FANTASIA: a framework for advanced natural tools and applications in social, interactive approaches

Origlia, Antonio; Cutugno, Francesco; Rodà, Antonio; Cosi, Piero; Zmarich, Claudio

doi:10.1007/s11042-019-7362-5

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…In the literature, several works represented knowledge through graph databases in different domains of application. Origlia et al (2019) considered them very important for the realisation of interactive experiences, so much to be included in their framework FANTASIA. KBot (see Ait-Mlouk and Jiang ( 2020)) is a chatbot system based on knowledge graph database that leverages the technologies of machine learning and natural language understanding, including named entity recognition, factoid, and recurrent questions, as well as dialogue management, which improved the end-to-end user experience in terms of interactive question answering and performance.…”

Section: Related Workmentioning

confidence: 99%

Graph-based representations of clarification strategies supporting automatic dialogue management

Russo¹,

Mancini²,

Grazioso³

et al. 2022

ijcol

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This paper aims at presenting a dialogue-oriented approach to the construction of a graph knowledge base (KB) supporting task-oriented human-machine interactions. In particular, we focus on different pragmatic scenarios, facing the Common Ground issue and arguing that knowledge bases (in the form of graphs) are needed to make a clarification and recover pieces of information when inconsistencies occur during the communicative exchange.The main contributions of this work are: 1) a flexible dialog system architecture designed to be plugged into existing service infrastructures, 2) a graph-based knowledge representation protocol to manage both dialog domain and dialog management, 3) a detailed investigation of clarification requests forms with respect to their functions. After a brief introduction (see Section 1), we present: the theoretical underpinnings of the paper and the background work (see Section 2) our system architecture (see Sections 3 and 4) and the clarification requests (CRs) issue (see Section 5); our CRs classification, and some examples in context (see Section 6).

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Section: Related Workmentioning

confidence: 99%

Graph-based representations of clarification strategies supporting automatic dialogue management

Russo¹,

Mancini²,

Grazioso³

et al. 2022

ijcol

View full text Add to dashboard Cite

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“…The interactive 3D environment projected on the curved screen has been developed by using the popular game engine Unreal Engine 4 3 and by taking advantage of the facilities provided by the framework FANTASIA [19]. The framework aims at supporting a rapid design and implementation of interactive applications for HCI studies by integrating several modules, such as voice synthesis, speech recognition, graph database and dialogue manager.…”

Section: System Architecturementioning

confidence: 99%

Natural interaction with traffic control cameras through multimodal interfaces

Grazioso¹,

Podda²,

Barra³

et al. 2021

Preprint

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Human-Computer Interfaces have always played a fundamental role in usability and commands' interpretability of the modern software systems. With the explosion of the Artificial Intelligence concept, such interfaces have begun to fill the gap between the user and the system itself, further evolving in Adaptive User Interfaces (AUI). Meta Interfaces are a further step towards the user, and they aim at supporting the human activities in an ambient interactive space; in such a way, the user can control the surrounding space and interact with it. This work aims at proposing a meta user interface that exploits the Put That There paradigm to enable the user to fast interaction by employing natural language and gestures. The application scenario is a video surveillance control room, in which the speed of actions and reactions is fundamental for urban safety and driver and pedestrian security. The interaction is oriented towards three environments: the first is the control room itself, in which the operator can organize the views of the monitors related to the cameras on site by vocal commands and gestures, as well as conveying the audio on the headset or in the speakers of the room. The second one is related to the control of the video, in order to go back and forth to a particular scene showing specific events, or zoom in/out a particular camera; the third allows the operator to send rescue vehicle in a particular street, in case of need. The gestures data are acquired through a Microsoft Kinect 2 which captures pointing and gestures allowing the user to interact multimodally thus increasing the naturalness of the interaction; the related module maps the movement information to a particular instruction, also supported by vocal commands which enable its execution. Vocal commands are mapped by means of the LUIS (Language Understanding) framework by Microsoft, which helps to yield a fast deploy of the application; furthermore, LUIS guarantees the possibility to extend the dominion related command list so as to constantly improve and update the model. A testbed procedure investigates both the system usability and multimodal recognition performances. Multimodal sentence error rate (intended as the number of incorrectly recognized utterances even for a single item) is around 15%, given by the

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“…The system presented here is intended as one of the possible applications of the framework FANTASIA by (Origlia et al 2019), whose architecture is shown in Figure 1 1 . FANTASIA's aim is to integrate different modules, such as a graph database, a dialogue manager, a game engine, and a voice synthesis engine for the development of social interactive systems.…”

Section: System Architecturementioning

confidence: 99%

“…Figure1shows an improved version of the architecture of the one displayed in the reference paper(Origlia et al 2019) …”

mentioning

confidence: 99%

Cutting melted butter? Common Ground inconsistencies management in dialogue systems using graph databases

Maro¹,

Origlia²,

Cutugno³

2021

ijcol

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In this work, a spoken dialogue system architecture capable of dealing with Common Ground inconsistencies is proposed. Specifically, attention will be drawn upon the Conflict Search Graph, with insights on its ability to recognise problems and make them explicit via polar questions. Appropriate question forms are, indeed, adopted for the occurring type of common ground conflict, based on previous experiments, which showed that providing automatic dialogue systems with such grounding capabilities can lead to improved usability and naturalness. The described system architecture is, thus, able to detect conflicts and to use argumentation-based pragmatic strategies to signal them consistently with previous observations.

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FANTASIA: a framework for advanced natural tools and applications in social, interactive approaches

Cited by 13 publications

References 19 publications

Graph-based representations of clarification strategies supporting automatic dialogue management

Graph-based representations of clarification strategies supporting automatic dialogue management

Natural interaction with traffic control cameras through multimodal interfaces

Cutting melted butter? Common Ground inconsistencies management in dialogue systems using graph databases

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