Artykuły w czasopismach na temat „Conversation Agent”
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Belainine, Billal, Fatiha Sadat i Hakim Lounis. "Modelling a Conversational Agent with Complex Emotional Intelligence". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 10 (3.04.2020): 13710–11. http://dx.doi.org/10.1609/aaai.v34i10.7127.
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Chatbots or conversational agents have enjoyed great popularity in recent years. They surprisingly perform sensitive tasks in modern societies. However, despite the fact that they offer help, support, and fellowship, there is a task that is not yet mastered: dealing with complex emotions and simulating human sensations. This research aims to design an architecture for an emotional conversation agent for long-text conversations (multi-turns). This agent is intended to work in areas where the analysis of users feelings plays a leading role. This work refers to natural language understanding and response generation.
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Goh, Ong Sing, Chun Che Fung, Kok Wai Wong i Arnold Depickere. "Embodied Conversational Agents for H5N1 Pandemic Crisis". Journal of Advanced Computational Intelligence and Intelligent Informatics 11, nr 3 (20.03.2007): 282–88. http://dx.doi.org/10.20965/jaciii.2007.p0282.
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This paper presents a novel framework for modeling embodied conversational agent for crisis communication focusing on the H5N1 pandemic crisis. Our system aims to cope with the most challenging issue on the maintenance of an engaging while convincing conversation. What primarily distinguishes our system from other conversational agent systems is that the human-computer conversation takes place within the context of H5N1 pandemic crisis. A Crisis Communication Network, called CCNet, is established based on a novel algorithm incorporating natural language query and embodied conversation agent simultaneously. Another significant contribution of our work is the development of a Automated Knowledge Extraction Agent (AKEA) to capitalize on the tremendous amount of data that is now available online to support our experiments. What makes our system differs from typical conversational agents is the attempt to move away from strictly task-oriented dialogue.
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Watkinson, Neftali, Fedor Zaitsev, Aniket Shivam, Michael Demirev, Mike Heddes, Tony Givargis, Alexandru Nicolau i Alexander Veidenbaum. "EdgeAvatar: An Edge Computing System for Building Virtual Beings". Electronics 10, nr 3 (20.01.2021): 229. http://dx.doi.org/10.3390/electronics10030229.
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Dialogue systems, also known as conversational agents, are computing systems that use algorithms for speech and language processing to engage in conversation with humans or other conversation-capable systems. A chatbot is a conversational agent that has, as its primary goal, to maximize the length of the conversation without any specific targeted task. When a chatbot is embellished with an artistic approach that is meant to evoke an emotional response, then it is called a virtual being. On the other hand, conversational agents that interact with the physical world require the use of specialized hardware to sense and process captured information. In this article we describe EdgeAvatar, a system based on Edge Computing principles for the creation of virtual beings. The objective of the EdgeAvatar system is to provide a streamlined and modular framework for virtual being applications that are to be deployed in public settings. We also present two implementations that use EdgeAvatar and are inspired by historical figures to interact with visitors of the Venice Biennale 2019. EdgeAvatar can adapt to fit different approaches for AI powered conversations.
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Pilato, Giovanni, Agnese Augello i Salvatore Gaglio. "A Modular System Oriented to the Design of Versatile Knowledge Bases for Chatbots". ISRN Artificial Intelligence 2012 (5.03.2012): 1–10. http://dx.doi.org/10.5402/2012/363840.
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The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base for a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations. The modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According to this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into account particular features of the dialogue or the user behavior. We illustrate the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation methodologies and capable of managing different conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate.
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Young Oh, Eun, Donggil Song i Hyeonmi Hong. "Interactive Computing Technology in Anti-Bullying Education: The Effects of Conversation-Bot’s Role on K-12 Students’ Attitude Change Toward Bullying Problems". Journal of Educational Computing Research 58, nr 1 (10.04.2019): 200–219. http://dx.doi.org/10.1177/0735633119839177.
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The aim of this study was to examine the effects of an anti-bullying activity that utilizes conversational virtual agents (called conversation-bots or chatbots) on students’ attitudes toward bullying problems. An experimental pre- or posttest design with a three-group setting was used. Eighty-nine fifth-grade students were assigned to one of three groups: Conversation with a virtual agent of (a) bully’s role, (b) victim’s role, and (c) teacher’s role. All agents are conversation-bots designed to support learner–computer interactions. The bully agent defends the notion that bullying behaviors are acceptable whereas the victim agent argues that bullying behavior cannot be tolerated. The teacher agent teaches students the types of bullying and its negative aspects. The participants completed an anti-bullying attitude test at pre- and posttest, which included students’ anti-bully, intention, pro-victim, behavior, and self-efficacy factors. The results show that students’ attitudes toward bullying problems changed to more positive responses after the implementation that used the conversation-bot. In addition, the results revealed that the agent’s role had an impact on the students’ attitudes toward the anti-bully factor. Implications and future research regarding the use of conversation-bots in education are discussed.
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Lin, Phoebe, Jessica Van Brummelen, Galit Lukin, Randi Williams i Cynthia Breazeal. "Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 09 (3.04.2020): 13381–88. http://dx.doi.org/10.1609/aaai.v34i09.7061.
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Understanding how machines learn is critical for children to develop useful mental models for exploring artificial intelligence (AI) and smart devices that they now frequently interact with. Although children are very familiar with having conversations with conversational agents like Siri and Alexa, children often have limited knowledge about AI and machine learning. We leverage their existing familiarity and present Zhorai, a conversational platform and curriculum designed to help young children understand how machines learn. Children ages eight to eleven train an agent through conversation and understand how the knowledge is represented using visualizations. This paper describes how we designed the curriculum and evaluated its effectiveness with 14 children in small groups. We found that the conversational aspect of the platform increased engagement during learning and the novel visualizations helped make machine knowledge understandable. As a result, we make recommendations for future iterations of Zhorai and approaches for teaching AI to children.
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Oktarini, Kadek Ratih Dwi. "Are You Flirting, Objectifying or What? a Conversation Analysis of “you’re very sexy” Conversational Turn". SOSHUM : Jurnal Sosial dan Humaniora 10, nr 3 (28.11.2020): 294–308. http://dx.doi.org/10.31940/soshum.v10i3.1972.
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Intent identification is one of the most critical components in conversational agent design. Conversational agent “is any dialogue system that not only conducts natural language processing but also responds automatically using human language.” (Conversational Agent, 2019). The crux of designing human-like conversational agent is to mimic how human understands another human and then responds “naturally”. The current study attempts to answer the fundamental question: how to model human processes of understanding another human? In order to answer that question, it starts from exploring some basic concepts relevant to intent identification from Conversation Analysis (CA). CA is a mature field that studies authentic human interaction. The basic concepts from CA are then synthesised into a model that potentially fit to existing framework and paradigm in conversational agent design, i.e. Natural Conversation Framework (NCF) and Intent-Entity-Context-Response (IECR) paradigm. Instead of using a made-up sentence, the model is then tested to an authentic conversational turn seksi sekali dirimu ‘you’re very sexy’. The test shows that the model is able to detect several possible intents contain in this authentic conversational turn. The model is also able to handle Conversational Indonesian and multi-modality. Considering the versatility of Conversation Analysis, in all likelihood the model will be able to handle any language and all kinds of modalities. Future study can be done to analyse more Conversational Indonesian data (to develop library of intent for Conversational Indonesian Language), as well as conversational data from different languages and conversational data containing diverse modalities.
