Epistemological approach to the process of practice
- Dazeley, Richard, Kang, Byeongho
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Journal article
- Relation: Minds and Machines Vol. 18, no. 4 (2008), p. 547-567
- Full Text:
- Reviewed:
- Description: Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence of a model for the process of practise in current approaches is a significant contributor to brittleness. This paper will investigate how the process of practise relates to deeper forms of contextual representations of knowledge. While researchers and developers of knowledge based systems have often incorporated the notion of context they treat context as a static entity, neglecting many connectionists' work in learning hidden and dynamic contexts. This paper argues that the omission of these higher forms of context is one of the fundamental problems in the application and interpretation of symbolic knowledge. Finally, these ideas for modelling context will lead to the reinterpretation of situation cognition which makes a significant step towards a philosophy of knowledge that could lead to the modelling of the process of practice. © 2008 Springer Science+Business Media B.V.
- Description: C1
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Journal article
- Relation: Minds and Machines Vol. 18, no. 4 (2008), p. 547-567
- Full Text:
- Reviewed:
- Description: Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence of a model for the process of practise in current approaches is a significant contributor to brittleness. This paper will investigate how the process of practise relates to deeper forms of contextual representations of knowledge. While researchers and developers of knowledge based systems have often incorporated the notion of context they treat context as a static entity, neglecting many connectionists' work in learning hidden and dynamic contexts. This paper argues that the omission of these higher forms of context is one of the fundamental problems in the application and interpretation of symbolic knowledge. Finally, these ideas for modelling context will lead to the reinterpretation of situation cognition which makes a significant step towards a philosophy of knowledge that could lead to the modelling of the process of practice. © 2008 Springer Science+Business Media B.V.
- Description: C1
Prediction using a symbolic based hybrid system
- Dazeley, Richard, Kang, Byeongho
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at Pacific Rim Knowledge Acquisition Workshop 2008, PKAW-08, Hanoi, Vietnam : 15th-16th December 2008
- Full Text:
- Description: Knowledge Based Systems (KBS) are highly successful in classification and diagnostics situations; however, they are generally unable to identify specific values for prediction problems. When used for prediction they either use some form of uncertainty reasoning or use a classification style inference where each class is a discrete predictive value instead. This paper applies a hybrid algorithm that allows an expert’s knowledge to be adapted to provide continuous values to solve prediction problems. The method applied to prediction in this paper is built on the already established Multiple Classification Ripple-Down Rules (MCRDR) approach and is referred to as Rated MCRDR (RM). The method is published in a parallel paper in this workshop titled Generalisation with Symbolic Knowledge in Online Classification. Results indicate a strong propensity to quickly adapt and provide accurate predictions.
- Description: 2003006510
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at Pacific Rim Knowledge Acquisition Workshop 2008, PKAW-08, Hanoi, Vietnam : 15th-16th December 2008
- Full Text:
- Description: Knowledge Based Systems (KBS) are highly successful in classification and diagnostics situations; however, they are generally unable to identify specific values for prediction problems. When used for prediction they either use some form of uncertainty reasoning or use a classification style inference where each class is a discrete predictive value instead. This paper applies a hybrid algorithm that allows an expert’s knowledge to be adapted to provide continuous values to solve prediction problems. The method applied to prediction in this paper is built on the already established Multiple Classification Ripple-Down Rules (MCRDR) approach and is referred to as Rated MCRDR (RM). The method is published in a parallel paper in this workshop titled Generalisation with Symbolic Knowledge in Online Classification. Results indicate a strong propensity to quickly adapt and provide accurate predictions.
- Description: 2003006510
An expert system methodology for SMEs and NPOs
- Authors: Dazeley, Richard
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at 11th Australian Conference on Knowledge Management and Intelligent Decision Support, ACKMIDS 2008, Ballarat, Victoria : 8th-10th December 2008
- Full Text:
- Description: Traditionally Expert Systems (ES) require a full analysis of the business problem by a Knowledge Engineer (KE) to develop a solution. This inherently makes ES technology very expensive and beyond the affordability of the majority of Small and Medium sized Enterprises (SMEs) and Non-Profit Organisations (NPOs). Therefore, SMEs and NPOs tend to only have access to off-the-shelf solutions to generic problems, which rarely meet the full extent of an organisation’s requirements. One existing methodological stream of research, Ripple-Down Rules (RDR) goes some of the way to being suitable to SMEs and NPOs as it removes the need for a knowledge engineer. This group of methodologies provide an environment where a company can develop large knowledge based systems themselves, specifically tailored to the company’s individual situation. These methods, however, require constant supervision by the expert during development, which is still a significant burden on the organisation. This paper discusses an extension to an RDR method, known as Rated MCRDR (RM) and a feature called prudence analysis. This enhanced methodology to ES development is particularly well suited to the development of ES in restricted environments such as SMEs and NPOs.
