Artificial Intelligence and Robotics Commons^™

Automatic Annotation Of Referring Expression In Situated Dialogues, Niels Schütte, John D. Kelleher, Brian Mac Namee

Articles

To apply machine learning techniques to the production and interpretation of natural language, we need large amounts of annotated language data. Manual annotation, however, is an expensive and time consuming process since it involves human annotators looking at the data and explicitly adding information that is implicitly contained in the data, based on their judgment. This work presents an approach to automatically annotating referring expressions in situated dialogues by exploiting the interpretation of language by the participants in the dia- logue. We associate instructions concerning objects in the environment with automatically detected events involving these objects and predict the referents …

Feasibility Study Of Utility-Directed Behaviour For Computer Game Agents, Colm Sloan, John D. Kelleher, Brian Mac Namee

Conference papers

Utility-based control (UBC) hasn’t been widely adopted for commercial game AI. Some of the reasons for this are that UBC is perceived to be: (1) resource intensive, (2) difficult to design complex behaviours with, and (3) difficult to scale for use in complex environments. This paper investigates these perceptions to see if UBC is suitable for controlling the behaviour of non-player characters in commercial games. The investigation compares agents using a UBC system against two control systems that are more frequently used in commercial games: finite state machines (FSMs), considered a simple control system, and goal-oriented action planning (GOAP), considered …

Visual Salience And Reference Resolution In Situated Dialogues: A Corpus-Based Evaluation., Niels Schütte, John D. Kelleher, Brian Mac Namee

Conference papers

Dialogues between humans and robots are necessarily situated and so, often, a shared visual context is present. Exophoric references are very frequent in situated dialogues, and are particularly important in the presence of a shared visual context - for example when a human is verbally guiding a tele-operated mobile robot. We present an approach to automatically resolving exophoric referring expressions in a situated dialogue based on the visual salience of possible referents. We evaluate the effectiveness of this approach and a range of different salience metrics using data from the SCARE corpus which we have augmented with visual information. The …

Situating Spatial Templates For Human-Robot Interaction, John D. Kelleher, Robert J. Ross, Brian Mac Namee, Colm Sloan

Conference papers

People often refer to objects by describing the object's spatial location relative to another object. Due to their ubiquity in situated discourse, the ability to use 'locative expressions' is fundamental to human-robot dialogue systems. A key component of this ability are computational models of spatial term semantics. These models bridge the grounding gap between spatial language and sensor data. Within the Artificial Intelligence and Robotics communities, spatial template based accounts, such as the Attention Vector Sum model (Regier and Carlson, 2001), have found considerable application in mediating situated human-machine communication (Gorniak, 2004; Brenner et a., 2007; Kelleher and Costello, 2009). …

Topology In Composite Spatial Terms, John D. Kelleher, Robert J. Ross

Conference papers

People often refer to objects by describing the object's spatial location relative to another object, e.g. the book on the right of the table. This type of referring expression is called a spatial locative expression. Spatial locatives have three major components: (1) the target object that is being located (the book), (2) the landmark object relative to which the target is being located (the table), and (3) the description of the spatial relationship that exists between the target and the landmark (on the right of ). In English spatial relationships are often described using spatial prepositions. The set of English …

Proceedings Of The Sixth International Natural Language Generation Conference (Inlg 2010)., John D. Kelleher, Brian Mac Namee, Ielka Van Der Sluis

Conference papers

No abstract provided.

Handling Concept Drift In Text Data Stream Constrained By High Labelling Cost, Patrick Lindstrom, Sarah Jane Delany, Brian Mac Namee

Conference papers

In many real-world classification problems the concept being modelled is not static but rather changes over time - a situation known as concept drift. Most techniques for handling concept drift rely on the true classifications of test instances being available shortly after classification so that classifiers can be retrained to handle the drift. However, in applications where labelling instances with their true class has a high cost this is not reasonable. In this paper we present an approach for keeping a classifier up-to-date in a concept drift domain which is constrained by a high cost of labelling. We use …

The Extent Of Clientelism In Irish Politics: Evidence From Classifying Dáil Questions On A Local-National Dimension, Sarah Jane Delany, Richard Sinnott, Niall O'Reilly