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Rodríguez, John Ibañez, Santiago Rocha Durán, Daniel Díaz-López, Javier Pastor-Galindo i Félix Gómez Mármol. "C3-Sex: A Conversational Agent to Detect Online Sex Offenders". Electronics 9, nr 11 (27.10.2020): 1779. http://dx.doi.org/10.3390/electronics9111779.
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Prevention of cybercrime is one of the missions of Law Enforcement Agencies (LEA) aiming to protect and guarantee sovereignty in the cyberspace. In this regard, online sex crimes are among the principal ones to prevent, especially those where a child is abused. The paper at hand proposes C3-Sex, a smart chatbot that uses Natural Language Processing (NLP) to interact with suspects in order to profile their interest regarding online child sexual abuse. This solution is based on our Artificial Conversational Entity (ACE) that connects to different online chat services to start a conversation. The ACE is designed using generative and rule-based models in charge of generating the posts and replies that constitute the conversation from the chatbot side. The proposed solution also includes a module to analyze the conversations performed by the chatbot and calculate a set of 25 features that describes the suspect’s behavior. After 50 days of experiments, the chatbot generated a dataset with 7199 profiling vectors with the features associated to each suspect. Afterward, we applied an unsupervised method to describe the results that differentiate three groups, which we categorize as indifferent, interested, and pervert. Exhaustive analysis is conducted to validate the applicability and advantages of our solution.
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Roberts, Seán G., i Stephen C. Levinson. "Conversation, cognition and cultural evolution". Interaction Studies 18, nr 3 (8.12.2017): 402–42. http://dx.doi.org/10.1075/is.18.3.06rob.
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This paper outlines a first attempt to model the special constraints that arise in language processing in conversation, and to explore the implications such functional considerations may have on language typology and language change. In particular, we focus on processing pressures imposed by conversational turn-taking and their consequences for the cultural evolution of the structural properties of language. We present an agent-based model of cultural evolution where agents take turns at talk in conversation. When the start of planning for the next turn is constrained by the position of the verb, the stable distribution of dominant word orders across languages evolves to match the actual distribution reasonably well. We suggest that the interface of cognition and interaction should be a more central part of the story of language evolution.
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Arsovski, Sasa, Adrian David Cheok, Kirthana Govindarajoo, Nurizzaty Salehuddin i Somaiyeh Vedadi. "Artificial intelligence snapchat: Visual conversation agent". Applied Intelligence 50, nr 7 (26.02.2020): 2040–49. http://dx.doi.org/10.1007/s10489-019-01621-2.
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Ueno, Miki, Naoki Mori i Keinosuke Matsumoto. "Picture information shared conversation agent: Pictgent". ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 1, nr 1 (1.07.2013): 53–61. http://dx.doi.org/10.14201/adcaij2012115361.
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Recently, the various dialogue systems have been proposed to make a natural conversation with users. In this paper, we propose a novel dialogue system called Pictgent which utilizes "pictures" with model of situation in order to share common knowledge between users and the system. We show the basic concept and system structure of proposed Pictgent. Statistical experiments are carried out in order to confirm the effectiveness of Pictgent..
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Gutnik, G., i G. A. Kaminka. "Representing Conversations for Scalable Overhearing". Journal of Artificial Intelligence Research 25 (16.03.2006): 349–87. http://dx.doi.org/10.1613/jair.1829.
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Open distributed multi-agent systems are gaining interest in the academic community and in industry. In such open settings, agents are often coordinated using standardized agent conversation protocols. The representation of such protocols (for analysis, validation, monitoring, etc) is an important aspect of multi-agent applications. Recently, Petri nets have been shown to be an interesting approach to such representation, and radically different approaches using Petri nets have been proposed. However, their relative strengths and weaknesses have not been examined. Moreover, their scalability and suitability for different tasks have not been addressed. This paper addresses both these challenges. First, we analyze existing Petri net representations in terms of their scalability and appropriateness for overhearing, an important task in monitoring open multi-agent systems. Then, building on the insights gained, we introduce a novel representation using Colored Petri nets that explicitly represent legal joint conversation states and messages. This representation approach offers significant improvements in scalability and is particularly suitable for overhearing. Furthermore, we show that this new representation offers a comprehensive coverage of all conversation features of FIPA conversation standards. We also present a procedure for transforming AUML conversation protocol diagrams (a standard human-readable representation), to our Colored Petri net representation.
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Qin, Jinghui, Zheng Ye, Jianheng Tang i Xiaodan Liang. "Dynamic Knowledge Routing Network for Target-Guided Open-Domain Conversation". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 05 (3.04.2020): 8657–64. http://dx.doi.org/10.1609/aaai.v34i05.6390.
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Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversations. Existing methods mainly rely on single-turn data-driven learning and simple target-guided strategy without considering semantic or factual knowledge relations among candidate topics/keywords. This results in poor transition smoothness and low success rate. In this work, we adopt a structured approach that controls the intended content of system responses by introducing coarse-grained keywords, attains smooth conversation transition through turn-level supervised learning and knowledge relations between candidate keywords, and drives an conversation towards an specified target with discourse-level guiding strategy. Specially, we propose a novel dynamic knowledge routing network (DRKN) which considers semantic knowledge relations among candidate keywords for accurate next topic prediction of next discourse. With the help of more accurate keyword prediction, our keyword-augmented response retrieval module can achieve better retrieval performance and more meaningful conversations. Besides, we also propose a novel dual discourse-level target-guided strategy to guide conversations to reach their goals smoothly with higher success rate. Furthermore, to push the research boundary of target-guided open-domain conversation to match real-world scenarios better, we introduce a new large-scale Chinese target-guided open-domain conversation dataset (more than 900K conversations) crawled from Sina Weibo. Quantitative and human evaluations show our method can produce meaningful and effective target-guided conversations, significantly improving over other state-of-the-art methods by more than 20% in success rate and more than 0.6 in average smoothness score.
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Jiang, Zhuoxuan, Jie Ma, Jingyi Lu, Guangyuan Yu, Yipeng Yu i Shaochun Li. "A General Planning-Based Framework for Goal-Driven Conversation Assistant". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 9857–58. http://dx.doi.org/10.1609/aaai.v33i01.33019857.
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We propose a general framework for goal-driven conversation assistant based on Planning methods. It aims to rapidly build a dialogue agent with less handcrafting and make the more interpretable and efficient dialogue management in various scenarios. By employing the Planning method, dialogue actions can be efficiently defined and reusable, and the transition of the dialogue are managed by a Planner. The proposed framework consists of a pipeline of Natural Language Understanding (intent labeler), Planning of Actions (with a World Model), and Natural Language Generation (learned by an attention-based neural network). We demonstrate our approach by creating conversational agents for several independent domains.