- Description: 2003006507
- Authors: Dazeley, Richard
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at 11th Australian Conference on Knowledge Management and Intelligent Decision Support, ACKMIDS 2008, Ballarat, Victoria : 8th-10th December 2008
- Full Text:
- Description: Traditionally Expert Systems (ES) require a full analysis of the business problem by a Knowledge Engineer (KE) to develop a solution. This inherently makes ES technology very expensive and beyond the affordability of the majority of Small and Medium sized Enterprises (SMEs) and Non-Profit Organisations (NPOs). Therefore, SMEs and NPOs tend to only have access to off-the-shelf solutions to generic problems, which rarely meet the full extent of an organisation’s requirements. One existing methodological stream of research, Ripple-Down Rules (RDR) goes some of the way to being suitable to SMEs and NPOs as it removes the need for a knowledge engineer. This group of methodologies provide an environment where a company can develop large knowledge based systems themselves, specifically tailored to the company’s individual situation. These methods, however, require constant supervision by the expert during development, which is still a significant burden on the organisation. This paper discusses an extension to an RDR method, known as Rated MCRDR (RM) and a feature called prudence analysis. This enhanced methodology to ES development is particularly well suited to the development of ES in restricted environments such as SMEs and NPOs.
- Description: 2003006507
Generalising symbolic knowledge in online classification and prediction
- Dazeley, Richard, Kang, Byeongho
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2009
- Type: Text , Journal article
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 5465 LNAI, no. (15 December 2008 through 16 December 2008 2009), p. 91-108
- Full Text:
- Reviewed:
- Description: Increasingly, researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. For instance, Repertory Grids, Formal Concept Analysis (FCA) and Ripple-Down Rules (RDR) all integrate either implicit or explicit contextual information. However, these methodologies treat context as a static entity, neglecting many connectionists' work in learning hidden and dynamic contexts, which aid their ability to generalize. This paper presents a method that models hidden context within a symbolic domain in order to achieve a level of generalisation. The method developed builds on the already established Multiple Classification Ripple-Down Rules (MCRDR) approach and is referred to as Rated MCRDR (RM). RM retains a symbolic core, while using a connection based approach to learn a deeper understanding of the captured knowledge. This method is applied to a number of classification and prediction environments and results indicate that the method can learn the information that experts have difficulty providing. © Springer-Verlag Berlin Heidelberg 2009.
- Description: 2003006509
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2009
- Type: Text , Journal article
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 5465 LNAI, no. (15 December 2008 through 16 December 2008 2009), p. 91-108
- Full Text:
- Reviewed:
- Description: Increasingly, researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. For instance, Repertory Grids, Formal Concept Analysis (FCA) and Ripple-Down Rules (RDR) all integrate either implicit or explicit contextual information. However, these methodologies treat context as a static entity, neglecting many connectionists' work in learning hidden and dynamic contexts, which aid their ability to generalize. This paper presents a method that models hidden context within a symbolic domain in order to achieve a level of generalisation. The method developed builds on the already established Multiple Classification Ripple-Down Rules (MCRDR) approach and is referred to as Rated MCRDR (RM). RM retains a symbolic core, while using a connection based approach to learn a deeper understanding of the captured knowledge. This method is applied to a number of classification and prediction environments and results indicate that the method can learn the information that experts have difficulty providing. © Springer-Verlag Berlin Heidelberg 2009.