Conference papers

The availability of the full text of Irish parliamentary questions offers opportunities for using machine learning techniques to examine the currently much discussed role of elected representatives (TDs) in the Irish parliamentary system. Bluntly, are TDs mainly national legislators or “constituency messenger boys”? This paper presents an initial investigation into the use of automated text classification techniques to categorise parliamentary questions from 1922 up to 2008 as national or local. The approach uses a bag of words representation, standard feature reduction methods and an SVM classifier. Initial results show there is very little evidence in the corpus of parliamentary questions …

Motion In Augmented Reality Games: An Engine For Creating Plausible Physical Interactions In Augmented Reality Games, Brian Mac Namee, David Beaney, Qingqing Dong

Articles

The next generation of Augmented Reality (AR) games will require real and virtual objects to coexist in motion in immersive game environments. This will require the illusion that real and virtual objects interact physically together in a plausible way. The Motion in Augmented Reality Games (MARG) engine described in this paper has been developed to allow these kinds of game environments. The paper describes the design and implementation of the MARG engine and presents two proof-of-concept AR games that have been developed using it. Evaluations of these games have been performed and are presented to show that the MARG engine …

Cbtv: Visualising Case Bases For Similarity Measure Design And Selection, Brian Mac Namee, Sarah Jane Delany

Conference papers

In CBR the design and selection of similarity measures is paramount. Selection can benefit from the use of exploratory visualisation- based techniques in parallel with techniques such as cross-validation ac- curacy comparison. In this paper we present the Case Base Topology Viewer (CBTV) which allows the application of different similarity mea- sures to a case base to be visualised so that system designers can explore the case base and the associated decision boundary space. We show, using a range of datasets and similarity measure types, how the idiosyncrasies of particular similarity measures can be illustrated and compared in CBTV allowing …

Inside The Selection Box: Visualising Active Learning Selection Strategies, Brian Mac Namee, Rong Hu, Sarah Jane Delany

Conference papers

Visualisations can be used to provide developers with insights into the inner workings of interactive machine learning techniques. In active learning, an inherently interactive machine learning technique, the design of selection strategies is the key research question and this paper demonstrates how spring model based visualisations can be used to provide insight into the precise operation of various selection strategies. Using sample datasets, this paper provides detailed examples of the differences between a range of selection strategies.

Exploring The Frontier Of Uncertainty Space, Rong Hu, Patrick Lindstrom, Sarah Jane Delany, Brian Mac Namee

Conference papers

We aim to investigate methods balancing exploitation with exploration in active learning to improve the performance of uncertainty sampling. Two exploration guided sampling methods are compared to uncertainty sampling on various real-life datasets from the 2010 Active Learning Challenge. Our initial experiments seems to indicate that combining exploration with uncertainty sampling improves performance on certain datasets but not all.

Svm Based Active Learning With Exploration, Patrick Lindstrom, Rong Hu, Sarah Jane Delany, Brian Mac Namee

Conference papers

No abstract provided.

Egal: Exploration Guided Active Learning For Tcbr, Rong Hu, Sarah Jane Delany, Brian Mac Namee

Conference papers

The task of building labelled case bases can be approached using active learning (AL), a process which facilitates the labelling of large collections of examples with minimal manual labelling effort. The main challenge in designing AL systems is the development of a selection strategy to choose the most informative examples to manually label. Typical selection strategies use exploitation techniques which attempt to refine uncertain areas of the decision space based on the output of a classifier. Other approaches tend to balance exploitation with exploration, selecting examples from dense and interesting regions of the domain space. In this paper we present …

Cognitive Effort For Multi Agent Systems, Luca Longo

Conference papers

Cognitive Effort is a multi-faceted phenomenon that has suffered from an imperfect understanding, an informal use in everyday life and numerous definitions. This paper attempts to clarify the concept, along with some of the main influencing factors, by presenting a possible heuristic formalism intended to be implemented as a computational concept, and therefore be embedded in an artificial agent capable of cognitive effort-based decision support. Its applicability in the domain of Artificial Intelligence and Multi-Agent Systems is discussed. The technical challenge of this contribution is to start an active discussion towards the formalisation of Cognitive Effort and its application in …

