Ontologies Supporting Intelligent Agent-Based Assistance

Intelligent agent-based assistants are systems that try to simplify peoples work based on computers. Recent research on intelligent assistance has presented significant results in several and different situations. Building such a system is a difficult task that requires expertise in numerous artificial intelligence and engineering disciplines. A key point in this kind of system is knowledge handling. The use of ontologies for representing domain knowledge and for supporting reasoning is becoming wide-spread in many areas, including intelligent assistance. In this paper we present how ontologies can be used to support intelligent assistance in a multi-agent system context. We show how ontologies may be spread over the multi-agent system architecture, highlighting their role controlling user interaction and service description. We present in detail an ontology-based conversational interface for personal assistants, showing how to design an ontology for semantic interpretation and how the interpretation process uses it for semantic analysis. We also present how ontologies are used to describe decentralized services based on a multi-agent architecture

Improving Knowledge Acquisition in Collaborative Knowledge Construction Tool with Virtual Catalyst

Noctua is a web tool to assist in Knowledge Acquisition and Collaborative Knowledge Construction processes. Noctua has an innovation: a Virtual Catalyst designed to facilitate the task of eliciting and validating knowledge. The Virtual Catalyst queries participants, proposing new knowledge, seeking confirmation to the knowledge already elicited, and showing conflicting opinions. The Virtual Catalyst takes into account participants' profiles in order to automatically ask them questions related to each one's field of knowledge or interest. This paper presents Noctua and its Virtual Catalyst. The tool was submitted to experimentation and the analysis of the results showed that the primary goal of increasing the rate of knowledge construction was achieved (up to 144 % in the rate of knowledge creation), and also showed some unexpected beneficial outcomes

ICAI (une interface conversationnelle pour une aide intelligente)

Cette thèse concerne la conception d'une interface conversationnelle pour les agents assistants personnels. Nous avons focalisé notre recherche sur la définition et la conception d'une interface conversationnelle pour un agent assistant, en tenant compte de plusieurs contraintes et hypothèses liées à l'agent lui-même, à son pilotage et à la nature du rapport utilisateur-agent, mais aussi aux caractéristiques des applications potentielles. Nous estimons qu'une interface conversationnelle simplifiera son pilotage, entraînant une amélioration de la qualité de l'aide. De notre travail est né le concept d'interface conversationnelle pour une aide intelligente - ICA. Une ICAI est le résultat de la conjonction d'un mécanisme conversationnel en langage naturel parlé et de la gestion intelligente de ce mécanisme, permettant le déroulement d'un dialogue coopératif, et capable de gérer le déclenchement de plusieurs tâches à la demande de l'utilisateur.This thesis concerns the design of a conversational interface for personal assistant agents. We studied the typical user interface used in personal assistants. We conclude that conversational interfaces are the one especially suitable for personal assistants. To design the conversational interface, we took in account several constraints related to the agent itself and the potential applications. As the result of this approach we expect to improve the quality of assistance and to reduce the user's cognitive load. ln this thesis we present a new concept called ICAI: A Conversational Interface for an Intelligent Assistance. An ICAI is the result of the conjunction of a conversational spoken natural language mechanism and an intelligent management of such mechanism, enabling a cooperative dialogue between the user and the assistant agent. Our approach is based on ontologies that allow us to interpret messages from human and artificial agents.COMPIEGNE-BU (601592101) / SudocSudocFranceF

Optimizing Profit by Mitigating Recurrent Churn Labeling Issues: Analysis from the Game Domain

Churn can be interpreted as customer defection and can be considered one of the most critical challenges in the Game Analytics domain because of its impact on the game industry's profit. When predicting churn, the first step is defining what is considered churn, which can change depending on the players' behaviors and approaches. This work studied related works and revealed two recurrent issues in the labeling process: limitations on the adopted labeling approaches (1) and the static definition of churn (2). To mitigate the first issue, an individualized labeling approach was deployed. To address the second one, a novel evaluation method, based on the impact of a change in the churn definition, was proposed. This method allowed the proposition of two new labeling approaches, which were included in the analysis. By comparing the labeling approaches in two games using a profit perspective, it was identified that the new ones present statistically significant benefits compared to the traditional ones. Regarding the evaluation method, its usage can justify when the redefinition of churn and the classifier's retraining should happen to improve profit. The results are valuable for the game context, potentially extended to other contexts by delivering more reliable labels and more validated classification performance

E-mail address: HSDUDLVR#KGV XWF IU Université de Technologie de Compiègne

Groupware and collaborative tools have been proposed to support cooperative work. However, it was found that they suffer from some rather severe limitations. Alternatively, multi-agent systems have been developed to improve the situation. In the latter case, the user is normally interfaced through a special agent called a personal assistant. In this paper, we describe the design of an ontology-based speech interface for personal assistants applied in the context of cooperative projects. We believe that this type of interface will improve the quality of assistance. We present the interface and its insertion into a multi-agent system designed for research and development projects. We describe the design of the interface, highlighting the role of ontologies for semantic interpretation. As a result of this conversational speech interface, we expect an increase in the quality of assistance and a reduction in the time needed to answer user’s requests

CSCW in Software Development: Collaboration among Humans and Artificial Agents through Dialogs

International audienceIn this paper, we discuss the construction of dialogs for Personal Assistant Agents that are in charge of the interface between users and a Multi-Agent System. Such a system aims at providing support for small teams developing software collaboratively. Small teams have specific needs such as the integration of free or open-source tools or the support to elaborate project documentation. Considering such specific needs, we have elaborated an architecture implemented using a Multi-Agent platform. We present the structure offered by the platform for handling dialogs with users and discuss some implementation details. We also give examples of the dialogs that represent interactions between members of small software development teams and their Personal Assistant Agents. We consider that the use of Personal Assistant Agents can help small teams handle documentation issues in an integrated and undemanding way

MBA: A system of systems architecture model for supporting collaborative work

International audienc

A collaborative virtual workspace for software development.

International audienc

Temporal Relation Extraction in Clinical Texts

International audienceUnstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning–based models with few recent initiatives to push the state-of-the-art with deep learning–based models. For temporal relations between entities (event and temporal expressions) in the document, factors such as dataset imbalance because of candidate pair generation and task complexity directly affect the system's performance. The state-of-the-art resides on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. However, further experiments and advances in the research topic are required until real-time clinical domain applications are released. Furthermore, most of the publications mainly reside on the same dataset, hindering the need for new annotation projects that provide datasets for different medical specialties, clinical text types, and even languages