- Description: 2003006509
Automatically determining phishing campaigns using the USCAP methodology
- Layton, Robert, Watters, Paul, Dazeley, Richard
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at General Members Meeting and eCrime Researchers Summit, eCrime 2010 p. 1-8
- Full Text:
- Reviewed:
- Description: Phishing fraudsters attempt to create an environment which looks and feels like a legitimate institution, while at the same time attempting to bypass filters and suspicions of their targets. This is a difficult compromise for the phishers and presents a weakness in the process of conducting this fraud. In this research, a methodology is presented that looks at the differences that occur between phishing websites from an authorship analysis perspective and is able to determine different phishing campaigns undertaken by phishing groups. The methodology is named USCAP, for Unsupervised SCAP, which builds on the SCAP methodology from supervised authorship and extends it for unsupervised learning problems. The phishing website source code is examined to generate a model that gives the size and scope of each of the recognized phishing campaigns. The USCAP methodology introduces the first time that phishing websites have been clustered by campaign in an automatic and reliable way, compared to previous methods which relied on costly expert analysis of phishing websites. Evaluation of these clusters indicates that each cluster is strongly consistent with a high stability and reliability when analyzed using new information about the attacks, such as the dates that the attack occurred on. The clusters found are indicative of different phishing campaigns, presenting a step towards an automated phishing authorship analysis methodology. © 2010 IEEE.
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at General Members Meeting and eCrime Researchers Summit, eCrime 2010 p. 1-8
- Full Text:
- Reviewed:
- Description: Phishing fraudsters attempt to create an environment which looks and feels like a legitimate institution, while at the same time attempting to bypass filters and suspicions of their targets. This is a difficult compromise for the phishers and presents a weakness in the process of conducting this fraud. In this research, a methodology is presented that looks at the differences that occur between phishing websites from an authorship analysis perspective and is able to determine different phishing campaigns undertaken by phishing groups. The methodology is named USCAP, for Unsupervised SCAP, which builds on the SCAP methodology from supervised authorship and extends it for unsupervised learning problems. The phishing website source code is examined to generate a model that gives the size and scope of each of the recognized phishing campaigns. The USCAP methodology introduces the first time that phishing websites have been clustered by campaign in an automatic and reliable way, compared to previous methods which relied on costly expert analysis of phishing websites. Evaluation of these clusters indicates that each cluster is strongly consistent with a high stability and reliability when analyzed using new information about the attacks, such as the dates that the attack occurred on. The clusters found are indicative of different phishing campaigns, presenting a step towards an automated phishing authorship analysis methodology. © 2010 IEEE.
Constructing stochastic mixture policies for episodic multiobjective reinforcement learning tasks
- Vamplew, Peter, Dazeley, Richard, Barker, Ewan, Kelarev, Andrei
- Authors: Vamplew, Peter , Dazeley, Richard , Barker, Ewan , Kelarev, Andrei
- Date: 2009
- Type: Text , Book chapter
- Relation: AI 2009 : Advances in Artificial Intelligence : 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009. Proceedings Chapter p. 340-349
- Full Text:
- Description: Multiobjective reinforcement learning algorithms extend reinforcement learning techniques to problems with multiple conflicting objectives. This paper discusses the advantages gained from applying stochastic policies to multiobjective tasks and examines a particular form of stochastic policy known as a mixture policy. Two methods are proposed for deriving mixture policies for episodic multiobjective tasks from deterministic base policies found via scalarised reinforcement learning. It is shown that these approaches are an efficient means of identifying solutions which offer a superior match to the user’s preferences than can be achieved by methods based strictly on deterministic policies.
- Description: 2003007906
- Authors: Vamplew, Peter , Dazeley, Richard , Barker, Ewan , Kelarev, Andrei
- Date: 2009
- Type: Text , Book chapter
- Relation: AI 2009 : Advances in Artificial Intelligence : 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009. Proceedings Chapter p. 340-349
- Full Text:
- Description: Multiobjective reinforcement learning algorithms extend reinforcement learning techniques to problems with multiple conflicting objectives. This paper discusses the advantages gained from applying stochastic policies to multiobjective tasks and examines a particular form of stochastic policy known as a mixture policy. Two methods are proposed for deriving mixture policies for episodic multiobjective tasks from deterministic base policies found via scalarised reinforcement learning. It is shown that these approaches are an efficient means of identifying solutions which offer a superior match to the user’s preferences than can be achieved by methods based strictly on deterministic policies.
