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Displaying Results 76 - 100 of 3054 on page 4 of 123
Marked
Mark
The Limits of CBR in Software Project Estimation
(1999)
Delany, Sarah Jane; Cunningham, Padraig
The Limits of CBR in Software Project Estimation
(1999)
Delany, Sarah Jane; Cunningham, Padraig
Abstract:
TCD-CS-1999-21
Software project cost estimation is difficult because of problems of quantifying project size and because of the continual emergence of new technology. This presents as a classic example of a weak theory domain where experience is key and appears well suited to CBR. Indeed there are several reports in the literature on the use of CBR in project cost estimation. Research described to date has focused on applications late in the development life cycle and in a narrow domain. In this paper we describe existing research and explore the use of CBR beyond these limits. We look at the use of CBR for estimation earlier in the life cycle and for use in broader domains where more abstract remindings need to be supported.
http://hdl.handle.net/2262/13014
Marked
Mark
Shaping a CBR view with XML
(1999)
Hayes, Conor; Cunningham, Padraig
Shaping a CBR view with XML
(1999)
Hayes, Conor; Cunningham, Padraig
Abstract:
TCD-CS-1999-23
Case Based Reasoning has found increasing application on the Internet as an assistant in Internet commerce stores and as a reasoning agent for online technical support. The strength of CBR in this area stems from its reuse of the knowledge base associated with a particular application, thus providing an ideal way to make personalised configuration or technical information available to the Internet user. Since case data may be one aspect of a company?s entire corporate knowledge system, it is important to integrate case data easily within a company?s IT infrastructure, using industry specific vocabulary. We suggest XML as the likely candidate to provide such integration. Some applications have already begun to use XML as a case representation language. We review these and present the idea of a standard case view in XML that can work with the vocabularies or namespaces being developed by specific industries. Earlier research has produced version 1.0 of a Case Based ...
http://hdl.handle.net/2262/13015
Marked
Mark
Modelling of Engineering Thermal Problems: An Implementation using CBR with Derivational Analogy
(1993)
Finn, Donal; Slattery, Sean; Cunningham, Padraig
Modelling of Engineering Thermal Problems: An Implementation using CBR with Derivational Analogy
(1993)
Finn, Donal; Slattery, Sean; Cunningham, Padraig
Abstract:
TCD-CS-1993-20
An interactive cased based reasoning tool for assisting engineers with the mathematical modelling tasks associated with the analysis of thermal problems is described. By representing fundamental thermal modelling scenarios as cases, complex physical systems are modelled in a piecewise fashion by successive application of matching cases. Retrieval is based on the use of qualitative indices, derivational analogy allows for generative adaption of retrieved cases, thereby providing a basis for validating cases in the context of the problem under consideration. This work represents an alternative perspective to model based reasoning approaches that have been applied to model generation to date.
http://hdl.handle.net/2262/12698
Marked
Mark
Complexity of Adaptation in Real-World Case-Based Reasoning Systems
(1993)
Smyth, Barry; Cunningham, Padraig
Complexity of Adaptation in Real-World Case-Based Reasoning Systems
(1993)
Smyth, Barry; Cunningham, Padraig
Abstract:
TCD-CS-93-23
The essence of Case-Based Reasoning (CBR) as a problem solving paradigm is that solutions are generated by adapting the solutions of similar problems rather than solving the problem from first principles. In this paper we present a categorisation of problem solving tasks, arranged according to complexity. In addition we categorise CBR systems according to the complexity of the adaptation process involved. We describe three CBR systems; a system for property valuation, a system for software design and a system for modelling in engineering analysis. We discuss the manner in which the advantage of a CBR solution to these problems shifts as the task becomes more complex and the complexity of the adaptation process changes.
