Cooperative co-evolutionary approaches have shown the ability to evolve specialised agent behaviours in multi-agent systems. A key aspect of applying cooperative co-evolution as an optimisation technique is the decomposition of the overall problem into smaller, interacting sub-components. Typically, problem decomposition occurs a priori, with a common approach being to decompose by agent, where each agent's behaviour corresponds to a separate evolving individual. Such approaches have inherent scalability limitations as the number of agents increases. In this work, we demonstrate a novel dynamic decomposition scheme based on the hierarchal subdivision of the environment, rather than by agent. In our approach, decomposition is performed simultaneously with the evolution of behaviour, starting with behaviour for the whole environment, followed by environment sub-division and behaviour specialisation. The approach was evaluated by evolving the behaviour of a set of cooperating combat unit agents in a real-time strategy game. The game was set in an environment composed of areas, each requiring a different behaviour for optimal gameplay. Using the behaviour evolved by our approach, the agents consistently defeated a numerically superior opponent and demonstrated location-appropriate behaviour. This approach may benefit other multi-agent systems where distinct environment sections call for different specialisation.

In this paper, an unsupervised approach incorporating variable bandwidth mean-shift and robust statistics is presented for generating fuzzy membership functions from data. The approach takes an attribute and automatically learns the number of representative functions from the underlying data distribution. Given a specific membership function, the approach also works out the associated parameters. The investigation here examines the application of approach using the triangular membership function. Results from partitioning of attributes confirm that the generated membership functions can better separate the underlying distributions when compared to a number of other techniques. Classification performance of fuzzy rule sets produced using four different methods of parameterizing the associated attributes is examined. We observed that the classifier constructed using the proposed method of generating membership function outperformed the 3 other classifiers that had used other methods of parameterizing the attributes. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

Design of level layouts typically involves the production of a set of levels which are different, yet display a consistent style based on the purpose of a particular level. In this paper, a new approach to the generation of unique level layouts, based on a target set of attributes, is presented. These attributes, which are learned automatically from an example layout, are used for the off-line evolution of a set of cellular automata rules. These rules can then be used for the real-time generation of level layouts that meet the target parameters. The approach is demonstrated on a set of maze-like level layouts. Results are presented to show the effect of various CA parameters and rule representation.

In this paper, an approach is presented for the detection of abnormal behaviours in the activities of people living alone in their homes. The proposed approach takes input from Kinect cameras and extracts a number of visual attributes that represent the occupant's location and orientation. A set of fuzzy logic parameters is first learnt from the training data. Next the proposed approach learns epochs of activities in each location and then generates models of normal behaviour patterns. Unusual behaviour is then detected in subsequent data by looking for patterns which differ from the learnt normal behaviours based on their time of occurrence, visual attributes, or duration. Experiments conducted showed the effectiveness of the proposed system.

The rapid growth of the entertainment industry has driven the requirement for more efficient development of computerized games. Importantly, the diversity of audiences that participate in playing games has called for the development of new technologies that allow games to address users with differing levels of skills and preferences. This study explored the application of dynamic difficulty adjustment in computerized 2D plat former games through procedural level generation and interactive evolutionary computation. Computerized agents were introduced into the evolutionary cycle to play game levels in the player's stead. Additionally, agent behavior was based on the player's game-play characteristics, which has provided a more practical means for evaluating and evolving game levels tailored to a particular player's skills.

The Nearest Shrunken Centroid (NSC) method, with Prediction Analysis for Microarrays being its most well known implementation, has been widely used as a classification method for high dimensional biomedical data. A threshold value must also be provided in this method as input and normally, this is selected manually on a “trial and error” basis by executing the NSC method many times using a number of predetermined shrinkage threshold values. The optimal value is then obtained by minimizing the cross-validated error on the training data. This process can be time-consuming and the optimal threshold value may be limited by the granularity of the predetermined values. In this paper, an approach incorporating the NSC method and a multi-objective evolutionary algorithm, Non-dominated Sorting Algorithm 2, is proposed for obtaining the optimal shrinkage threshold value automatically. The NSC method acts as the fitness evaluator in the evolutionary process. Multiple objectives can be incorporated for determining the threshold values and a number of optimal solutions are obtained, each on the basis of tradeoffs between the objectives. By providing multiple potential solutions, it allows biomedical experts to better explore the joint behaviors of features in their data. The proposed approach also overcomes limitations normally associated with single objective approaches; a single optimum and the need to determine weightings associated with various objective functions in an aggregated objective function. The proposed approach was evaluated using the Alzheimer's Disease, Colon and Leukemia cancer dataset.