A Context-Aware Approach Based On Self-Organizing Maps To Study Web-Users' Tendencies From Their Behaviour, Luca Longo, Stephen Barrett

Conference papers

In the context of a highly volatile web of uneven quality, the identification of content deemed valuable by end users is of paramount importance. Where page content undergoes rapid change, this issue is particularly challenging. Web browsing activity represents a unique source of context by which the value of web pages can be determined via an assessment of individual user interactions, such as scrolling, clicking, saving and so forth. Over time, this data set forms a pattern of activity which can be mined for meaning. In this paper we present an approach to web content, based on Kohonen mapping, used …

Applying Computational Models Of Spatial Prepositions To Visually Situated Dialog, John D. Kelleher, Fintan Costello

Articles

This article describes the application of computational models of spatial prepositions to visually situated dialog systems. In these dialogs, spatial prepositions are important because people often use them to refer to entities in the visual context of a dialog. We first describe a generic architecture for a visually situated dialog system and highlight the interactions between the spatial cognition module, which provides the interface to the models of prepositional semantics, and the other components in the architecture. Following this, we present two new computational models of topological and projective spatial prepositions. The main novelty within these models is the fact …

The Good, The Bad And The Incorrectly Classified: Profiling Cases For Case-Base Editing, Sarah Jane Delany

Conference papers

Case-based approaches to classification, as instance-based learning techniques, have a particular reliance on training examples that other supervised learning techniques do not have. In this paper we present the RDCL case profiling technique that categorises each case in a case-base based on its classification by the case-base, the benefit it has and/or the damage it causes by its inclusion in the case-base. We show how these case profiles can identify the cases that should be removed from a case-base in order to improve generalisation accuracy and we show what aspects of existing noise reduction algorithms contribute to good performance and …

Widening The Evaluation Net, Brian Mac Namee, Mark Dunne

Conference papers

Intelligent Virtual Agent (IVA) systems are notoriously difficult to evaluate, particularly due to the subjectivity involved. From the various efforts to develop standard evaluation schemes for IVA systems the scheme proposed by Isbister & Doyle, which evaluates systems across five categories, seems particularly appropriate. To examine how these categories are being used, the evaluations presented in the proceedings of IVA '07 and IVA '08 are summarised and the extent to which the five categories in the Isbister & Doyle scheme are used is highlighted. Finally, to illustrate how the full scheme can be used, an evaluation of an IVA system …

Stepping Off The Stage, Brian Mac Namee, John D. Kelleher

Conference papers

Mixed-reality virtual agents are an attractive solution to the problems associated with human-robot interaction, allowing all the expressiveness of virtual characters to be married with the advantages of a physical artifact which exists in a shared environment with the user. However, common approaches to achieving this restrict the virtual characters appearing on top of, or encompassing the robot. This paper describes the Stepping Off the Stage system in which mixed-reality agents are allowed to step off the robot stage and move to other parts of the environment, offering compelling new interaction possibilities.

Sampling With Confidence: Using K-Nn Confidence Measures In Active Learning, Rong Hu, Sarah Jane Delany, Brian Macnamee

Conference papers

Active learning is a process through which classifiers can be built from collections of unlabelled examples through the cooperation of a human oracle who can label a small number of examples selected as most informative. Typically the most informative examples are selected through uncertainty sampling based on classification scores. However, previous work has shown that, contrary to expectations, there is not a direct relationship between classification scores and classification confidence. Fortunately, there exists a collection of particularly effective techniques for building measures of classification confidence from the similarity information generated by k-NN classifiers. This paper investigates using these confidence measures …

Forked:A Demonstration Of Physics Realism In Augmented Reality, David Beaney, Brian Mac Namee

Conference papers

In making fully immersive augmented reality (AR) applications, real and virtual objects will have to be seen to physically interact together in a realistic and believable way. This paper describes Forked! a system that has been developed to show how physical interactions between real and virtual objects can be simulated re- alistically and believably through appropriate use of a physics en- gine. The system allows users control a robotic forklift to manipu- late virtual crates in an AR environment. The paper also describes a evaluation experiment in which it is shown that the physical inter- actions between the forklift and …