http://hdl.handle.net/2262/12938
Marked
Mark
Where's Waldo? - or - A taxonomy for thinking about location in pervasive computing
(2004)
Dobson, Simon
Where's Waldo? - or - A taxonomy for thinking about location in pervasive computing
(2004)
Dobson, Simon
Abstract:
TCD-CS-2004-05
[Introduction] Virtually all pervasive computing systems use some form of location for affecting the system's behaviour. Location-based services are available commercially, albeit in a primitive form, from many mobile telecommunications providers. Despite this, location is a remarkably subtle concept to reason with. There are a huge number of possible answers to what is superficially a simple question: each kind of answer reveals something about the way in which we conceptualise location and any services based upon it. This may seem like a trivial observation, but it cuts to the heart of the development of programming environments for pervasive computing. Software design is the process of mapping concepts to programming structures, whether one is developing object models or complete programming languages. A system is easy to program when the developer can express his concepts directly or with minimal mental gymnastics; conversely, the developer takes on a con...
http://hdl.handle.net/2262/13280
Marked
Mark
Context Boosting Collaborative Recommendations
(2003)
Hayes, Conor; Cunningham, Padraig
Context Boosting Collaborative Recommendations
(2003)
Hayes, Conor; Cunningham, Padraig
Abstract:
TCD-CS-2003-26
This paper describes the operation of and research behind a networked application for the delivery of personalised streams of music at Trinity College Dublin. Smart Radio is a web based client-server application that uses streaming audio technology and recommendation techniques to allow users build, manage and share music programmes. Since good content descriptors are difficult to obtain in the audio domain, we originally used automated collaborative filtering, a `content less? approach as our recommendation strategy. We describe how we improve the ACF technique by leveraging a light content-based technique that attempts to capture the user?s current listening `context?. This involves a two stage retrieval process where ACF recommendations are ranked according to the user?s current interests. Finally, we demonstrate a novel online evaluation strategy that pits the ACF strategy against the context-boosted strategy in a real time competition.
http://hdl.handle.net/2262/12579
Marked
Mark
Smart Radio - a proposal
(1999)
Hayes, Conor; Cunningham, Padraig
Smart Radio - a proposal
(1999)
Hayes, Conor; Cunningham, Padraig
Abstract:
TCD-CS-1999-24
There is significant value in having predictions for an item before deciding whether to invest time or money in consuming that item. In a web based scenario where the items are multimedia items such as audio, recommendations can be made to users based on an understanding of their previous consumption or their indications of likes and dislikes. We examine two types of recommendation: content based and non-content or collaborative recommendation. We then apply out thinking to the area of new internet services such as online radio, and propose an architecture for an intelligent music radio system. We then suggest the efficacy of using conceptual clustering techniques in such a paradigm.
http://hdl.handle.net/2262/13016
Marked
Mark
On balancing client - server load in intelligent web-based applications involving dialog
(1999)
Doyle, Michelle; Cunningham, Padraig
On balancing client - server load in intelligent web-based applications involving dialog
(1999)
Doyle, Michelle; Cunningham, Padraig
Abstract:
TCD-CS-1999-25
With the explosive growth of the Internet have come problems of increased server load and network latency. This means that systems that require interchange of data between server and client over the network can be slow to unusable (at peak times). We are investigating methods for developing distributed CBR systems which minimise the load on the servers and the network, thus increasing response time and usability. The first part of this research focuses on balancing load in a client/server situation that involves a long-lived dialog. This report discusses the various issues to be tackled when attempting to effectively balance the processing load.
http://hdl.handle.net/2262/13017
Marked
Mark
Knowledge Engineering in a Real World Case-Based Reasoning Application
(1999)
Cunningham, Padraig; Bonzano, Andrea
Knowledge Engineering in a Real World Case-Based Reasoning Application
(1999)
Cunningham, Padraig; Bonzano, Andrea
Abstract:
TCD-CS-1999-36
Case-Based Reasoning (CBR) has emerged from research in cognitive psychology as a model of human memory and remembering. It has been embraced by researchers of AI applications as a methodology that avoids some of the knowledge acquisition and reasoning problems that occur with other methods for developing knowledge-based systems. In this paper we propose that, in developing knowledge based systems, knowledge engineering addresses two tasks. There is a problem analysis task that produces the problem representation and there is the task of developing the inference mechanism. CBR has an impact on the second of these tasks but helps less with the first. We argue that in some domains this problem analysis process can be significant and propose an iterative methodology for addressing it. To evaluate this, we describe the application of case-based reasoning to the problem of aircraft conflict resolution in a system called ISAC. We describe the application of this iterati...