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BARBUCEANU, MIHAI, i MARK S. FOX. "CAPTURING AND MODELING COORDINATION KNOWLEDGE FOR MULTI-AGENT SYSTEMS". International Journal of Cooperative Information Systems 05, nr 02n03 (czerwiec 1996): 275–314. http://dx.doi.org/10.1142/s0218843096000117.
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The agent view provides a level of abstraction at which we envisage computational systems carrying out cooperative work by interoperating globally across networks connecting people, organizations and machines. A major challenge in building such systems is coordinating the behavior of the individual agents to achieve the individual and shared goals of the participants. As part of a larger project targeted at developing an Agent Building Shell for multiagent applications, we have designed and implemented a coordination language aimed at explicitly representing, applying and capturing coordination knowledge for multiagent systems. The language provides KQML-based communication, an agent definition and execution environment, support for modeling interactions as multiple structured conversations among agents, rule-based approaches to conversation selection and execution, as well as an interactive tool for in context acquisition and debugging of cooperation knowledge. The paper presents these components in detail and then shows how the coordination language is used in the Agent Building Shell to manage content-based information distribution scenarios among agents and the coordination aspects of conflict management processes that occur when agents encounter inconsistencies. The major application of the system is the construction and integration of multiagent supply chain systems for manufacturing enterprises. This application is used throughout the paper to illustrate the introduced concepts and language constructs.
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Fadhil, Ahmed, Yunlong Wang i Harald Reiterer. "Assistive Conversational Agent for Health Coaching: A Validation Study". Methods of Information in Medicine 58, nr 01 (22.05.2019): 009–23. http://dx.doi.org/10.1055/s-0039-1688757.
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Objective Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual's behavioral modification. Although there are abundant lifestyle promotion applications and tools, they are still limited in providing tailored social support that goes beyond their predefined functionalities. In addition, virtual coaching approaches are still unable to handle user emotional needs. Our approach presents a human–virtual agent mediated system that leverages the conversational agent to handle menial caregiver's works by engaging users (e.g., patients) in a conversation with the conversational agent. The dialog used a natural conversation to interact with users, delivered by the conversational agent and handled with a finite state machine automaton. Our research differs from existing approaches that replace a human coach with a fully automated assistant on user support. The methodology allows users to interact with the technology and access health-related interventions. To assist physicians, the conversational agent gives weighting to user's adherence, based on prior defined conditions. Materials and Methods This article describes the design and validation of CoachAI, a conversational agent-assisted health coaching system to support health intervention delivery to individuals or groups. CoachAI instantiates a text-based health care conversational agent system that bridges the remote human coach and the users. Results We will discuss our approach and highlight the outcome of a 1-month validation study on physical activity, healthy diet, and stress coping. The study validates technology aspects of our human–virtual agent mediated health coaching system. We present the intervention settings and findings from the study. In addition, we present some user-experience validation results gathered during or after the experimentation. Conclusions The study provided a set of dimensions when building a human–conversational agent powered health intervention tool. The results provided interesting insights when using human–conversational agent mediated approach in health coaching systems. The findings revealed that users who were highly engaged were also more adherent to conversational-agent activities. This research made key contributions to the literature on techniques in providing social, yet tailored health coaching support: (1) identifying habitual patterns to understand user preferences; (2) the role of a conversational agent in delivering health promoting microactivities; (3) building the technology while adhering to individuals' daily messaging routine; and (4) a socio-technical system that fits with the role of conversational agent as an assistive component. Future Work Future improvements will consider building the activity recommender based on users' interaction data and integrating users' dietary pattern and emotional wellbeing into the initial user clustering by leveraging information and communication technology approaches (e.g., machine learning). We will integrate a sentiment analysis capability to gather further data about individuals and report these data to the caregiver.
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Lei, Shuyu, Xiaojie Wang i Caixia Yuan. "Cooperative Multi-Agent Reinforcement Learning with Conversation Knowledge for Dialogue Management". Applied Sciences 10, nr 8 (15.04.2020): 2740. http://dx.doi.org/10.3390/app10082740.
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Dialogue management plays a vital role in task-oriented dialogue systems, which has become an active area of research in recent years. Despite the promising results brought from deep reinforcement learning, most of the studies need to develop a manual user simulator additionally. To address the time-consuming development of simulator policy, we propose a multi-agent dialogue model where an end-to-end dialogue manager and a user simulator are optimized simultaneously. Different from prior work, we optimize the two-agents from scratch and apply the reward shaping technology based on adjacency pairs constraints in conversational analysis to speed up learning and to avoid the derivation from normal human-human conversation. In addition, we generalize the one-to-one learning strategy to one-to-many learning strategy, where a dialogue manager can be concurrently optimized with various user simulators, to improve the performance of trained dialogue manager. The experimental results show that one-to-one agents trained with adjacency pairs constraints can converge faster and avoid derivation. In cross-model evaluation with human users involved, the dialogue manager trained in one-to-many strategy achieves the best performance.
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Rivière, Jérémy, Carole Adam i Sylvie Pesty. "Langage de conversation multimodal pour agent conversationnel animé". Techniques et sciences informatiques 31, nr 4 (30.04.2012): 455–76. http://dx.doi.org/10.3166/tsi.31.455-476.
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Zhao, Guoguang, Jianyu Zhao, Yang Li, Christoph Alt, Robert Schwarzenberg, Leonhard Hennig, Stefan Schaffer, Sven Schmeier, Changjian Hu i Feiyu Xu. "MOLI: Smart Conversation Agent for Mobile Customer Service". Information 10, nr 2 (15.02.2019): 63. http://dx.doi.org/10.3390/info10020063.
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Human agents in technical customer support provide users with instructional answers to solve a task that would otherwise require a lot of time, money, energy, physical costs. Developing a dialogue system in this domain is challenging due to the broad variety of user questions. Moreover, user questions are noisy (for example, spelling mistakes), redundant and have various natural language expressions. In this work, we introduce a conversational system, MOLI (the name of our dialogue system), to solve customer questions by providing instructional answers from a knowledge base. Our approach combines models for question type and intent category classification with slot filling and a back-end knowledge base for filtering and ranking answers, and uses a dialog framework to actively query the user for missing information. For answer-ranking we find that sequential matching networks and neural multi-perspective sentence similarity networks clearly outperform baseline models, achieving a 43% error reduction. The end-to-end P@1(Precision at top 1) of MOLI was 0.69 and the customers’ satisfaction was 0.73.
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K, Karpagam, i Saradha A. "AN INTELLIGENT CONVERSATION AGENT FOR HEALTH CARE DOMAIN". ICTACT Journal on Soft Computing 4, nr 3 (1.04.2014): 772–76. http://dx.doi.org/10.21917/ijsc.2014.0110.
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YUAN, SOE-TSYR, i ZENG-LUNG WU. "AN INFRASTRUCTURE FOR ENGINEERING COOPERATIVE AGENTS". International Journal of Software Engineering and Knowledge Engineering 10, nr 06 (grudzień 2000): 681–711. http://dx.doi.org/10.1142/s0218194000000377.
