Showing items 1 - 20 of 46

Your selections:

An anomaly intrusion detection system using C5 decision tree classifier

- Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter

Human-aligned artificial intelligence is a multiobjective problem

- Vamplew, Peter, Dazeley, Richard, Foale, Cameron, Firmin, Sally, Mummery, Jane

  • Authors: Vamplew, Peter , Dazeley, Richard , Foale, Cameron , Firmin, Sally , Mummery, Jane
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Ethics and Information Technology Vol. 20, no. 1 (2018), p. 27-40
  • Full Text:
  • Reviewed:
  • Description: As the capabilities of artificial intelligence (AI) systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility (MEU) paradigm provides insufficient support for such multiobjective decision-making. We show that a Multiobjective Maximum Expected Utility paradigm based on the combination of vector utilities and non-linear action–selection can overcome many of the issues which limit MEU’s effectiveness in implementing aligned AI. We examine existing approaches to multiobjective AI, and identify how these can contribute to the development of human-aligned intelligent agents. © 2017, Springer Science+Business Media B.V.

Non-functional regression : A new challenge for neural networks

- Vamplew, Peter, Dazeley, Richard, Foale, Cameron, Choudhury, Tanveer

  • Authors: Vamplew, Peter , Dazeley, Richard , Foale, Cameron , Choudhury, Tanveer
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Neurocomputing Vol. 314, no. (2018), p. 326-335
  • Full Text: false
  • Reviewed:
  • Description: This work identifies an important, previously unaddressed issue for regression based on neural networks – learning to accurately approximate problems where the output is not a function of the input (i.e. where the number of outputs required varies across input space). Such non-functional regression problems arise in a number of applications, and can not be adequately handled by existing neural network algorithms. To demonstrate the benefits possible from directly addressing non-functional regression, this paper proposes the first neural algorithm to do so – an extension of the Resource Allocating Network (RAN) which adds additional output neurons to the network structure during training. This new algorithm, called the Resource Allocating Network with Varying Output Cardinality (RANVOC), is demonstrated to be capable of learning to perform non-functional regression, on both artificially constructed data and also on the real-world task of specifying parameter settings for a plasma-spray process. Importantly RANVOC is shown to outperform not just the original RAN algorithm, but also the best possible error rates achievable by any functional form of regression.

A taxonomy of griefer type by motivation in massively multiplayer online role-playing games

- Achterbosch, Leigh, Miller, Charlynn, Vamplew, Peter

  • Authors: Achterbosch, Leigh , Miller, Charlynn , Vamplew, Peter
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Behaviour and Information Technology Vol. 36, no. 8 (2017), p. 846-860
  • Full Text: false
  • Reviewed:
  • Description: There is an anti-social phenomenon known as griefing that occurs in online games. Griefing refers to the act of one player intentionally disrupting another player’s game experience for personal pleasure and possibly potential gain. Achterbosch [2015. “Causes, Magnitude and Implications of Griefing in Massively Multiplayer Online Role-Playing Games.” PhD thesis, Faculty of Science and Technology, Federation University Australia] carried out a substantial two-phase mixed method investigation into the behaviour and experiences of both griefers and griefed players in massively multiplayer online role-playing games. The first phase consisted of a survey that attracted 1188 participants of a representative player population. The second phase consisted of interviews with 15 participants to expand the findings with more personalised data. The data were analysed from the perspectives of different demographics and different associations to griefing. One of the most unique findings is the factors that motivated a player to cause grief to another player. This paper analyses these factors to propose a taxonomy of ‘Griefer’ types (griefer being the individual who imposes upon others). The taxonomy consisted of eight types of griefers, based on their motivation for griefing. Some types related to previous studies, although new types of griefers were discovered such as the retaliator and elitist and these are discussed in detail in the article. © 2017 Informa UK Limited, trading as Taylor & Francis Group.