- Description: 2003007906
Authorship attribution for Twitter in 140 characters or less
- Layton, Robert, Watters, Paul, Dazeley, Richard
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at - 2nd Cybercrime and Trustworthy Computing Workshop, CTC 2010 p. 1-8
- Full Text:
- Reviewed:
- Description: Authorship attribution is a growing field, moving from beginnings in linguistics to recent advances in text mining. Through this change came an increase in the capability of authorship attribution methods both in their accuracy and the ability to consider more difficult problems. Research into authorship attribution in the 19th century considered it difficult to determine the authorship of a document of fewer than 1000 words. By the 1990s this values had decreased to less than 500 words and in the early 21 st century it was considered possible to determine the authorship of a document in 250 words. The need for this ever decreasing limit is exemplified by the trend towards many shorter communications rather than fewer longer communications, such as the move from traditional multi-page handwritten letters to shorter, more focused emails. This trend has also been shown in online crime, where many attacks such as phishing or bullying are performed using very concise language. Cybercrime messages have long been hosted on Internet Relay Chats (IRCs) which have allowed members to hide behind screen names and connect anonymously. More recently, Twitter and other short message based web services have been used as a hosting ground for online crimes. This paper presents some evaluations of current techniques and identifies some new preprocessing methods that can be used to enable authorship to be determined at rates significantly better than chance for documents of 140 characters or less, a format popularised by the micro-blogging website Twitter1. We show that the SCAP methodology performs extremely well on twitter messages and even with restrictions on the types of information allowed, such as the recipient of directed messages, still perform significantly higher than chance. Further to this, we show that 120 tweets per user is an important threshold, at which point adding more tweets per user gives a small but non-significant increase in accuracy. © 2010 IEEE.
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at - 2nd Cybercrime and Trustworthy Computing Workshop, CTC 2010 p. 1-8
- Full Text:
- Reviewed:
- Description: Authorship attribution is a growing field, moving from beginnings in linguistics to recent advances in text mining. Through this change came an increase in the capability of authorship attribution methods both in their accuracy and the ability to consider more difficult problems. Research into authorship attribution in the 19th century considered it difficult to determine the authorship of a document of fewer than 1000 words. By the 1990s this values had decreased to less than 500 words and in the early 21 st century it was considered possible to determine the authorship of a document in 250 words. The need for this ever decreasing limit is exemplified by the trend towards many shorter communications rather than fewer longer communications, such as the move from traditional multi-page handwritten letters to shorter, more focused emails. This trend has also been shown in online crime, where many attacks such as phishing or bullying are performed using very concise language. Cybercrime messages have long been hosted on Internet Relay Chats (IRCs) which have allowed members to hide behind screen names and connect anonymously. More recently, Twitter and other short message based web services have been used as a hosting ground for online crimes. This paper presents some evaluations of current techniques and identifies some new preprocessing methods that can be used to enable authorship to be determined at rates significantly better than chance for documents of 140 characters or less, a format popularised by the micro-blogging website Twitter1. We show that the SCAP methodology performs extremely well on twitter messages and even with restrictions on the types of information allowed, such as the recipient of directed messages, still perform significantly higher than chance. Further to this, we show that 120 tweets per user is an important threshold, at which point adding more tweets per user gives a small but non-significant increase in accuracy. © 2010 IEEE.
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
- 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
Evaluating authorship distance methods using the positive Silhouette coefficient
- Layton, Robert, Watters, Paul, Dazeley, Richard
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2013
- Type: Text , Journal article
- Relation: Natural Language Engineering Vol. 19, no. 4 (2013), p. 517-535
- Full Text:
- Reviewed:
- Description: Unsupervised Authorship Analysis (UAA) aims to cluster documents by authorship without knowing the authorship of any documents. An important factor in UAA is the method for calculating the distance between documents. This choice of the authorship distance method is considered more critical to the end result than the choice of cluster analysis algorithm. One method for measuring the correlation between a distance metric and a labelling (such as class values or clusters) is the Silhouette Coefficient (SC). The SC can be leveraged by measuring the correlation between the authorship distance method and the true authorship, evaluating the quality of the distance method. However, we show that the SC can be severely affected by outliers. To address this issue, we introduce the Positive Silhouette Coefficient, given as the proportion of instances with a positive SC value. This metric is not easily altered by outliers and produces a more robust metric. A large number of authorship distance methods are then compared using the PSC, and the findings are presented. This research provides an insight into the efficacy of methods for UAA and presents a framework for testing authorship distance methods.
