressiveness, understandability and rigorous mathematical basis, Bayesian networks have become one of the first methods investigated,
when faced with an uncertain problem domain. However, a recurring problem persists
in specifying a Bayesian network. Both the structure and parameters can be difficult for
experts to conceive, especially if their knowledge is tacit.To counteract these problems, research has been ongoing, on learning both the structure
and parameters of Bayesian networks from data. Whilst there are simple methods for
learning the parameters, learning the structure has proved harder. Part ofthis stems from
the NP-hardness of the problem and the super-exponential space of possible structures.
To help solve this task, this thesis seeks to employ a relatively new technique, that has
had much success in tackling NP-hard problems. This technique is called ant colony
optimisation. Ant colony optimisation is a metaheuristic based on the behaviour of ants
acting together in a colony. It uses the stochastic activity of artificial ants to find good
solutions to combinatorial optimisation problems. In the current work, this method is
applied to the problem of searching through the space of equivalence classes of Bayesian
networks, in order to find a good match against a set of data. The system uses operators
that evaluate potential modifications to a current state. Each of the modifications is
scored and the results used to inform the search. In order to facilitate these steps, other
techniques are also devised, to speed up the learning process. The techniques includeThe techniques are tested by sampling data from gold standard networks and learning
structures from this sampled data. These structures are analysed using various goodnessof-fit measures to see how well the algorithms perform. The measures include structural
similarity metrics and Bayesian scoring metrics. The results are compared in depth
against systems that also use ant colony optimisation and other methods, including
evolutionary programming and greedy heuristics. Also, comparisons are made to well
known state-of-the-art algorithms and a study performed on a real-life data set. The
results show favourable performance compared to the other methods and on modelling
the real-life data
Artificial Intelligence Techniques Applied To Draughts
This thesis documents the work done to develop a draughts playing program that learns game strategies utilising various Artificial Intelligence (AI) techniques with the goal of being able to play draughts at a reasonably high skill level as a result of having played against itself without external guidance.
Context/Background:
AI is a fast evolving field of study. The motivation being programming computers to learn from experience should eventually eliminate the need for this detailed, time consuming, and costly programming effort currently required to program solutions to problems.
Aims:
The aim is to investigate a variety of AI techniques. The programās effectiveness will be assessed in both evaluating moves and playing a computationally intensive game.
Minimax based algorithms together with a basic scoring heuristic are used to evaluate enough of the game tree to pick high utility moves. Later the scoring heuristic is augmented using artificial intelligence techniques. As a result of this training āsmart scoring behaviourā the program is expected to learn how to best assign values to each of the squares on the draughts board enabling it to play at an adequately high skill level.
Method:
In this thesis a version of the board game Draughts is implemented in the Java programming language. Players were developed using a variety of techniques. These algorithms
were tested by comparing running times, number of nodes of the game tree searched and the utility of the moves picked. In addition an algorithm is developed to assign scores to
given board states using a genetic algorithm.
Results:
The project was a success for the most part permitting the creation of the game of draughts in the JAVA programming language. Four out of the five proposed move selection techniques were successfully tested in isolation. Finally the genetic algorithm demonstrated the ability to augment the scoring heuristic without the benefit of external guidance in the form of human experience
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