Softmax exploration strategies for multiobjective reinforcement learning

- Vamplew, Peter, Dazeley, Richard, Foale, Cameron

  • Authors: Vamplew, Peter , Dazeley, Richard , Foale, Cameron
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Neurocomputing Vol. 263, no. (2017), p. 74-86
  • Full Text: false
  • Reviewed:
  • Description: Despite growing interest over recent years in applying reinforcement learning to multiobjective problems, there has been little research into the applicability and effectiveness of exploration strategies within the multiobjective context. This work considers several widely-used approaches to exploration from the single-objective reinforcement learning literature, and examines their incorporation into multiobjective Q-learning. In particular this paper proposes two novel approaches which extend the softmax operator to work with vector-valued rewards. The performance of these exploration strategies is evaluated across a set of benchmark environments. Issues arising from the multiobjective formulation of these benchmarks which impact on the performance of the exploration strategies are identified. It is shown that of the techniques considered, the combination of the novel softmax–epsilon exploration with optimistic initialisation provides the most effective trade-off between exploration and exploitation.

Steering approaches to Pareto-optimal multiobjective reinforcement learning

- Vamplew, Peter, Issabekov, Rustam, Dazeley, Richard, Foale, Cameron, Berry, Adam, Moore, Tim, Creighton, Douglas

  • Authors: Vamplew, Peter , Issabekov, Rustam , Dazeley, Richard , Foale, Cameron , Berry, Adam , Moore, Tim , Creighton, Douglas
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Neurocomputing Vol. 263, no. (2017), p. 26-38
  • Full Text: false
  • Reviewed:
  • Description: For reinforcement learning tasks with multiple objectives, it may be advantageous to learn stochastic or non-stationary policies. This paper investigates two novel algorithms for learning non-stationary policies which produce Pareto-optimal behaviour (w-steering and Q-steering), by extending prior work based on the concept of geometric steering. Empirical results demonstrate that both new algorithms offer substantial performance improvements over stationary deterministic policies, while Q-steering significantly outperforms w-steering when the agent has no information about recurrent states within the environment. It is further demonstrated that Q-steering can be used interactively by providing a human decision-maker with a visualisation of the Pareto front and allowing them to adjust the agent’s target point during learning. To demonstrate broader applicability, the use of Q-steering in combination with function approximation is also illustrated on a task involving control of local battery storage for a residential solar power system.

Coarse Q-Learning : Addressing the convergence problem when quantizing continuous state variables

- Dazeley, Richard, Vamplew, Peter, Bignold, Adam

  • Authors: Dazeley, Richard , Vamplew, Peter , Bignold, Adam
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making
  • Full Text: false
  • Reviewed:
  • Description: Value-based approaches to reinforcement learning (RL) maintain a value function that measures the long term utility of a state or state-action pair. A long standing issue in RL is how to create a finite representation in a continuous, and therefore infinite, state environment. The common approach is to use function approximators such as tile coding, memory or instance based methods. These provide some balance between generalisation, resolution, and storage, but converge slowly in multidimensional state environments. Another approach of quantizing state into lookup tables has been commonly regarded as highly problematic, due to large memory requirements and poor generalisation. In particular , attempting to reduce memory requirements and increase generalisation by using coarser quantization forms a non-Markovian system that does not converge. This paper investigates the problem in using quantized lookup tables and presents an extension to the Q-Learning algorithm, referred to as Coarse Q-Learning (C QL), which resolves these issues. The presented algorithm will be shown to drastically reduce the memory requirements and increase generalisation by simulating the Markov property. In particular, this algorithm means the size of the input space is determined by the granularity required by the policy being learnt, rather than by the inadequacies of the learning algorithm or the nature of the state-reward dynamics of the environment. Importantly, the method presented solves the problem represented by the curse of dimensionality.

Patient admission prediction using a pruned fuzzy min-max neural network with rule extraction

- Wang, Jin, Lim, Cheepeng, Creighton, Douglas, Khorsavi, Abbas, Nahavandi, Saeid, Ugon, Julien, Vamplew, Peter, Stranieri, Andrew, Martin, Laura, Freischmidt, Anton

  • Authors: Wang, Jin , Lim, Cheepeng , Creighton, Douglas , Khorsavi, Abbas , Nahavandi, Saeid , Ugon, Julien , Vamplew, Peter , Stranieri, Andrew , Martin, Laura , Freischmidt, Anton
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 26, no. 2 (2015), p. 277-289
  • Full Text: false
  • Reviewed:
  • Description: A useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min–max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions.