In this paper, a hybrid approach incorporating the Nearest Shrunken Centroid (NSC) and Genetic Algorithm (GA) is proposed to automatically search for an optimal range of shrinkage threshold values for the NSC to improve feature selection and classification accuracy for high dimensional data. The selection of a threshold value is crucial as it is the key factor in the NSC to find significant relative differences between the overall centroid and the class centroid. However, selecting this threshold value via “trial and error” in empirical approaches can be time-consuming and imprecise. In the proposed NSC-GA approach, shrinkage threshold values for the NSC are encoded as genes in chromosomes that are evaluated using a fitness measure obtained from the classifier in the NSC. The proposed approach automatically searches for the optimal threshold for the NSC by utilizing GA. The proposed approach was evaluated using a number of data sets; Alzheimer's disease, Colon and Leukemia cancer datasets. Experimental results indicated that the proposed approach finds the optimal range of shrinkage thresholds for each dataset, subsequently leading to a higher classification result and involving a smaller number of features when compared to previous studies.

The increased number of security threats against the Internet has made communications more vulnerable to attacks. Despite much research and improvement in network security, the number of denial of service (DoS) attacks has rapidly grown in frequency, severity, and sophistication in recent years. Thus, serious attention needs to be paid to network security. However, to create a secure network that can stay ahead of all threats, detection and response features are real challenges. In this paper, we look at the the interaction between the attacker and the defender in a Red Team/Blue Team exercise. We also propose a quantitative decision framework which is able to provide optimal solutions to defend against well-organized and sophisticated attacks. A large number of possible scenarios for testing of DoS defences will be examined through this framework in order to help experts to improve decisions regarding optimal solutions to defend against DoS threats.

A new approach is proposed in this paper for identification of biomarkers and classification on Alzheimer's disease data by employing a rough-fuzzy hybrid approach called ARFIS (a framework for Adaptive TS-type Rough-Fuzzy Inference Systems). In this approach, the entropy-based discretization technique is employed first on the training data to generate clusters for each attribute with respect to the output information. The rough set-based feature reduction method is then utilized to reduce the number of features in a decision table obtained using the cluster information. Another rough set-based approach is employed for the generation of decision rules. After the construction and the evaluation phases of the proposed rough-fuzzy hybrid system, the classification is carried out on the testing set of the given data. The experimental results showed that the proposed approach achieved compatible classification results on Alzheimer's disease data compared to results from other existing approaches in the literature. It can be concluded that the proposed rough-fuzzy hybrid approach is a novel approach in predictive data mining in clinical medicine in terms of utilizing 1) rough set-based approaches for feature reduction and rule generation, 2) a hybrid fuzzy system for pattern classification, and revealing 3) rules for prediction of diagnostic results.

Software selection involves the assimilation of information and results for each candidate to enable a comparison for decisions to be made. The processes and tools developed assist with software selection to enhance quality, documentation and repeatability. The CdCE process aims to retain and document the information used in selection to assist decisions and to document them for reference as the system evolves. This paper describes the CdCE process and our approach to assist the shortlisting of candidates through a suite of classifiers. The application of the suite is illustrated using a selection and evaluation case study. Applying this approach helps retain the multidimensional nature of the selection process and enhances user awareness in the decision making process.

Protocol conformance testing generally involves checking whether the protocol under test conforms to the given specification. An important issue in protocol conformance testing is the generation of test sequences in an efficient and effective way that achieves the required fault detection coverage. This paper proposed an approach for finding the shorter test sequences for protocol conformance testing based on the Wp method. The approach provides a technique for transforming the problem of the test sequence generation from a given FSM into one of finding the shortest path in the asymmetric travelling salesman problem by using one of the many existing meta-heuristic algorithms for addressing TSP. The approach addresses the issue of reformulation of Software Engineering problems as search-based problems in Search-based Software Engineering. The paper also shows that the resulting test sequences will maintain the same fault detection capability as those of the Wp method.

