Designing Interfaces to Support Collaboration in Information Retrieval

Information retrieval systems should acknowledge the existence of collaboration in the search process. Collaboration can help users to be more effective in both learning systems and in using them. We consider some issues of viewing interfaces to information retrieval systems as collaborative notations and how to build systems that more actively support collaboration. We describe a system that embodies just one kind of explicit support; a graphical representation of the search process that can be manipulated and discussed by the users. By acknowledging the importance of other people in the search process, we can develop systems that not only improve help-giving by people but which can lead to a more robust search activity, more able to cope with, and indeed exploit, the failures of any intelligent agents used

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Understanding Computer Role-Playing Games: A Genre Analysis Based on Gameplay Features in Combat Systems

A game genre as diverse as that of computer role-playing games is difficult to overview. This poses challenges or both developers and researchers to position their work clearly within the genre. We present an overview of the genre based on clustering games with similar gameplay features. This allows a tracing of relations between subgenres through their gameplay, and connecting this to concrete game examples. The analysis was done through using gameplay design patterns to identify gameplay features and focused upon the combat systems in the games. The resulting cluster structure makes use of 321 patterns to create 37 different subgenre classifications based solely on gameplay features. In addition to the clusters, we identify four categories of patterns that help designers and researchers understand the combat systems in computer role-playing games

A Review on Cooperative Question-Answering Systems

The Question-Answering (QA) systems fall in the study area of Information Retrieval (IR) and Natural Language Processing (NLP). Given a set of documents, a QA system tries to obtain the correct answer to the questions posed in Natural Language (NL). Normally, the QA systems comprise three main components: question classification, information retrieval and answer extraction. Question classification plays a major role in QA systems since it classifies questions according to the type in their entities. The techniques of information retrieval are used to obtain and to extract relevant answers in the knowledge domain. Finally, the answer extraction component is an emerging topic in the QA systems. This module basically classifies and validates the candidate answers. In this paper we present an overview of the QA systems, focusing on mature work that is related to cooperative systems and that has got as knowledge domain the Semantic Web (SW). Moreover, we also present our proposal of a cooperative QA for the SW

Personalizing Interactions with Information Systems

Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialog-based applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the user’s mental model with the underlying information system’s organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains

Proceedings of the 11th European Agent Systems Summer School Student Session

This volume contains the papers presented at the Student Session of the 11th European Agent Systems Summer School (EASSS) held on 2nd of September 2009 at Educatorio della Providenza, Turin, Italy. The Student Session, organised by students, is designed to encourage student interaction and feedback from the tutors. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector. Table of Contents: Andrew Koster, Jordi Sabater Mir and Marco Schorlemmer, Towards an inductive algorithm for learning trust alignment . . . 5; Angel Rolando Medellin, Katie Atkinson and Peter McBurney, A Preliminary Proposal for Model Checking Command Dialogues. . . 12; Declan Mungovan, Enda Howley and Jim Duggan, Norm Convergence in Populations of Dynamically Interacting Agents . . . 19; Akın Günay, Argumentation on Bayesian Networks for Distributed Decision Making . . 25; Michael Burkhardt, Marco Luetzenberger and Nils Masuch, Towards Toolipse 2: Tool Support for the JIAC V Agent Framework . . . 30; Joseph El Gemayel, The Tenacity of Social Actors . . . 33; Cristian Gratie, The Impact of Routing on Traffic Congestion . . . 36; Andrei-Horia Mogos and Monica Cristina Voinescu, A Rule-Based Psychologist Agent for Improving the Performances of a Sportsman . . . 39; --Autonomer Agent,Agent,Künstliche Intelligenz

GuessWhat?! Visual object discovery through multi-modal dialogue

We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.Comment: 23 pages; CVPR 2017 submission; see https://guesswhat.a

The non-Verbal Structure of Patient Case Discussions in Multidisciplinary Medical Team Meetings

Meeting analysis has a long theoretical tradition in social psychology, with established practical rami?cations in computer science, especially in computer supported cooperative work. More recently, a good deal of research has focused on the issues of indexing and browsing multimedia records of meetings. Most research in this area, however, is still based on data collected in laboratories, under somewhat arti?cial conditions. This paper presents an analysis of the discourse structure and spontaneous interactions at real-life multidisciplinary medical team meetings held as part of the work routine in a major hospital. It is hypothesised that the conversational structure of these meetings, as indicated by sequencing and duration of vocalisations, enables segmentation into individual patient case discussions. The task of segmenting audio-visual records of multidisciplinary medical team meetings is described as a topic segmentation task, and a method for automatic segmentation is proposed. An empirical evaluation based on hand labelled data is presented which determines the optimal length of vocalisation sequences for segmentation, and establishes the competitiveness of the method with approaches based on more complex knowledge sources. The effectiveness of Bayesian classi?cation as a segmentation method, and its applicability to meeting segmentation in other domains are discusse