Referring Expression Generation Challenge 2008 Dit System Descriptions (Dit-Fbi, Dit-Tvas, Dit-Cbsr, Dit-Rbr, Dit-Fbi-Cbsr, Dit-Tvas-Rbr), John D. Kelleher, Brian Mac Namee

Conference papers

This papers desibes a set of systems developed at DIT for the Referring Expression Generation challenage at INLG 2008.In Proceedings of the 5th International Natural Language Generation Conference (INLG-08)

A Translation Mechanism For Recommendations, Pierpaolo Dondio, Luca Longo, Stephen Barrett

Conference papers

An important class of distributed Trust-based solutions is based on the information sharing. A basic requirement of such systems is the ability of participating agents to effectively communicate, receiving and sending messages that can be interpreted correctly. Unfortunately, in open systems it is not possible to postulate a common agreement about the representation of a rating, its semantic meaning and cognitive and computational mechanisms behind a trust-rating formation. Social scientists agree to consider unqualified trust values not transferable, but a more pragmatic approach would conclude that qualified trust judgments are worth being transferred as far as decisions taken considering others’ …

Medical Language Processing For Patient Diagnosis Using Text Classification And Negation Labelling, Brian Mac Namee, John D. Kelleher, Sarah Jane Delany

Conference papers

This paper describes the approach of the DIT AIGroup to the i2b2 Obesity Challenge to build a system to diagnose obesity and related co-morbidities from narrative, unstructured patient records. Based on experimental results a system was developed which used knowledge-light text classification using decision trees, and negation labelling.

Object Detection And Classification With Applications To Skin Cancer Screening, Jonathan Blackledge, Dmitryi Dubovitskiy

Articles

This paper discusses a new approach to the processes of object detection, recognition and classification in a digital image. The classification method is based on the application of a set of features which include fractal parameters such as the Lacunarity and Fractal Dimension. Thus, the approach used, incorporates the characterisation of an object in terms of its texture.

The principal issues associated with object recognition are presented which includes two novel fast segmentation algorithms for which C++ code is provided. The self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and …

Temporal Factors To Evaluate Trustworthiness Of Virtual Identities, Luca Longo, Pierpaolo Dondio, Stephen Barrett

Conference papers

In this paper we investigate how temporal factors (i.e. factors computed by considering only the time-distribution of interactions) can be used as an evidence of an entity’s trustworthiness. While reputation and direct experience are the two most widely used sources of trust in applications, we believe that new sources of evidence and new applications should be investigated [1]. Moreover, while these two classical techniques are based on evaluating the outcomes of interactions (direct or indirect), temporal factors are based on quantitative analysis, representing an alternative way of assessing trust. Our presumption is that, even with this limited information, temporal factors …

Proceedings Of The 18th Irish Conference On Artificial Intelligence And Cognitive Science, Sarah Jane Delany, Michael Madden

Books/Book Chapters

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI).

Proceedings Of The 4th Acl-Sigsem Workshop On Prepositions At Acl-2007., Fintan Costello, John D. Kelleher, Martin Volk

Conference papers

This volume contains the papers presented at the Fourth ACL-SIGSEM Workshop on Prepositions. This workshop is endorsed by the ACL Special Interest Group on Semantics (ACL-SIGSEM), and is hosted in conjunction with ACL 2007, taking place on 28th June, 2007 in Prague, the Czech Republic.

Using Computer Vision To Create A 3d Representation Of A Snooker Table For Televised Competition Broadcasting, Hao Guo, Brian Mac Namee

Conference papers

The Snooker Extraction and 3D Builder (SE3DB) is designed to be used as a viewer aid in televised snooker broadcasting. Using a single camera positioned over a snooker table, the system creates a virtual 3D model of the table which can be used to allow audiences view the table from any angle. This would be particularly useful in allowing viewers to determine if particular shots are possible or not. This paper will describe the design, development and evaluation of this system. Particular focus in the paper will be given to the techniques used to recognise and locate the balls on …