Reinforcement learning of pareto-optimal multiobjective policies using steering

- Vamplew, Peter, Issabekov, Rustam, Dazeley, Richard, Foale, Cameron

  • Authors: Vamplew, Peter , Issabekov, Rustam , Dazeley, Richard , Foale, Cameron
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 28th Australasian Joint Conference on Artificial Intelligence, AI 2015; Canberra, ACT; 30th November-4th December 2015 Vol. 9457, p. 596-608
  • Full Text: false
  • Reviewed:
  • Description: There has been little research into multiobjective reinforcement learning (MORL) algorithms using stochastic or non-stationary policies, even though such policies may Pareto-dominate deterministic stationary policies. One approach is steering which forms a nonstationary combination of deterministic stationary base policies. This paper presents two new steering algorithms designed for the task of learning Pareto-optimal policies. The first algorithm (w-steering) is a direct adaptation of previous approaches to steering, and therefore requires prior knowledge of recurrent states which are guaranteed to be revisited. The second algorithm (Q-steering) eliminates this requirement. Empirical results show that both algorithms perform well when given knowledge of recurrent states, but that Q-steering provides substantial performance improvements over w-steering when this knowledge is not available. © Springer International Publishing Switzerland 2015.

Griefers versus the Griefed - what motivates them to play Massively Multiplayer Online Role-Playing Games?

- Achterbosch, Leigh, Miller, Charlynn, Turville, Christopher, Vamplew, Peter

  • Authors: Achterbosch, Leigh , Miller, Charlynn , Turville, Christopher , Vamplew, Peter
  • Date: 2014
  • Type: Text , Journal article
  • Relation: The Computer Games Journal Vol. 3, no. 1 (2014), p. 5-18
  • Full Text:
  • Reviewed:
  • Description: 'Griefing' is a term used to describe when a player within a multiplayer online environment intentionally disrupts another player’s game experience for his or her own personal enjoyment or gain. Every day a certain percentage of users of Massively Multiplayer Online Role-Playing Games (MMORPG) are experiencing some form of griefing. There have been studies conducted in the past that attempted to ascertain the factors that motivate users to play MMORPGs. A limited number of studies specifically examined the motivations of users who perform griefing (who are also known as 'griefers'). However, those studies did not examine the motivations of users subjected to griefing. Therefore, the aim of this paper is to examine the factors that motivate the subjects of griefing to play MMORPGs, as well as the factors motivating the griefers. The authors conducted an online survey with the intention to discover the motivations for playing MMORPGs among those whom identified themselves as (i) those that perform griefing, and (ii) those who have been subjected to griefing. A previously devised motivational model by Nick Yee that incorporated ten factors was used to determine the respondents’ motivational trends. In general, players who identified themselves as griefers were more likely to be motivated by all three 'achievement' sub-factors (advancement, game mechanics and competition) at the detriment of all other factors. The subjects of griefing were highly motivated by 'advancement' and 'mechanics', but they ranked 'competition' significantly lower (compared to the griefers). In addition, 'immersion' factors were rated highly by the respondents who were subjected to griefing, with a significantly higher rating of the 'escapism' factor (compared with rankings by griefers). In comparison to the griefers, the respondents subjected to griefing with many years’ experience in the genre of MMORPGs, also placed a greater emphasis on the 'socializing' and 'relationship' factors. Overall, the griefers in this survey considered 'achievement' to be a prime motivating factor, whereas the griefed players tended to be motivated by all ten factors to a similar degree.