- Description: C1
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2013
- Type: Text , Journal article
- Relation: Natural Language Engineering Vol. 19, no. 4 (2013), p. 517-535
- Full Text:
- Reviewed:
- Description: Unsupervised Authorship Analysis (UAA) aims to cluster documents by authorship without knowing the authorship of any documents. An important factor in UAA is the method for calculating the distance between documents. This choice of the authorship distance method is considered more critical to the end result than the choice of cluster analysis algorithm. One method for measuring the correlation between a distance metric and a labelling (such as class values or clusters) is the Silhouette Coefficient (SC). The SC can be leveraged by measuring the correlation between the authorship distance method and the true authorship, evaluating the quality of the distance method. However, we show that the SC can be severely affected by outliers. To address this issue, we introduce the Positive Silhouette Coefficient, given as the proportion of instances with a positive SC value. This metric is not easily altered by outliers and produces a more robust metric. A large number of authorship distance methods are then compared using the PSC, and the findings are presented. This research provides an insight into the efficacy of methods for UAA and presents a framework for testing authorship distance methods.
- Description: C1
The viability of prudence analysis
- Dazeley, Richard, Kang, Byeongho
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at Pacific Rim Knowledge Acquisition Workshop 2008, PKAW-08, Hanoi, Vietnam : 15th-16th December 2008
- Full Text:
- Description: Prudence analysis (PA) is a relatively new, practical and highly innovative approach to solving the problem of brittleness. PA is essentially an incremental validation approach, where each situation or case is presented to the KBS for inferencing and the result is subsequently validated. Therefore, instead of the system simply providing a conclusion, it also provides a warning when the validation fails. This allows the user to check the solution and correct any potential deficiencies found in the knowledge base. There have been a small number of potentially viable approaches to PA published that show a high degree of accuracy in identifying errors. However, none of these are perfect, very rarely a case is classified incorrectly and not identified by the PA system. The work in PA thus far, has focussed on reducing the frequency of these missed warnings, however there has been no studies on the affect of these on the final knowledge base’s performance. This paper will investigate how these errors in a knowledge base affect its ability to correctly classify cases. The results in this study strongly indicate that the missed errors have a significantly smaller influence on the inferencing results than would be expected, which strongly support the viability of PA.
- Description: 2003006508
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at Pacific Rim Knowledge Acquisition Workshop 2008, PKAW-08, Hanoi, Vietnam : 15th-16th December 2008
- Full Text:
- Description: Prudence analysis (PA) is a relatively new, practical and highly innovative approach to solving the problem of brittleness. PA is essentially an incremental validation approach, where each situation or case is presented to the KBS for inferencing and the result is subsequently validated. Therefore, instead of the system simply providing a conclusion, it also provides a warning when the validation fails. This allows the user to check the solution and correct any potential deficiencies found in the knowledge base. There have been a small number of potentially viable approaches to PA published that show a high degree of accuracy in identifying errors. However, none of these are perfect, very rarely a case is classified incorrectly and not identified by the PA system. The work in PA thus far, has focussed on reducing the frequency of these missed warnings, however there has been no studies on the affect of these on the final knowledge base’s performance. This paper will investigate how these errors in a knowledge base affect its ability to correctly classify cases. The results in this study strongly indicate that the missed errors have a significantly smaller influence on the inferencing results than would be expected, which strongly support the viability of PA.
- Description: 2003006508
The ballarat incremental knowledge engine
- Dazeley, Richard, Warner, Philip, Johnson, Scott, Vamplew, Peter
- Authors: Dazeley, Richard , Warner, Philip , Johnson, Scott , Vamplew, Peter
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper pressented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 195-207
- Full Text:
- Reviewed:
- Description: Ripple Down Rules (RDR) is a maturing collection of methodologies for the incremental development and maintenance of medium to large rule-based knowledge systems. While earlier knowledge based systems relied on extensive modeling and knowledge engineering, RDR instead takes a simple no-model approach that merges the development and maintenance stages. Over the last twenty years RDR has been significantly expanded and applied in numerous domains. Until now researchers have generally implemented their own version of the methodologies, while commercial implementations are not made available. This has resulted in much duplicated code and the advantages of RDR not being available to a wider audience. The aim of this project is to develop a comprehensive and extensible platform that supports current and future RDR technologies, thereby allowing researchers and developers access to the power and versatility of RDR. This paper is a report on the current status of the project and marks the first release of the software. © 2010 Springer-Verlag Berlin Heidelberg.