http://hdl.handle.net/2262/13018
Marked
Mark
A neural network approach to auditory streaming
(1999)
Dicke, Ulrike
A neural network approach to auditory streaming
(1999)
Dicke, Ulrike
Abstract:
TCD-CS-1999-39
This paper suggests a neural network architecture for the streaming of an acoustic input using fundamental grouping cues of the mammalian auditory system. The auditory periphery is simulated using biologi- cally relevant models, and the primary auditory cortex is represented by a neural network layer, in which three di#11;erent features of the input are mapped and streamed. Finally, an attentional searchlight that is driven by the cortical network neurons, focuses on a selected cortical area representing a listener who focuses their attention on a distinct acoustic input.
http://hdl.handle.net/2262/13019
Marked
Mark
Adaptation as a Selection Constraint On Analogical Mapping
(1993)
Keane, Mark T.
Adaptation as a Selection Constraint On Analogical Mapping
(1993)
Keane, Mark T.
Abstract:
TCD-CS-93-28
In any given analogy, there are potentially a large number of possible mapping interpretations. One of the key issues in analogy research is how one of these mappings comes to be selected as optimal and used as the basis for the analogical comparison. It is well-established that structural factors, notably systematicity, can act as selection constraints on mapping. The present work tests to see if pragmatic and adaptation factors can also act as selection constraints on mapping. The selection of a mapping based on pragmatic factors proposes that people can exploit the higher-order, schematic structure of a domain to select one mapping over another. With respect to adaptation factors, the proposal is that a mapping will be selected if it is evaluated as being easily adapted relative to other competing mappings. Both of these predictions are tested in a novel, problem solving paradigm. The main finding is that adaptation factors do act as a selection constraint but th...
http://hdl.handle.net/2262/12941
Marked
Mark
Design Synthesis: A Model of Hierarchical Case-Based Reasoning
(1993)
Smyth, Barry; Finn, Donal; Keane, Mark T.
Design Synthesis: A Model of Hierarchical Case-Based Reasoning
(1993)
Smyth, Barry; Finn, Donal; Keane, Mark T.
Abstract:
TCD-CS-93-29
A variety of artificial intelligence techniques have been used in attempts to automate design synthesis tasks. Two common approaches are case-based and decompositional design. While powerful techniques in their own right, their integration has lead to a new generation of design synthesis systems capable of tackling a larger range of problems with greater effectiveness. In this paper previous attempts at integrating these approaches are examined in a number of design systems. Although significant advances have been made, important shortcomings still exist. The main focus of this paper is to address the limitations of these design synthesis models. To this end Deja Vu, a new hybrid model of design synthesis, has been developed and is described. Deja Vu integrates decompositional and case-based approaches in a framework that exploits the power of experiential knowledge, and benefits from far greater domain applicability when compared to existing design techniques. Two ...
http://hdl.handle.net/2262/12942
Marked
Mark
Retrieval Issues in Real-World CBR Applications How far can we go with discrimination-nets?
(1992)
Cunningham, Padraig; Smyth, Barry; Finn, Donal; Cahill, Eamonn
Retrieval Issues in Real-World CBR Applications How far can we go with discrimination-nets?