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Currently, systems of cooperative agents (multi-agent systems), possessing the capabilities of autonomy, adaptation, and cooperation, are being used in an increasingly wide variety of application areas, and the conversation-based multi-agent system design is the major design for those multi-agent systems. Supposedly, conversation-based multi-agent systems should have been prevailing enough for tackling dynamic aspects of problems in a variety of domains. However, for industries, multi-agent systems are still found to be in the birth stage where they only show their new values in anticipation for further explorations and improvements in order to attract critical mass of users of information executives or software developers. Nevertheless, what are the success factors that can result in a critical mass of multi-agent system designers? This paper shows one possible success factor — an infrastructure for the bottom-up design of multi-agent systems. The bottom-up design makes it possible for agents to be reassembled into multi-agent systems and reused as needed. However, what do we need to successfully support the bottom-up design? This paper is the first attempt to present a tool that fully supports the bottom-up design of multi-agent systems. The tool has three parts. The first part is a wrapper that wraps each agent so that it exempts the designers from the careful detailed deployment of the inter-relationships between cooperation knowledge and task knowledge inside the agent. This wrapper should be independent of the functions of agents. The second part is an environment that can support the wrapper to automate the cooperation process on behalf of agents. The third part is a graphical assembly panel for developers to visually configure wrapped agents residing at different places of the Internet into a working multi-agent system.
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De Medeiros, Luciano Frontino, Armando Kolbe Junior i Alvino Moser. "A Cognitive Assistant that Uses Small Talk in Tutoring Conversation". International Journal of Emerging Technologies in Learning (iJET) 14, nr 11 (14.06.2019): 138. http://dx.doi.org/10.3991/ijet.v14i11.10288.
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This paper presents a cognitive conversational agent for use in teaching and learning processes named THOTH (Training by Highly Ontology-oriented Tutoring Host) that is capable of formulating and enunciating a well-defined set of small talk segments in a Q&A (Question and Answer) interaction. The small talk structures are placed within the tutoring conversation by an agent designed as a cognitive assistant, in order to make communication smoother and less formal, presenting a more “concerned” behavior. Twelve small talk segments are suggested, included in conversation stages such as opening and closing the conversation, maintaining the rhythm and managing learning. We also explore some branches of the theoretical assumptions and concepts grounding THOTH, such as Dennett’s intentional stance, Bloom’s taxonomy and microlearning theory. In order to measure the perception and effects of using THOTH, we performed a quantitative and qualitative study with a group of students from a course in Applied Artificial Intelligence over one semester. The outcomes are classified into two main categories of analysis – interactivity and intentionality – informing the discussion on the potential uses of a small talk agent as a valuable resource in tutoring interaction, and also raising some points for improvement. In addition to this study, we also drew a small talk profile for this group of students revealing what structures and topics they use the most, as well as a partial performance analysis that allows identifying some effects on learning.
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Montenegro, Cesar, Asier López Zorrilla, Javier Mikel Olaso, Roberto Santana, Raquel Justo, Jose Antonio Lozano i María Inés Torres. "A Dialogue-Act Taxonomy for a Virtual Coach Designed to Improve the Life of Elderly". Multimodal Technologies and Interaction 3, nr 3 (11.07.2019): 52. http://dx.doi.org/10.3390/mti3030052.
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This paper presents a dialogue act taxonomy designed for the developmentof a conversational agent for elderly. The main goal of this conversational agent is to improvelife quality of the user by means of coaching sessions in different topics. In contrast to otherapproaches such as task-oriented dialogue systems and chit-chat implementations, the agent shoulddisplay a pro-active attitude, driving the conversation to reach a number of diverse coachinggoals. Therefore, the main characteristic of the introduced dialogue act taxonomy is its capacityfor supporting a communication based on the GROW model for coaching. In addition, the taxonomyhas a hierarchical structure between the tags and it is multimodal. We use the taxonomy toannotate a Spanish dialogue corpus collected from a group of elder people. We also present apreliminary examination of the annotated corpus and discuss on the multiple possibilities it presentsfor further research.
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Ren, Pengjie, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz i Maarten De Rijke. "Conversations with Search Engines: SERP-based Conversational Response Generation". ACM Transactions on Information Systems 39, nr 4 (31.10.2021): 1–29. http://dx.doi.org/10.1145/3432726.
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In this article, we address the problem of answering complex information needs by conducting conversations with search engines , in the sense that users can express their queries in natural language and directly receive the information they need from a short system response in a conversational manner. Recently, there have been some attempts towards a similar goal, e.g., studies on Conversational Agent s (CAs) and Conversational Search (CS). However, they either do not address complex information needs in search scenarios or they are limited to the development of conceptual frameworks and/or laboratory-based user studies. We pursue two goals in this article: (1) the creation of a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines, and (2) the development of a state-of-the-art pipeline for conversations with search engines, Conversations with Search Engines (CaSE), using this dataset. SaaC is built based on a multi-turn conversational search dataset, where we further employ workers from a crowdsourcing platform to summarize each relevant passage into a short, conversational response. CaSE enhances the state-of-the-art by introducing a supporting token identification module and a prior-aware pointer generator, which enables us to generate more accurate responses. We carry out experiments to show that CaSE is able to outperform strong baselines. We also conduct extensive analyses on the SaaC dataset to show where there is room for further improvement beyond CaSE. Finally, we release the SaaC dataset and the code for CaSE and all models used for comparison to facilitate future research on this topic.
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Chen, Jengchung Victor, Huyen Thi Le i Sinh Thi Thu Tran. "Understanding automated conversational agent as a decision aid: matching agent's conversation with customer's shopping task". Internet Research 31, nr 4 (21.01.2021): 1376–404. http://dx.doi.org/10.1108/intr-11-2019-0447.
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PurposeTo provide better services to customers, especially immediate responses and 24/7 availability, businesses are implementing text-based automated conversational agents, i.e. chatbots on their social platforms and websites. Chatbots are required to not only provide customers with necessary consultancy and guidance but also communicate friendly and socially. Based on the cognitive fit theory, this study attempts to examine the role of chatbot as a decision aid and how the match between information presentation in forms of decisional guidance and communication style and the shopping task influences consumers' perceived cognitive fit and decision performance outcomes.Design/methodology/approachA 2 x 2 x 2 between subject online experiment was conducted to identify which kind of decisional guidance (suggestive and informative guidance) and communication style (task-oriented vs social-oriented style) are the most appropriate for each type of shopping task (searching vs browsing task).FindingsThe findings show that when customers interact with chatbots, they will perceive higher cognitive fit if the chatbots provide them with suggestive guidance and communicate in a friendly style especially when they perform a searching task.Originality/valueThis study is the first attempt to understand the role of chatbots as a decision aid to customers using the communicative language. This study also tries to explore the cognitive fit theory in a novel way, and we propose the information presentation in forms of communicative language rather than matrices, tables and graphs.