Although attention has been given to the use of UML (Unified Modelling Language) activity diagrams in the generation of scenarios, thin-threads and test-cases, the processes described in the literature rely heavily on manual intervention either in the information extraction process or in the process of transforming them to an alternate structure. This paper introduces an approach that capture, store and output usage scenarios derived automatically from UML activity diagrams.

Conformance testing using test sequences is used to ensure that a protocol implementation conforms to its specification. A commonly used technique to generate test sequences for specifications described by the finite state machines is the Wp-method with the reset technique, which frequently results in long test sequences. In this paper, we propose a bipartite graph approach to generate optimal test sequences for protocol conformance testing. Our approach significantly reduces the length of the test sequences required for conformance testing while maintaining the same fault detection capability.

Test case generation is one of the most important issues in software testing research and industrial practice. Test scenarios are frequently used to derive test cases for scenario-based software testing. However, the generation of the test scenarios is usually a manual and labor-intensive task. It is desired that test scenarios can be automatically generated. In this paper, we propose an automated approach using adaptive agents to directly generate test scenarios from the UML activity diagrams.

Although a number of weaknesses of symbolic execution, when used for software testing, have been highlighted in the literature, the recent resurgence of strongly-typed languages has created opportunities for re-examining symbolic execution to determine whether these shortfalls can be overcome. This paper discusses symbolic execution in general and makes two contributions: (a) overcoming one of the key problems, analysing programs with indexed arrays; and (b) describing the incorporation of a symbolic execution module for test case generation into an integrated testing tool. For methods which index arrays, a new approach determines all the possible values of each array index, allowing the generation of equivalence classes for every possible combination of array element aliases. An incremental simplification approach, which converts path expressions to canonical forms in order to identify infeasible paths at the earliest opportunity and thus reduce the analysis time, is also described. Symbolic execution is most effective when included in an integrated test and analysis environment: a component test bench was built with a symbolic execution module integrated into it, providing a toolbox of software component test and code analysis methods aimed at programmers at all levels. Copyright © 2000 John Wiley & Sons, Ltd.

Software testing plays a crucial role in assuring software quality. One of the most important issues in software testing research is the generation of the test cases. For scenario-based software testing, the thin-threads, which are the usage scenarios in a software system from the end user's point of view, are frequently used to generate test cases. However, the generation of the thin-threads is not an easy task. A scenario-based business model has to be manually derived or labor-intensive business analysis has to be manually carried out in order to extract the thin-threads from a software system. In this work, we propose an automated approach to directly generate thin-threads from the UML artifacts. The generated thin-threads can be used to generate and to prioritize the test cases for scenario-based software testing.

Component-based software engineering (CBSE) provides solutions to the development of complex and evolving systems. As these systems are created and maintained, the task of selecting components is repeated. The context-driven component evaluation (CdCE) project is developing strategies and techniques for automating a repeatable process for assessing software components. This paper describes our work using artificial intelligence (AI) techniques to classify components based on an ideal component specification. Using AI we are able to represent dependencies between attributes, overcoming some of the limitations of existing aggregation-based approaches to component selection.

We describe a process for selecting and evaluating candidates for component based software engineering. The process is aimed at developers sourcing components from third party vendors. Component metadata and a formalised specification of the ideal component, including context information, are used to drive the process. This specification is used to shortlist candidate components from commercial repositories and to generate the tests and adaptations for the candidate components. Metrics from each stage of the selection and evaluation process are then combined to compare and rank components for inclusion in the target application. This approach to component selection, using context information and formal methods, helps address issues with component sourcing, selection and testing of third party components.

This paper describes an investigation into the use of parametric 2D models describing the movement of edges for the determination of possible 3D shape and hence function of an object. An assumption of this research is that the camera can foveate and track particular features. It is argued that simple 2D analytic descriptions of the movement of edges can infer 3D shape while the camera is moved. This uses an advantage of foveation i.e. The problem becomes object centred. The problem of correspondence for numerous edge points is overcome by the use of a tree based representation for the competing hypotheses. Numerous hypothesis are maintained simultaneously and it does not rely on a single kinematic model which assumes constant velocity or acceleration. The numerous advantages of this strategy are described.>