(1992)
Cunningham, Padraig; Smyth, Barry; Finn, Donal; Cahill, Eamonn
Abstract:
TCD-CS-92-37
In this paper we present a proposition and ponder a question. We propose that a useful perspective on analogical reasoning and CBR is to consider them on a continuum of abstraction of remindings. This is an alternative to the conventional view where CBR and analogical reasoning are seen as separate endeavours with analogical reasoning dealing with remindings between domains and CBR concerned with remindings within one domain. The question is how far towards the abstract end of the continuum can the index-based retrieval techniques that are effective in CBR be used (eg. discrimination networks). We are considering episode retrieval as a two stage process; the first stage being the initial filtering of the case base, and the second stage selecting the best case from this candidate set. We focus on the base filtering stage and conclude that discrimination networks are adequate for quite complex applications. However, problems arise when the system is required to suppor...
http://hdl.handle.net/2262/12758
Marked
Mark
An Object-Oriented Approach for Replication Management
(1992)
Gourhant, Yvon
An Object-Oriented Approach for Replication Management
(1992)
Gourhant, Yvon
Abstract:
TCD-CS-92-20
http://hdl.handle.net/2262/12943
Marked
Mark
Advanced Image Understanding and Autonomous Systems
(1992)
Vernon, David
Advanced Image Understanding and Autonomous Systems
(1992)
Vernon, David
Abstract:
TCD-CS-92-05
The ultimate goal of most image understanding systems is to produce an unambiguous 3-D representation of the local visual environment. This representation can then be employed by robotic systems to effect some meaningful action. A great deal of research effort is concerned with the development of visual and 'manipulative' representations, and their generative processes, which allow for the effective linking of such visual perception and robotic action. Part of the motivation for this effort is the desire to develop autonomous systems. It is argued in this paper that the requirements for the development of autonomous systems are not fully compatible with the current representation-based A.I. paradigm. While this approach is ideal for the construction of goal-oriented systems which function in environments that can be speci ed a priori, it does not, and cannot, address the problems encountered when adaptive, self-determining, autonomous systems are required....
http://hdl.handle.net/2262/12779
Marked
Mark
A Comparison of Model-Based and Incremental Case-Based Approaches to Electronic Fault Diagnosis
(1994)
Cunningham, Padraig; Smyth, Barry
A Comparison of Model-Based and Incremental Case-Based Approaches to Electronic Fault Diagnosis
(1994)
Cunningham, Padraig; Smyth, Barry
Abstract:
TCD-CS-94-21
CBR seems well suited to fault diagnosis because diagnostic episodes naturally form cases and much of expert competence seems to be based on reuse of old solutions. However, in many diagnosis problems it is difficult to compile a complete case description in advance, consequently the conventional one-shot case retrieval methodology will not work. In this paper we introduce a set of fault diagnosis problems that have this characteristic and we describe a model-based goal-driven system that produces focused questions that request extra information required for diagnosis. The central contribution in this paper is a description of a CBR system that also has this characteristic of producing focused questions in diagnosis. We describe the information theoretic mechanism that allows the CBR system to do this and we present an evaluation of the CBR system and a comparison of the two systems.
http://hdl.handle.net/2262/12780
Marked
Mark
Retrieving Reusable Design Cases: Exploiting Adaptation Knowledge in Design Reuse
(1994)
Smyth, Barry; Keane, Mark T.
Retrieving Reusable Design Cases: Exploiting Adaptation Knowledge in Design Reuse
(1994)
Smyth, Barry; Keane, Mark T.
Abstract:
TCD-CS-94-15
Case-based reasoning has been identified as a potentially fruitful candidate technology with which to investigate the development of automated design systems. Two critical stages in case-based design are design retrieval and design adaptation. In the former, designs that can be reused for a new design problem must be located. In the latter, retrieved designs must be modified to meet the specific demands of the new target situation. In this paper we will address both of these stages in the context of a case-based software design system called Deja Vu. In particular, it will be argued that the notions of design reusability and adaptability are intricately linked and an approach will be described which allows the adaptation requirements of design cases to be accurately predicted during retrieval and subsequently exploited during adaptation. We argue that this approach benefits from improved retrieval accuracy, exibility, and greater overall problem solving effcacy.