- Authors: Dazeley, Richard , Warner, Philip , Johnson, Scott , Vamplew, Peter
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper pressented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 195-207
- Full Text:
- Reviewed:
- Description: Ripple Down Rules (RDR) is a maturing collection of methodologies for the incremental development and maintenance of medium to large rule-based knowledge systems. While earlier knowledge based systems relied on extensive modeling and knowledge engineering, RDR instead takes a simple no-model approach that merges the development and maintenance stages. Over the last twenty years RDR has been significantly expanded and applied in numerous domains. Until now researchers have generally implemented their own version of the methodologies, while commercial implementations are not made available. This has resulted in much duplicated code and the advantages of RDR not being available to a wider audience. The aim of this project is to develop a comprehensive and extensible platform that supports current and future RDR technologies, thereby allowing researchers and developers access to the power and versatility of RDR. This paper is a report on the current status of the project and marks the first release of the software. © 2010 Springer-Verlag Berlin Heidelberg.
Consensus clustering and supervised classification for profiling phishing emails in internet commerce security
- Dazeley, Richard, Yearwood, John, Kang, Byeongho, Kelarev, Andrei
- Authors: Dazeley, Richard , Yearwood, John , Kang, Byeongho , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 235-246
- Full Text:
- Reviewed:
- Description: This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast supervised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg.
- Authors: Dazeley, Richard , Yearwood, John , Kang, Byeongho , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 235-246
- Full Text:
- Reviewed:
- Description: This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast supervised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg.
Unsupervised authorship analysis of phishing webpages
- Layton, Robert, Watters, Paul, Dazeley, Richard
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Authorship analysis on phishing websites enables the investigation of phishing attacks, beyond basic analysis. In authorship analysis, salient features from documents are used to determine properties about the author, such as which of a set of candidate authors wrote a given document. In unsupervised authorship analysis, the aim is to group documents such that all documents by one author are grouped together. Applying this to cyber-attacks shows the size and scope of attacks from specific groups. This in turn allows investigators to focus their attention on specific attacking groups rather than trying to profile multiple independent attackers. In this paper, we analyse phishing websites using the current state of the art unsupervised authorship analysis method, called NUANCE. The results indicate that the application produces clusters which correlate strongly to authorship, evaluated using expert knowledge and external information as well as showing an improvement over a previous approach with known flaws. © 2012 IEEE.
- Description: 2003010678
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Authorship analysis on phishing websites enables the investigation of phishing attacks, beyond basic analysis. In authorship analysis, salient features from documents are used to determine properties about the author, such as which of a set of candidate authors wrote a given document. In unsupervised authorship analysis, the aim is to group documents such that all documents by one author are grouped together. Applying this to cyber-attacks shows the size and scope of attacks from specific groups. This in turn allows investigators to focus their attention on specific attacking groups rather than trying to profile multiple independent attackers. In this paper, we analyse phishing websites using the current state of the art unsupervised authorship analysis method, called NUANCE. The results indicate that the application produces clusters which correlate strongly to authorship, evaluated using expert knowledge and external information as well as showing an improvement over a previous approach with known flaws. © 2012 IEEE.
- Description: 2003010678
Local n-grams for author identification: Notebook for PAN at CLEF 2013 C3 - CEUR Workshop Proceedings
- Layton, Robert, Watters, Paul, Dazeley, Richard
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2013
- Type: Text , Conference proceedings
- Full Text:
- Description: Our approach to the author identification task uses existing authorship attribution methods using local n-grams (LNG) and performs a weighted ensemble. This approach came in third for this year's competition, using a relatively simple scheme of weights by training set accuracy. LNG models create profiles, consisting of a list of character n-grams that best represent a particular author's writing. The use of a weighted ensemble improved upon the accuracy of the method without reducing the speed of the algorithm; the submitted solution was not only near the top of the leaderboard in terms of accuracy, but it was also one of the faster algorithms submitted.