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Morita, Masaya, i Kazuhiro Kuwabara. "Agent-based customization of a remote conversation support system". Intelligent Decision Technologies 7, nr 3 (19.06.2013): 175–84. http://dx.doi.org/10.3233/idt-130161.
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Lin, Fuhua, i Douglas H. Norrie. "Schema-based conversation modeling for agent-oriented manufacturing systems". Computers in Industry 46, nr 3 (październik 2001): 259–74. http://dx.doi.org/10.1016/s0166-3615(01)00133-6.
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AHN, HYUNG JUN, i SUNG JOO PARK. "A FLEXIBLE TRANSACTION FRAMEWORK FOR DYNAMIC COLLABORATION OF AGENTS–WITH AN ONLINE TRAVEL APPLICATION". International Journal of Cooperative Information Systems 13, nr 04 (grudzień 2004): 487–520. http://dx.doi.org/10.1142/s0218843004001036.
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The advancement of Internet-based commerce has created a turbulent market environment by allowing easier introduction of new products, services, and suppliers. For this dynamic environment, information systems need to be designed in a flexible way to meet the changing requirements. This paper proposes a flexible transaction framework for multi-agent systems so that agent systems can adapt to various types of changes in the dynamic electronic market environment. The flexible transaction framework consists of a flexible conversation model (FCM) for agents and an ontology meta-model called TOO (Task-Oriented Ontology) for the specific requirements of FCM. FCM permits agents to use newly acquired conversation policies that are induced by the frequent changes in transaction methods. Since FCM requires agents to be aware of a broader set of ontology entities, TOO is used to reduce the complexity of the ontology design and implementation for FCM. The flexible transaction framework is illustrated with an online traveling system developed for Korean domestic transportation services. The prospective synergy between the present work and Web services is discussed in the discussion section.
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Mooney Berges, Bethany, Christine Cambareri, Samuel U. Takvorian, Mike Serpa, Lawrence N. Shulman, Justin E. Bekelman, Katharine A. Rendle, Jesse Argon i Roy Rosin. "Leveraging a conversational agent to support adherence to oral anticancer agents: A usability study." Journal of Clinical Oncology 37, nr 15_suppl (20.05.2019): 6534. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.6534.
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6534 Background: Identifying effective, scalable strategies to ensure patient adherence to oral anticancer agents (OACAs) is a major challenge. Previous studies have shown widely variable rates of adherence, and suboptimal adherence is associated with decreased effectiveness and higher costs. A small but growing literature supports digital health behavioral interventions across a variety of chronic illnesses, including in cancer. In particular, conversational agents—or technologies that mimic human conversation using text or spoken language—have shown early promise in supporting behavior change, but have yet to be rigorously tested in the context of OACAs. Methods: A rapid cycle prototyping approach led to the development of ‘Penny’ – a bidirectional, conversational agent that engages patients via text messaging, and leverages natural language processing and machine learning to learn from clinical interactions. Core functionalities include: (1) real-time dosing instructions, (2) motivational reminders, and (3) symptom monitoring with self-management support. We conducted a four-month usability study between December 24, 2017 and May 1, 2018 in a large academic cancer center. At monthly intervals for the first 12 weeks of follow-up, research staff conducted qualitative interviews with participants to evaluate usability and acceptance. Results: 11 patients with gastrointestinal neuroendocrine cancer on capecitabine and temozolomide were approached regarding the study. Of these, 10 agreed to participate (ages 45 to 71). Overall, participant satisfaction was high with a Net Promoter Score of 100. Reliability of Penny’s algorithmic branching to provide accurate dosing information and symptom triage was also high: symptoms were accurately graded 100% of the time, and there was appropriate self-management advice or provider triage 100% of the time. Average daily adherence (based on self-report) was 98%. Participants reported that 3 emergency room visits were avoided during the study period. Conclusions: In preliminary testing, a mobile phone-based conversational agent was a usable and acceptable means of supporting OACA adherence. Expanded study testing patient safety and efficacy is underway.
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Venant, Antoine, i Nicholas Asher. "OK or not OK? Commitments in acknowledgments and corrections". Semantics and Linguistic Theory 25 (7.04.2016): 595. http://dx.doi.org/10.3765/salt.v25i0.3072.
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While a semantics without differing “points of view” of different agents is a good first hypothesis for the analysis of the content of monologue, dialogues typically involve differing points of view from different agents. In particular one agent may not agree with what another agent asserts, or may have a different interpretation of an utterance from that of its author. An adequate semantics for dialogue should proceed by attributing to different dialogue agents separate views of the contents of their conversation. We model this, following others, by assigning each agent her own commitment slate. In this paper we bring out a complication with this approach that has gone so far unnoticed in formal semantics and the prior work we just mentioned, albeit it is well-known from epistemic game theory: commitment slates interact; agents typically commit to the fact that other agents make certain commitments. We thus formulate the semantics of dialogue moves and conversational goals in terms of nested, public commitments. We develop two semantics for nested commitments, one for a simple propositional language, the other for a full description language for the discourse structure of dialogues; and we show how one is an approximation of the other. We apply this formal setting to provide a unified account of different linguistic problems: the problem of ambiguity and the problem of acknowledgments and grounding. We also briefly discuss the problem of corrections and how to integrate them in our framework.
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Alias, Suraya. "Unsupervised Text Feature Extraction for Academic Chatbot using Constrained FP-Growth". ASM Science Journal 14 (2.04.2021): 1–11. http://dx.doi.org/10.32802/asmscj.2020.576.
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In the edge where conversation merely involves online chatting and texting one another, an automated conversational agent is needed to support certain repetitive tasks such as providing FAQs, customer service and product recommendations. One of the key challenges is to identify and discover user’s intention in a social conversation where the focus of our work in the academic domain. Our unsupervised text feature extraction method for Intent Pattern Discovery is developed by applying text features constraints to the FP-Growth technique. The academic corpus was developed using a chat messages dataset where the conversation between students and academicians regarding undergraduate and postgraduate queries were extracted as text features for our model. We experimented with our new Constrained Frequent Intent Pattern (cFIP) model in contrast with the N-gram model in terms of feature-vector size reduction, descriptive intent discovery, and analysis of cFIP Rules. Our findings show significant and descriptive intent patterns was discovered with confidence rules value of 0.9 for cFIP of 3-sequence. We report an average feature-vector size reduction of 76% compared to the Bigram model using both undergraduate and postgraduate conversation datasets. The usability testing results depicted overall user satisfaction average mean score is 4.30 out of 5 in using the Academic chatbot which supported our intent discovery cFIP approach.
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SHI, Nuobei, Qin Zeng i Raymond Lee. "XAI Language Tutor - A XAI-based Language Learning Chatbot using Ontology and Transfer Learning Techniques". International Journal on Natural Language Computing 9, nr 5 (30.10.2020): 1–21. http://dx.doi.org/10.5121/ijnlc.2020.9501.