http://hdl.handle.net/2262/12783
Marked
Mark
Physical Model Generation in PDE Analysis using Model-based Case-based Reasoning
(1994)
Finn, Donal; Cunningham, Padraig
Physical Model Generation in PDE Analysis using Model-based Case-based Reasoning
(1994)
Finn, Donal; Cunningham, Padraig
Abstract:
TCD-CS-94-14
Model generation has emerged as a key task in engineering design and analysis. AI research in this area has focused on model based reasoning emphasising qualitative models in attempting to automate this process. In this paper, we propose that this work on the use of model based reasoning in model generation would benefit from the inclusion of case-based reasoning (CBR) techniques. We argue that the use of cases constrains the reasoning process as cases reflect known good routes in the solution space. Cases also have the advantage of facilitating the integration of heat transfer exemplars, approximations, formulae and correlations. In addition, much of human competence in this area is based on reusing solutions to previously solved problems and CBR emulates this. In the paper, we advance these arguments based on our experience with CoBRA, a CBR system for physical model generation for the domain of heat transfer described by partial differential equations.
http://hdl.handle.net/2262/12858
Marked
Mark
A Comparison of Incremental Case-Based Reasoning and Inductive Learning
(1994)
Smyth, Barry; Cunningham, Padraig
A Comparison of Incremental Case-Based Reasoning and Inductive Learning
(1994)
Smyth, Barry; Cunningham, Padraig
Abstract:
TCD-CS-94-13
This paper focuses on problems where the reuse of old solutions seems appropriate but the conventional CBR methodology is not adequate because a complete description of the new problem is not available to trigger case retrieval. We describe an information theoretic technique that solves this problem by producing focused questions to fill out the case description. This use of information theoretic techniques in CBR raises the question of whether a standard inductive learning approach would not solve this problem adequately. The main contribution of this paper is an evaluation of how this incremental case-based reasoning compares with a pure inductive learning approach to the same task.
http://hdl.handle.net/2262/12944
Marked
Mark
On the Limitations of Memory Based Reasoning
(1994)
Cunningham, Padraig; Smyth, Barry; Veale, Tony
On the Limitations of Memory Based Reasoning
(1994)
Cunningham, Padraig; Smyth, Barry; Veale, Tony
Abstract:
TCD-CS-94-12
Memory-Based Reasoning (MBR) represents a radical new departure in AI research. Whereas work in symbolic AI is based on inference and knowledge representation MBR depends on using a large memory of examples as a reasoning base. The MBR methodology is empirical so a typical system does not contain an explicit domain model. This means that MBR systems are quick to set up so the methodology shows considerable promise for knowledge based systems development. Indeed some impressive full scale systems have been demonstrated. In this paper we argue that despite this initial success there are considerable limitations to what can be achieved with MBR. We believe that the absence of a domain model means that MBR will not succeed in complex applications. We illustrate problems in natural language processing and planning that will require access to domain theories in their solution. Our conclusion is that the memory oriented philosophy of MBR has advantages but, for truly intel...
http://hdl.handle.net/2262/12945
Marked
Mark
Analogical Asides on Case-Based Reasoning
(1994)
Keane, Mark T.
Analogical Asides on Case-Based Reasoning
(1994)
Keane, Mark T.
Abstract:
TCD-CS-94-09
This paper explores some of the similarities and differences between cognitive models of analogy and case-based reasoning systems. I first point out a paradox in the treatment of adaptation in analogy and in case-based reasoning; a paradox which can be only resolved by expanding the role of adaptation in cognitive models of analogy. Some psychological research on the process of adaptation in human subjects is reported and then the implications of this research are propagated into analogy and then on into CBR. The argument is that some of the existing stages in CBR should be integrated into a more stream-lined architecture that would be more efficient than current schemes.
http://hdl.handle.net/2262/12946
Marked
Mark
Epistemological Pitfalls in Metaphor Comprehension: A Comparison of Three Models and a New Theory of Metaphor
(1995)
Veale, Tony; Keane, Mark T.