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2013
- Type: Text , Conference proceedings
- Full Text:
- Description: Our approach to the author identification task uses existing authorship attribution methods using local n-grams (LNG) and performs a weighted ensemble. This approach came in third for this year's competition, using a relatively simple scheme of weights by training set accuracy. LNG models create profiles, consisting of a list of character n-grams that best represent a particular author's writing. The use of a weighted ensemble improved upon the accuracy of the method without reducing the speed of the algorithm; the submitted solution was not only near the top of the leaderboard in terms of accuracy, but it was also one of the faster algorithms submitted.
Recentred local profiles for authorship attribution
- Layton, Robert, Watters, Paul, Dazeley, Richard
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2012
- Type: Text , Journal article
- Relation: Natural Language Engineering Vol. 18, no. 3 (2012), p. 293-312
- Full Text:
- Reviewed:
- Description: Authorship attribution methods aim to determine the author of a document, by using information gathered from a set of documents with known authors. One method of performing this task is to create profiles containing distinctive features known to be used by each author. In this paper, a new method of creating an author or document profile is presented that detects features considered distinctive, compared to normal language usage. This recentreing approach creates more accurate profiles than previous methods, as demonstrated empirically using a known corpus of authorship problems. This method, named recentred local profiles, determines authorship accurately using a simple 'best matching author' approach to classification, compared to other methods in the literature. The proposed method is shown to be more stable than related methods as parameter values change. Using a weighted voting scheme, recentred local profiles is shown to outperform other methods in authorship attribution, with an overall accuracy of 69.9% on the ad-hoc authorship attribution competition corpus, representing a significant improvement over related methods. Copyright © Cambridge University Press 2011.
- Description: 2003010688
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2012
- Type: Text , Journal article
- Relation: Natural Language Engineering Vol. 18, no. 3 (2012), p. 293-312
- Full Text:
- Reviewed:
- Description: Authorship attribution methods aim to determine the author of a document, by using information gathered from a set of documents with known authors. One method of performing this task is to create profiles containing distinctive features known to be used by each author. In this paper, a new method of creating an author or document profile is presented that detects features considered distinctive, compared to normal language usage. This recentreing approach creates more accurate profiles than previous methods, as demonstrated empirically using a known corpus of authorship problems. This method, named recentred local profiles, determines authorship accurately using a simple 'best matching author' approach to classification, compared to other methods in the literature. The proposed method is shown to be more stable than related methods as parameter values change. Using a weighted voting scheme, recentred local profiles is shown to outperform other methods in authorship attribution, with an overall accuracy of 69.9% on the ad-hoc authorship attribution competition corpus, representing a significant improvement over related methods. Copyright © Cambridge University Press 2011.
- Description: 2003010688
Automated unsupervised authorship analysis using evidence accumulation clustering
- Layton, Robert, Watters, Paul, Dazeley, Richard
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2013
- Type: Text , Journal article
- Relation: Natural Language Engineering Vol. 19, no. 1 (2013), p. 95-120
- Full Text:
- Reviewed:
- Description: Authorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of clusters of authorship within a corpus by analysts. However, there is a need in many fields for more sophisticated unsupervised methods to automate the discovery, profiling and organisation of related information through clustering of documents by authorship. An automated and unsupervised methodology for clustering documents by authorship is proposed in this paper. The methodology is named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble. Testing indicates that the derived clusters have a strong correlation to the true authorship of unseen documents. © 2011 Cambridge University Press.
- Description: 2003010584
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2013
- Type: Text , Journal article
- Relation: Natural Language Engineering Vol. 19, no. 1 (2013), p. 95-120
- Full Text:
- Reviewed:
- Description: Authorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of clusters of authorship within a corpus by analysts. However, there is a need in many fields for more sophisticated unsupervised methods to automate the discovery, profiling and organisation of related information through clustering of documents by authorship. An automated and unsupervised methodology for clustering documents by authorship is proposed in this paper. The methodology is named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble. Testing indicates that the derived clusters have a strong correlation to the true authorship of unseen documents. © 2011 Cambridge University Press.
- Description: 2003010584
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
- 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
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:
- 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.
- 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:
- 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.
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:
- 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.
- 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:
- 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.
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.
- 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.