A survey of multi-objective sequential decision-making

- Roijers, Diederik, Vamplew, Peter, Whiteson, Shimon, Dazeley, Richard

  • Authors: Roijers, Diederik , Vamplew, Peter , Whiteson, Shimon , Dazeley, Richard
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Journal of Artificial Intelligence Research Vol. 48, no. (2013), p. 67-113
  • Full Text:
  • Reviewed:
  • Description: Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work. © 2013 AI Access Foundation.
  • Description: C1

Ganking, corpse camping and ninja looting from the perception of the MMORPG community: Acceptable behaviour or unacceptable griefing?

- Achterbosch, Leigh, Miller, Charlynn, Vamplew, Peter

  • Authors: Achterbosch, Leigh , Miller, Charlynn , Vamplew, Peter
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 9th Australasian Conference on Interactive entertainment p. 19
  • Full Text: false
  • Reviewed:
  • Description: Every day in online games designed to entertain, an unknown percentage of users are experiencing what is known as 'Griefing'. Griefing is used to describe when a player within a multiplayer online environment intentionally disrupts another player's game experience for his/her own personal enjoyment or material gain. Unrestrained, griefing could lead to a downward spiral of the number of people playing Massively Multiplayer Online Role-Playing Games (MMORPG)s, and possibly the death of smaller MMORPGs. Big game publishers may not wish to risk supporting the genre. There have been studies conducted in the past that attempt to define griefing and the different forms it takes in MMORPGs. These were outlined from the perception of the general player, and so did not examine differences in perception of griefing by different types of players. The authors conducted an online survey with the intention to discover the perception of various in-game actions previously identified in research as griefing, among griefers and griefing victims. In general players who identified themselves as griefers were more likely to regard these actions as a part of the game people had to learn to accept and not griefing. However some patterns of commonality were also observed between griefers and subjects of griefing, with some actions previously identified as griefing in the literature less commonly regarded as griefing by both player types in this survey.

An empirical comparison of two common multiobjective reinforcement learning algorithms

- Issabekov, Rustam, Vamplew, Peter

Applications of machine learning for linguistic analysis of texts

- Torney, Rosemary, Yearwood, John, Vamplew, Peter, Kelarev, Andrei

  • Authors: Torney, Rosemary , Yearwood, John , Vamplew, Peter , Kelarev, Andrei
  • Date: 2012
  • Type: Text , Book chapter
  • Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 133-148
  • Full Text: false
  • Reviewed:
  • Description: This chapter describes a novel multistage method for linguistic clustering of large collections of texts available on the Internet as a precursor to linguistic analysis of these texts. This method addresses the practicalities of applying clustering operations to a very large set of text documents by using a combination of unsupervised clustering and supervised classification. The method relies on creating a multitude of independent clusterings of a randomized sample selected from the International Corpus of Learner English. Several consensus functions and sophisticated algorithms are applied in two substages to combine these independent clusterings into one final consensus clustering, which is then used to train fast classifiers in order to enable them to perform the profiling of very large collections of text and web data. This approach makes it possible to apply advanced highly accurate and sophisticated clustering techniques by combining them with fast supervised classification algorithms. For the effectiveness of this multistage method it is crucial to determine how well the supervised classification algorithms are going to perform at the final stage, when they are used to process large data sets available on the Internet. This performance may also serve as an indication of the quality of the combined consensus clustering obtained in the preceding stages. The authors' experimental results compare the performance of several classification algorithms incorporated in this multistage scheme and demonstrate that several of these classification algorithms achieve very high precision and recall and can be used in practical implementations of their method.

Detecting K-complexes for sleep stage identification using nonsmooth optimization

- Moloney, David, Sukhorukova, Nadezda, Vamplew, Peter, Ugon, Julien, Li, Gang, Beliakov, Gleb, Philippe, Carole, Amiel, Hélène, Ugon, Adrien

  • Authors: Moloney, David , Sukhorukova, Nadezda , Vamplew, Peter , Ugon, Julien , Li, Gang , Beliakov, Gleb , Philippe, Carole , Amiel, Hélène , Ugon, Adrien
  • Date: 2012
  • Type: Text , Journal article
  • Relation: ANZIAM Journal Vol. 52, no. 4 (2012), p. 319-332
  • Full Text:
  • Reviewed:
  • Description: The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient's overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract "easily classified" K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features. © 2012 Australian Mathematical Society.