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In this paper, we proposed a XAI-based Language Learning Chatbot (namely XAI Language Tutor) by using ontology and transfer learning techniques. To facilitate three levels of language learning, XAI Language Tutor consists of three levels for systematically English learning, which includes: 1) phonetics level for speech recognition and pronunciation correction; 2) semantic level for specific domain conversation, and 3) simulation of “free-style conversation” in English - the highest level of language chatbot communication as “free-style conversation agent”. In terms of academic contribution, we implement the ontology graph to explain the performance of free-style conversation, following the concept of XAI (Explainable Artificial Intelligence) to visualize the connections of neural network in bionics, and explain the output sentence from language model. From implementation perspective, our XAI Language Tutor agent integrated the mini-program in WeChat as front-end, and fine-tuned GPT-2 model of transfer learning as back-end to interpret the responses by ontology graph.
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Gaffney, Hannah, Warren Mansell i Sara Tai. "Conversational Agents in the Treatment of Mental Health Problems: Mixed-Method Systematic Review". JMIR Mental Health 6, nr 10 (18.10.2019): e14166. http://dx.doi.org/10.2196/14166.
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Background The use of conversational agent interventions (including chatbots and robots) in mental health is growing at a fast pace. Recent existing reviews have focused exclusively on a subset of embodied conversational agent interventions despite other modalities aiming to achieve the common goal of improved mental health. Objective This study aimed to review the use of conversational agent interventions in the treatment of mental health problems. Methods We performed a systematic search using relevant databases (MEDLINE, EMBASE, PsycINFO, Web of Science, and Cochrane library). Studies that reported on an autonomous conversational agent that simulated conversation and reported on a mental health outcome were included. Results A total of 13 studies were included in the review. Among them, 4 full-scale randomized controlled trials (RCTs) were included. The rest were feasibility, pilot RCTs and quasi-experimental studies. Interventions were diverse in design and targeted a range of mental health problems using a wide variety of therapeutic orientations. All included studies reported reductions in psychological distress postintervention. Furthermore, 5 controlled studies demonstrated significant reductions in psychological distress compared with inactive control groups. In addition, 3 controlled studies comparing interventions with active control groups failed to demonstrate superior effects. Broader utility in promoting well-being in nonclinical populations was unclear. Conclusions The efficacy and acceptability of conversational agent interventions for mental health problems are promising. However, a more robust experimental design is required to demonstrate efficacy and efficiency. A focus on streamlining interventions, demonstrating equivalence to other treatment modalities, and elucidating mechanisms of action has the potential to increase acceptance by users and clinicians and maximize reach.
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Laban, Guy, Jean-Noël George, Val Morrison i Emily S. Cross. "Tell me more! Assessing interactions with social robots from speech". Paladyn, Journal of Behavioral Robotics 12, nr 1 (12.12.2020): 136–59. http://dx.doi.org/10.1515/pjbr-2021-0011.
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AbstractAs social robots are increasingly introduced into health interventions, one potential area where they might prove valuable is in supporting people’s psychological health through conversation. Given the importance of self-disclosure for psychological health, this study assessed the viability of using social robots for eliciting rich disclosures that identify needs and emotional states in human interaction partners. Three within-subject experiments were conducted with participants interacting with another person, a humanoid social robot, and a disembodied conversational agent (voice assistant). We performed a number of objective evaluations of disclosures to these three agents via speech content and voice analyses and also probed participants’ subjective evaluations of their disclosures to three agents. Our findings suggest that participants overall disclose more to humans than artificial agents, that agents’ embodiment influences disclosure quantity and quality, and that people are generally aware of differences in their personal disclosures to three agents studied here. Together, the findings set the stage for further investigation into the psychological underpinnings of self-disclosures to artificial agents and their potential role in eliciting disclosures as part of mental and physical health interventions.
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BRIAN BLAKE, M. "AGENT-BASED COMMUNICATION FOR DISTRIBUTED WORKFLOW MANAGEMENT USING JINI TECHNOLOGIES". International Journal on Artificial Intelligence Tools 12, nr 01 (marzec 2003): 81–99. http://dx.doi.org/10.1142/s0218213003001125.
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Agent communication has developed widely over the past decade for various types of multiple agent environments. Originally, most of this research surrounded simulation systems and inference systems. Subsequently, agents are expected to adapt to, dynamically create, and understand evolving conversation policies. This concept of agent communication is not completely necessary in some domains. One such domain is that of distributed workflow management with implications into Electronic Commerce. In this domain, agents are "middle-agents" that represent the distributed components that implement each individual workflow step. By representing the component-based services of each step, multiple distributed agents can essentially manage a workflow or supply chain that spans several online businesses (B2B). The WARP (Workflow-Automation through Agent-Based Reflective Processes) architecture is a multi-agent architecture developed to support distributed workflow management environments where distributed components are used to implement each of the workflow steps This paper describes an object-oriented workflow ontology for this distributed workflow management domain. There is also a software engineering process for integrating new component-based services into this ontology. Furthermore, the interaction protocol and supporting implementation based on the Knowledge Query and Manipulation Language (KQML) are presented. This agent communication architecture is implemented using Sun MicroSystems' Java and Jini technologies.
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Mauch, Christof, Nga Le, Christian Lahnstein i Amy Hay. "The Legacy of Agent Orange: A Conversation about Risks and Responsibility". Global Environment 7, nr 1 (1.04.2014): 218–36. http://dx.doi.org/10.3197/197337314x13927191905005.
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Bennion, Matthew Russell, Gillian E. Hardy, Roger K. Moore, Stephen Kellett i Abigail Millings. "Usability, Acceptability, and Effectiveness of Web-Based Conversational Agents to Facilitate Problem Solving in Older Adults: Controlled Study". Journal of Medical Internet Research 22, nr 5 (27.05.2020): e16794. http://dx.doi.org/10.2196/16794.
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Background The usability and effectiveness of conversational agents (chatbots) that deliver psychological therapies is under-researched. Objective This study aimed to compare the system usability, acceptability, and effectiveness in older adults of 2 Web-based conversational agents that differ in theoretical orientation and approach. Methods In a randomized study, 112 older adults were allocated to 1 of the following 2 fully automated interventions: Manage Your Life Online (MYLO; ie, a chatbot that mimics a therapist using a method of levels approach) and ELIZA (a chatbot that mimics a therapist using a humanistic counseling approach). The primary outcome was problem distress and resolution, with secondary outcome measures of system usability and clinical outcome. Results MYLO participants spent significantly longer interacting with the conversational agent. Posthoc tests indicated that MYLO participants had significantly lower problem distress at follow-up. There were no differences between MYLO and ELIZA in terms of problem resolution. MYLO was rated as significantly more helpful and likely to be used again. System usability of both the conversational agents was associated with helpfulness of the agents and the willingness of the participants to reuse. Adherence was high. A total of 12% (7/59) of the MYLO group did not carry out their conversation with the chatbot. Conclusions Controlled studies of chatbots need to be conducted in clinical populations across different age groups. The potential integration of chatbots into psychological care in routine services is discussed.