Epistemological Pitfalls in Metaphor Comprehension: A Comparison of Three Models and a New Theory of Metaphor
(1995)
Veale, Tony; Keane, Mark T.
Abstract:
TCD-CS-95-25
[Introduction] If metaphor is to be viewed as a fundamental cognitive agency, as recent work suggests, what ramifications does this view have for a model of semantic memory? This paper presents a computational treatment of metaphor comprehension, named Sapper (see Veale & Keane 1993, 1994), which is built upon a parallel, adaptive, and learning network model of semantic memory. Sapper is a hybrid symbolic/connectionist model which views the interpretation of novel metaphors as a process of connectionist bridge-building, a process which subsequently alters the activation dynamics between different conceptual schemata in semantic memory, thereby causing these schemata to interact (following Black, 1962) in a representationally dynamic fashion. Sapper employs a bottom-up approach to metaphor comprehension, one which encourages the existing structure of semantic memory to shape and accommodate the most natural interpretation for each concept juxtaposition. In this w...
http://hdl.handle.net/2262/12859
Marked
Mark
A detailed derivation of the relationship between generalization error and ambiguity in regression ensembles
(1999)
Zenobi, Gabriele
A detailed derivation of the relationship between generalization error and ambiguity in regression ensembles
(1999)
Zenobi, Gabriele
Abstract:
TCD-CS-1999-76
In this technical report we will show the complete sequence of steps for the derivation of the equation of the Ensemble-Error E = E - A , introduced in the paper by Krogh and Vedelsby [1], that describes the error E of an ensemble of networks related to the average error E of the single network and the Ambiguity A of the ensemble, which in turn is a measure of the ?disagreement? among the networks.
http://hdl.handle.net/2262/13040
Marked
Mark
Discovering genome expression patterns with self-organizing neural networks
(2002)
Azuaje, Francisco
Discovering genome expression patterns with self-organizing neural networks
(2002)
Azuaje, Francisco
Abstract:
TCD-CS-2002-30
Self-organizing neural networks represent a family of useful clusteringbased classification methods in several application domains. One such technique is the Kohonen Self-Organizing Feature Map (SOM) (Kohonen, 2001), which has become one of the most successful approaches to analysing genomic expression data. This model is relatively easy to implement and evaluate, computationally inexpensive and scalable. In addition, it exhibits significant advantages in comparison to other options. For instance, unlike hierarchical clustering it facilitates an automatic detection and inspection of clusters. Unlike Bayesian-based clustering it does not require prior hypotheses or knowledge about the data under consideration. Compared to the k-means clustering algorithm, the SOM exemplifies a robust and structured classification process. - [Introduction]
http://hdl.handle.net/2262/2558
Marked
Mark
Efficient Prediction-Based Validation for Document Clustering
(2006)
Greene, Derek; Cunningham, Padraig
Efficient Prediction-Based Validation for Document Clustering
(2006)
Greene, Derek; Cunningham, Padraig
Abstract:
TCD-CS-2006-22
Recently, stability-based techniques have emerged as a very promising solution to the problem of cluster validation. An inherent drawback of these approaches is the computational cost of generating and assessing multiple clusterings of the data. In this paper we present an efficient prediction-based validation approach suitable for application to large, high-dimensional datasets such as text corpora. We use kernel clustering to isolate the validation procedure from the original data. Furthermore, we employ a prototype reduction strategy that allows us to work on a reduced kernel matrix, leading to significant computational savings. To ensure that this condensed representation accurately reflects the cluster structures in the data, we propose a density-biased selection strategy. This novel validation process is evaluated on a large number of real and artificial datasets, where it is shown to consistently produce good estimates for the optimal number of clusters.
http://hdl.handle.net/2262/13501
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