Prudent fraud detection in internet banking

- Maruatona, Omaru, Vamplew, Peter, Dazeley, Richard

  • Authors: Maruatona, Omaru , Vamplew, Peter , Dazeley, Richard
  • Date: 2012
  • Type: Text , Conference proceedings
  • Full Text:
  • Description: Most commercial Fraud Detection components of Internet banking systems use some kind of hybrid setup usually comprising a Rule-Base and an Artificial Neural Network. Such rule bases have been criticised for a lack of innovation in their approach to Knowledge Acquisition and maintenance. Furthermore, the systems are brittle; they have no way of knowing when a previously unseen set of fraud patterns is beyond their current knowledge. This limitation may have far reaching consequences in an online banking system. This paper presents a viable alternative to brittleness in Knowledge Based Systems; a potential milestone in the rapid detection of unique and novel fraud patterns in Internet banking. The experiments conducted with real online banking transaction log files suggest that Prudent based fraud detection may be a worthy alternative in online banking. © 2012 IEEE.
  • Description: 2003010883

RM and RDM, a preliminary evaluation of two prudent RDR Techniques

- Maruatona, Omaru, Vamplew, Peter, Dazeley, Richard

Taming the devil: a game based approach to teaching immunology

- Nankervis, Scott, Meredith, Grant, Vamplew, Peter, Fotinatos, Nina

  • Authors: Nankervis, Scott , Meredith, Grant , Vamplew, Peter , Fotinatos, Nina
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: ascilite 2012: Future challenges, sustainable futures
  • Full Text: false
  • Reviewed:
  • Description: Immunology is a complex field requiring rapid memorisation of numerous components. An indepth understanding of cellular and molecular biology is required before even moderately advanced concepts can be taught. We sought methods that actively engage students and help develop new knowledge and consolidate existing concepts to support lectures. We created an interactive and entertaining prototype immunology computer game as a tool for learning and revision, with the ability to interactively cover course content outside of class that modern learners expect. Our prototype appears to be a successful study aid when used additionally to attendance at lectures. We seek to continue the development of the game in a higher education context, but also produce a modified version for a secondary school context, in an effort to raise the profile of this key health area and promote learning for the future through the study of the sciences prior to students entering higher education.

Using psycholinguistic features for profiling first language of authors

- Torney, Rosemary, Vamplew, Peter, Yearwood, John

  • Authors: Torney, Rosemary , Vamplew, Peter , Yearwood, John
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Journal of the American Society for Information Science and Technology Vol. 63, no. 6 (2012), p. 1256-1269
  • Full Text: false
  • Reviewed:
  • Description: This study empirically evaluates the effectiveness of different feature types for the classification of the first language of an author. In particular, it examines the utility of psycholinguistic features, extracted by the Linguistic Inquiry and Word Count (LIWC) tool, that have not previously been applied to the task of author profiling. As LIWC is a tool that has been developed in the psycholinguistic field rather than the computational linguistics field, it was hypothesized that it would be effective, both as a single type feature set because of its psycholinguistic basis, and in combination with other feature sets, because it should be sufficiently different to add insight rather than redundancy. It was found that LIWC features were competitive with previously used feature types in identifying the first language of an author, and that combined feature sets including LIWC features consistently showed better accuracy rates and average F measures than were achieved by the same feature sets without the LIWC features. As a secondary issue, this study also examined how effectively first language classification scaled up to a larger number of possible languages. It was found that the classification scheme scaled up effectively to the entire 16 language collection from the International Corpus of Learner English, when compared with results achieved on just 5 languages in previous research. 2012 ASIS&T.

Visualising the value of water

- Block, Jessica, Graymore, Michelle, Wallis, Anne, Vamplew, Peter, Mitchell, Bradley, O'Toole, Kevin, McRae-Williams, Pamela