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Walker, Traci, Heidi Christensen, Bahman Mirheidari, Thomas Swainston, Casey Rutten, Imke Mayer, Daniel Blackburn i Markus Reuber. "Developing an intelligent virtual agent to stratify people with cognitive complaints: A comparison of human–patient and intelligent virtual agent–patient interaction". Dementia 19, nr 4 (14.09.2018): 1173–88. http://dx.doi.org/10.1177/1471301218795238.
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Previous work on interactions in the memory clinic has shown that conversation analysis can be used to differentiate neurodegenerative dementia from functional memory disorder. Based on this work, a screening system was developed that uses a computerised ‘talking head’ (intelligent virtual agent) and a combination of automatic speech recognition and conversation analysis-informed programming. This system can reliably differentiate patients with functional memory disorder from those with neurodegenerative dementia by analysing the way they respond to questions from either a human doctor or the intelligent virtual agent. However, much of this computerised analysis has relied on simplistic, nonlinguistic phonetic features such as the length of pauses between talk by the two parties. To gain confidence in automation of the stratification procedure, this paper investigates whether the patients’ responses to questions asked by the intelligent virtual agent are qualitatively similar to those given in response to a doctor. All the participants in this study have a clear functional memory disorder or neurodegenerative dementia diagnosis. Analyses of patients’ responses to the intelligent virtual agent showed similar, diagnostically relevant sequential features to those found in responses to doctors’ questions. However, since the intelligent virtual agent’s questions are invariant, its use results in more consistent responses across people – regardless of diagnosis – which facilitates automatic speech recognition and makes it easier for a machine to learn patterns. Our analysis also shows why doctors do not always ask the same question in the exact same way to different patients. This sensitivity and adaptation to nuances of conversation may be interactionally helpful; for instance, altering a question may make it easier for patients to understand. While we demonstrate that some of what is said in such interactions is bound to be constructed collaboratively between doctor and patient, doctors could consider ensuring that certain, particularly important and/or relevant questions are asked in as invariant a form as possible to be better able to identify diagnostically relevant differences in patients’ responses.
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Rojc, Matej, Zdravko Kačič, Marko Presker i Izidor Mlakar. "TTS-driven Embodied Conversation Avatar for UMB-SmartTV". International Journal of Computers and Communications 15 (14.04.2021): 1–7. http://dx.doi.org/10.46300/91013.2021.15.1.
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When human-TV interaction is performed by remote controller and mobile devices only, the interactions tend to be mechanical, dreary and uninformative. To achieve more advanced interaction, and more human-human like, we introduce the virtual agent technology as a feedback interface. Verbal and co-verbal gestures are linked through complex mental processes, and although they represent different sides of the same mental process, the formulations of both are quite different. Namely, verbal information is bound by rules and grammar, whereas gestures are influenced by emotions, personality etc. In this paper a TTS-driven behavior generation system is proposed for more advanced interface used for smart IPTV platforms. The system is implemented as a distributive non-IPTV service and integrated into UMB-SmartTV in a service-oriented fashion. The behavior generation system fuses speech and gesture production models by using FSMs and HRG structures. Features for selecting the shape and alignment of co-verbal movement are based on linguistic features (that can be extracted from arbitrary input text), and prosodic features (as predicted within several processing steps in the TTS engine). At the end, the generated speech and co-verbal behavior are animated by an embodied conversational agent (ECA) engine and represented to the user within the UMB-SmarTV user interface.
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Panesar, Kulvinder. "Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions". Journal of Computer-Assisted Linguistic Research 4, nr 1 (26.05.2020): 47. http://dx.doi.org/10.4995/jclr.2020.12932.
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This paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution.
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Wati, Atikah. "THE ANALYSIS OF DERIVATION IN CONVERSATION SCRIPTS". Wiralodra English Journal 2, nr 2 (5.07.2019): 176–83. http://dx.doi.org/10.31943/wej.v2i2.34.
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Word derivation make the conversation more simple and specific to the topic. The case is many students didn’t recognise and aware of using it. this paper is trying to reveal the words derivation that the students use in their speaking class conversation whether or not it affected to the semantic. Dealing with vocabulary and grammar, speakers often choose certain vocabulary to express something and forming it into the correct grammatical rules, the choice of vocabulary that the speaker use during the planned speaking activity in the classroom and how this words implemented with the correct grammar in the form of literature dealing with morphology and semantic are the main focus of this paper. Many students didn’t really aware with the use of derivational words, 3 numbers of students conversational scripts didn’t insert any derivational words at all. While the other 2 insert very little derivational words. The method of analysis that the writer use to reveal the processes of derivation in more detail by noting three simultaneous processes, namely: a morphological process (e.g. changing the shape of an existing word by adding a prefix or suffix morpheme to an existing root morpheme) a syntactic process (changing the part of speech of a word, e.g. from verb to noun) and a semantic process (producing a new sense, agent, act, property).
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Wati, Atikah. "THE ANALYSIS OF DERIVATION IN CONVERSATION SCRIPTS". Wiralodra English Journal 2, nr 2 (1.09.2018): 176–83. http://dx.doi.org/10.31943/wej.v2i2.360.
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Word derivation make the conversation more simple and specific to the topic. The case is many students didn’t recognise and aware of using it. this paper is trying to reveal the words derivation that the students use in their speaking class conversation whether or not it affected to the semantic. Dealing with vocabulary and grammar, speakers often choose certain vocabulary to express something and forming it into the correct grammatical rules, the choice of vocabulary that the speaker use during the planned speaking activity in the classroom and how this words implemented with the correct grammar in the form of literature dealing with morphology and semantic are the main focus of this paper. Many students didn’t really aware with the use of derivational words, 3 numbers of students conversational scripts didn’t insert any derivational words at all. While the other 2 insert very little derivational words. The method of analysis that the writer use to reveal the processes of derivation in more detail by noting three simultaneous processes, namely: a morphological process (e.g. changing the shape of an existing word by adding a prefix or suffix morpheme to an existing root morpheme) a syntactic process (changing the part of speech of a word, e.g. from verb to noun) and a semantic process (producing a new sense, agent, act, property).
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Gritta, Milan, Gerasimos Lampouras i Ignacio Iacobacci. "Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management". Transactions of the Association for Computational Linguistics 9 (luty 2021): 36–52. http://dx.doi.org/10.1162/tacl_a_00352.
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Task-oriented dialogue systems typically rely on large amounts of high-quality training data or require complex handcrafted rules. However, existing datasets are often limited in size con- sidering the complexity of the dialogues. Additionally, conventional training signal in- ference is not suitable for non-deterministic agent behavior, namely, considering multiple actions as valid in identical dialogue states. We propose the Conversation Graph (ConvGraph), a graph-based representation of dialogues that can be exploited for data augmentation, multi- reference training and evaluation of non- deterministic agents. ConvGraph generates novel dialogue paths to augment data volume and diversity. Intrinsic and extrinsic evaluation across three datasets shows that data augmentation and/or multi-reference training with ConvGraph can improve dialogue success rates by up to 6.4%.
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Pritzker, Sonya Elizabeth, Jennifer Guzman, Ka-Kit Hui i Derjung Tarn. "The third speaker: The body as interlocutor in conventional, complementary, and integrative medicine encounters". Communication and Medicine 14, nr 3 (26.10.2018): 256–67. http://dx.doi.org/10.1558/cam.32577.