The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

Argumentation dialogues in web-based GDSS: an approach using machine learning techniques

Tese de doutoramento em InformaticsA tomada de decisão está presente no dia a dia de qualquer pessoa, mesmo que muitas vezes ela não tenha consciência disso. As decisões podem estar relacionadas com problemas quotidianos, ou podem estar relacionadas com questões mais complexas, como é o caso das questões organizacionais. Normalmente, no contexto organizacional, as decisões são tomadas em grupo. Os Sistemas de Apoio à Decisão em Grupo têm sido estudados ao longo das últimas décadas com o objetivo de melhorar o apoio prestado aos decisores nas mais diversas situações e/ou problemas a resolver. Existem duas abordagens principais à implementação de Sistemas de Apoio à Decisão em Grupo: a abordagem clássica, baseada na agregação matemática das preferências dos diferentes elementos do grupo e as abordagens baseadas na negociação automática (e.g. Teoria dos Jogos, Argumentação, entre outras). Os atuais Sistemas de Apoio à Decisão em Grupo baseados em argumentação podem gerar uma enorme quantidade de dados. O objetivo deste trabalho de investigação é estudar e desenvolver modelos utilizando técnicas de aprendizagem automática para extrair conhecimento dos diálogos argumentativos realizados pelos decisores, mais concretamente, pretende-se criar modelos para analisar, classificar e processar esses dados, potencializando a geração de novo conhecimento que será utilizado tanto por agentes inteligentes, como por decisiores reais. Promovendo desta forma a obtenção de consenso entre os membros do grupo. Com base no estudo da literatura e nos desafios em aberto neste domínio, formulou-se a seguinte hipótese de investigação - É possível usar técnicas de aprendizagem automática para apoiar diálogos argumentativos em Sistemas de Apoio à Decisão em Grupo baseados na web. No âmbito dos trabalhos desenvolvidos, foram aplicados algoritmos de classificação supervisionados a um conjunto de dados contendo argumentos extraídos de debates online, criando um classificador de frases argumentativas que pode classificar automaticamente (A favor/Contra) frases argumentativas trocadas no contexto da tomada de decisão. Foi desenvolvido um modelo de clustering dinâmico para organizar as conversas com base nos argumentos utilizados. Além disso, foi proposto um Sistema de Apoio à Decisão em Grupo baseado na web que possibilita apoiar grupos de decisores independentemente de sua localização geográfica. O sistema permite a criação de problemas multicritério e a configuração das preferências, intenções e interesses de cada decisor. Este sistema de apoio à decisão baseado na web inclui os dashboards de relatórios inteligentes que são gerados através dos resultados dos trabalhos alcançados pelos modelos anteriores já referidos. A concretização de cada um dos objetivos permitiu validar as questões de investigação identificadas e assim responder positivamente à hipótese definida.Decision-making is present in anyone’s daily life, even if they are often unaware of it. Decisions can be related to everyday problems, or they can be related to more complex issues, such as organizational issues. Normally, in the organizational context, decisions are made in groups. Group Decision Support Systems have been studied over the past decades with the aim of improving the support provided to decision-makers in the most diverse situations and/or problems to be solved. There are two main approaches to implementing Group Decision Support Systems: the classical approach, based on the mathematical aggregation of the preferences of the different elements of the group, and the approaches based on automatic negotiation (e.g. Game Theory, Argumentation, among others). Current argumentation-based Group Decision Support Systems can generate an enormous amount of data. The objective of this research work is to study and develop models using automatic learning techniques to extract knowledge from argumentative dialogues carried out by decision-makers, more specifically, it is intended to create models to analyze, classify and process these data, enhancing the generation of new knowledge that will be used both by intelligent agents and by real decision-makers. Promoting in this way the achievement of consensus among the members of the group. Based on the literature study and the open challenges in this domain, the following research hypothesis was formulated - It is possible to use machine learning techniques to support argumentative dialogues in web-based Group Decision Support Systems. As part of the work developed, supervised classification algorithms were applied to a data set containing arguments extracted from online debates, creating an argumentative sentence classifier that can automatically classify (For/Against) argumentative sentences exchanged in the context of decision-making. A dynamic clustering model was developed to organize conversations based on the arguments used. In addition, a web-based Group Decision Support System was proposed that makes it possible to support groups of decision-makers regardless of their geographic location. The system allows the creation of multicriteria problems and the configuration of preferences, intentions, and interests of each decision-maker. This web-based decision support system includes dashboards of intelligent reports that are generated through the results of the work achieved by the previous models already mentioned. The achievement of each objective allowed validation of the identified research questions and thus responded positively to the defined hypothesis.I also thank to Fundação para a Ciência e a Tecnologia, for the Ph.D. grant funding with the reference: SFRH/BD/137150/2018