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This paper examines talk about the body in interactions between patients and their complementary/ alternative providers (CAM), integrative physicians (IM), or conventional physicians. In an analysis of 603 consultations, we focus on instances where the body is spoken of in agentive terms. We thus examine particular micro-interactive moments where the body is constituted as an agent that speaks, responds, and otherwise acts in ways that direct the flow of conversation or the medical decision-making process. With this data, we demonstrate how body-as-agent metaphors in the clinical encounter underscore the communicative agency of providers and position the body as an interlocutor or ‘third speaker’ in conversation with the patient and provider. We further note that we found only limited differences in the ways body-as-agent metaphors were used by CAM/IM and conventional providers. Rather than arguing that such differences demonstrate a fundamental divide between CAM/IM and conventional approaches, we therefore suggest that these kinds of supportive body-as-agent talk exist as opportunities for all providers to support patients in taking a more active stance in managing their relationship with their body.
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Kokane, Shubham, Shreeyash Khalate, Shreya Newale, Sakshi Dubewar i Jameer Kotwal. "Emotionally Intelligent Chatbot". Journal of University of Shanghai for Science and Technology 23, nr 06 (31.05.2021): 69–76. http://dx.doi.org/10.51201/jusst/21/05215.
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An emotionally intelligent chatbot system aims to make an effective conversation between humans and machines in as natural and interactive a manner as possible. The chatbot agent has the pre-embedded knowledge base to identify the sentences, intents, entities, and context of the input query to be precise for making a valid, predictable decision itself as a self-generated response to answer the query. The present technical project consists of developing an intelligent system for college enquiry purposes using a web-based chatbot agent, through machine learning, query processing, and sentiment and emotion classification system to analyze the sentiment of the visitor towards the college. Emotionally Intelligent College Enquiry Chatbot System is nothing but a chatbot to understand the user queries and respond to them during a conversation. A chatbot can actively help humans to involve in a digital automated conversation with a machine or a system effective. In the following proposed system, feature extraction and data cleaning techniques are applied to the dataset, and classifiers such as multinomial naive Bayes, logistic regression and k nearest neighbours are used to train the model. The classifier with the highest accuracy is further used for the emotion classification of users.
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WHALLEY, IAN. "PIWeCS: enhancing human/machine agency in an interactive composition system". Organised Sound 9, nr 2 (sierpień 2004): 167–74. http://dx.doi.org/10.1017/s135577180400024x.
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This paper focuses on the infrastructure and aesthetic approach used in PIWeCS: a Public Space Interactive Web-based Composition System. The concern was to increase the sense of dialogue between human and machine agency in an interactive work by adapting Paine's (2002) notion of a conversational model of interaction as a ‘complex system’. The machine implementation of PIWeCS is achieved through integrating intelligent agent programming with MAX/MSP. Human input is through a web infrastructure. The conversation is initiated and continued by participants through arrangements and composition based on short performed samples of traditional New Zealand Maori instruments. The system allows the extension of a composition through the electroacoustic manipulation of the source material.
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Latour, Bruno, i Dipesh Chakrabarty. "Conflicts of Planetary Proportion – A Conversation". Journal of the Philosophy of History 14, nr 3 (19.11.2020): 419–54. http://dx.doi.org/10.1163/18722636-12341450.
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Abstract The introduction of the long-term history of the Earth into the preoccupations of historians has triggered a crisis because it has become impossible to keep the “planet” as one single entity outside of history properly understood. As soon as the planetary intruded into history, it became impossible to keep it as one naturalized background. By problematizing the planetary, Dipesh Chakrabarty has forced philosophers, historians and anthropologists to extend pluralism to the very ground on which history was supposed to unfold. Hence Bruno Latour’s attempt at counting the number of “planets” whose attractions are simultaneously being felt today on any political question. Each of his eight planets are defined by the disconnect between where they are situated and where they are imagined to be moving, which means that each planet is led by a different and incommensurable philosophy of history. Such a “fictional planetology” is then discussed by Chakrabarty, who reviews the difficulties historians have had in taking the nonhuman (and hence the planet) as a historical agent and then adds to Latour’s count a new planetary body which further complicates the geopolitical situation. The result of their joint effort is to shift questions of philosophy of history to philosophy of geography.
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Dybala, Pawel, Michal Ptaszynski, Jacek Maciejewski, Mizuki Takahashi, Rafal Rzepka i Kenji Araki. "Multiagent system for joke generation: Humor and emotions combined in human-agent conversation". Journal of Ambient Intelligence and Smart Environments 2, nr 1 (2010): 31–48. http://dx.doi.org/10.3233/ais-2010-0053.
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Chaabi, Youness, Lekdioui Khadija, Fatine Jebbor i Rochdi Messoussi. "Determination of Distant Learner’s Sociological Profile Based on Fuzzy Logic and Naïve Bayes Techniques". International Journal of Emerging Technologies in Learning (iJET) 12, nr 10 (2.11.2017): 56. http://dx.doi.org/10.3991/ijet.v12i10.6727.
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The present article is elaborated in the context of e-learning softwares that provide assistance and functionalities to learners engaged in distance learning. Our contribution consists of a system that estimates a behavioral (sociological) profile for each student. This estimation is based on automatic analysis of students’ textual asynchronous conversations. In general, the automatic analysis of textual conversations is based on speech acts for classification and categorization of messages. This technique has several disadvantages like the absence of standardization of speech acts for determining social behaviors of learners. To overcome this, we propose a multi-agent system based on fuzzy logic reasoning and supervised learning technique for automatic classification and categorization of textual conversation. The determined profiles are proposed to teachers to provide them assistance during tutoring tasks. The objective of this article is to share our reflections around these issues by presenting our experience in the analysis of asynchronous online discussion forums. In this paper, we specifically propose (i) definitions for the used sociological profiles and (ii) introduce the architecture of the Multi-Agent System (MAS) that determines the profiles. The system was experimented with the students of the Master Program “Software Quality” in the Ibn Tofail University. The results obtained from this experience, presented and discussed in this paper, show that the proposed approach can be of interest.
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KAMALI, KAIVAN, XIAOCONG FAN i JOHN YEN. "TOWARDS A THEORY FOR MULTIPARTY PROACTIVE COMMUNICATION IN AGENT TEAMS". International Journal of Cooperative Information Systems 16, nr 02 (czerwiec 2007): 271–98. http://dx.doi.org/10.1142/s0218843007001640.
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Helping behavior in effective teams is achieved via some overlapping "shared mental models" that are developed and maintained by members of the team. In this paper, we take the perspective that multiparty "proactive" communication is critical for establishing and maintaining such a shared mental model among teammates, which is the basis for agents to offer proactive help and to achieve coherent teamwork. We first provide formal semantics for multiparty proactive performatives within a team setting. We then examine how such performatives result in updates to mental model of teammates, and how such updates can trigger helpful behaviors from other teammates. We also provide conversation policies for multiparty proactive performatives.