Collaborative Behaviour Modelling of Virtual Agents using Communication in a Mixed Human-Agent Teamwork

International audience—The coordination is an essential ingredient for the mixed human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. In this paper, we proposed a collaborative conversational belief-desire-intention (C 2 BDI) behavioural agent architecture that allows to enhance the knowledge sharing using natural language communication between team members. We defined collaborative conversation protocols that provide proactive behaviour to agents for the coordination between team members. Furthermore, to endow the communication capabilities to C 2 BDI agent, we described the information state based approach for the natural language processing of the utterances. We have applied the proposed architecture to a real scenario in a collaborative virtual environment for training. Our solution enables the user to coordinate with other team members

Task-Oriented Conversational Behavior of Agents for Collaboration in Human-Agent Teamwork

International audienceCoordination is an essential ingredient for human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. This paper proposes a be-havioral architecture C 2 BDI that enhances the knowledge sharing using natural language communication between team members. Collaborative conversation protocols and resource allocation mechanism have been defined that provide proactive behavior to agents for coordination. This architecture has been applied to a real scenario in a collaborative virtual environment for learning. The solution enables users to coordinate with other team members

Detecting Team Conflict From Multiparty Dialogue

The emergence of online collaboration platforms has dramatically changed the dynamics of human teamwork, creating a veritable army of virtual teams composed of workers in different physical locations. The global world requires a tremendous amount of collaborative problem solving, primarily virtual, making it an excellent domain for computer scientists and team cognition researchers who seek to understand the dynamics involved in collaborative tasks to provide a solution that can support effective collaboration. Mining and analyzing data from collaborative dialogues can yield insights into virtual teams\u27 thought processes and help develop virtual agents to support collaboration. Good communication is indubitably the foundation of effective collaboration. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomenon in which humans synchronize their linguistic choices. This dissertation presents several technical innovations in the usage of machine learning towards analyzing, monitoring, and predicting collaboration success from multiparty dialogue by successfully handling the problems of resource scarcity and natural distribution shifts. First, we examine the problem of predicting team performance from embeddings learned from multiparty dialogues such that teams with similar conflict scores lie close to one another in vector space. We extract the embeddings from three types of features: 1) dialogue acts 2) sentiment polarity 3) syntactic entrainment. Although all of these features can be used to predict team performance effectively, their utility varies by the teamwork phase. We separate the dialogues of players playing a cooperative game into stages: 1) early (knowledge building), 2) middle (problem-solving), and 3) late (culmination). Unlike syntactic entrainment, both dialogue act and sentiment embeddings effectively classify team performance, even during the initial phase. Second, we address the problem of learning generalizable models of collaboration. Machine learning models often suffer domain shifts; one advantage of encoding the semantic features is their adaptability across multiple domains. We evaluate the generalizability of different embeddings to other goal-oriented teamwork dialogues. Finally, in addition to identifying the features predictive of successful collaboration, we propose multi-feature embedding (MFeEmb) to improve the generalizability of collaborative task success prediction models under natural distribution shifts and resource scarcity. MFeEmb leverages the strengths of semantic, structural, and textual features of the dialogues by incorporating the most meaningful information from dialogue acts (DAs), sentiment polarities, and vocabulary of the dialogues. To further enhance the performance of MFeEmb under a resource-scarce scenario, we employ synthetic data generation and few-shot learning. We use the method proposed by Bailey and Chopra (2018) for few-shot learning from the FsText python library. We replaced the universal embedding with our proposed multi-feature embedding to compare the performance of the two. For data augmentation, we propose using synonym replacement from collaborative dialogue vocabulary instead of synonym replacement from WordNet. The research was conducted on several multiparty dialogue datasets, including ASIST, SwDA, Hate Speech, Diplomacy, Military, SAMSum, AMI, and GitHub. Results show that the proposed multi-feature embedding is an excellent choice for the meta-training stage of the few-shot learning, even if it learns from a small train set of size as small as 62 samples. Also, our proposed data augmentation method showed significant performance improvement. Our research has potential ramifications for the development of conversational agents that facilitate teaming as well as towards the creation of more effective social coding platforms to better support teamwork between software engineers

Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

Artificial intelligence (AI)-enabled voice assistants (VAs) such as Amazon Alexa increasingly assist shopping decisions and exhibit empathic behavior. The advancement of empathic AI raises concerns about machines nudging consumers into purchasing undesired or unnecessary products. Yet, it is unclear how the machine’s empathic behavior affects consumer responses and decision-making outcomes during voice-enabled shopping. This article draws from the service robot acceptance model (sRAM) and social response theory (SRT) and presents an individual-session experiment where families (vs. individuals) complete actual shopping tasks using an ad-hoc Alexa app featuring high (vs. standard) empathic capabilities. We apply the experimental conditions as moderators to the structural model, bridging selected functional, social-emotional, and relational variables. Our framework collocates affective empathy, explicates the bases of consumers’ beliefs, and predicts behavioral outcomes. Findings demonstrate (i) an increase in consumers’ perceptions, beliefs, and adoption intentions with empathic Alexa, (ii) a positive response to empathic Alexa holding constant in family settings, and (iii) an interaction effect only on the functional model dimensions whereby families show greater responses to empathic Alexa while individuals to standard Alexa

Beyond Dyads: Decision support for online multiparty negotiation, coalition formation and negotiation outcomes

Ph.DDOCTOR OF PHILOSOPH

Confronting Wicked Issues Through the Implementation of a Business Development Unit

Universities and Faculties in Ontario are faced with wicked issues that are limiting the financial sustainability of the organizations. Wicked issues refer to problems that are not technical in nature, are not easily fixed, offer no single solution, and because of organizational interdependencies often create other problems when unraveled. Such issues introduced in this Organizational Improvement Plan (OIP) are: decreasing governmental funding, increased competition for students, the emergence of the non-traditional student and geopolitical pressure. The leadership approach to help address these issues is a combination of Boundary Spanning, Adaptive Leadership and Mindfulness. It is the grouping of these three leadership theories that can help this Faculty be more connected and responsive to external forces impacting it. These approaches introduce an optimistic view that organizational improvement is possible, while recognizing that change is often challenging for organizational members. This OIP is concerned with the advancement of business development acumen grounded in High Reliability Principles. It explores innovations such as data informed decision making, contemporary student engagement practices, and technological infrastructure that can help the Faculty remain financially sustainable as well as a place of higher learning. If executed correctly, this approach can contribute significantly to the Faculty’s financial resilience and sustainability

Multicast outing protocols and architectures in mobile ad-hoc wireless networks

The basic philosophy of personal communication services is to provide user-to-user, location independent communication services. The emerging group communication wireless applications, such as multipoint data dissemination and multiparty conferencing tools have made the design and development of efficient multicast techniques in mobile ad-hoc networking environments a necessity and not just a desire. Multicast protocols in mobile adhoc networks have been an area of active research for the past few years. In this dissertation, protocols and architectures for supporting multicast services are proposed, analyzed and evaluated in mobile ad-hoc wireless networks. In the first chapter, the activities and recent advances are summarized in this work-in-progress area by identifying the main issues and challenges that multicast protocols are facing in mobile ad-hoc networking environments and by surveying several existing multicasting protocols. a classification of the current multicast protocols is presented, the functionality of the individual existing protocols is discussed, and a qualitative comparison of their characteristics is provided according to several distinct features and performance parameters. In the second chapter, a novel mobility-based clustering strategy that facilitates the support of multicast routing and mobility management is presented in mobile ad-hoc networks. In the proposed structure, mobile nodes are organized into nonoverlapping clusters which have adaptive variable-sizes according to their respective mobility. The mobility-based clustering (MBC) approach which is proposed uses combination of both physical and logical partitions of the network (i.e. geographic proximity and functional relation between nodes, such as mobility pattern etc.). In the third chapter, an entropy-based modeling framework for supporting and evaluating the stability is proposed in mobile ad-hoc wireless networks. The basic motivations of the proposed modeling approach stem from the commonality observed in the location uncertainty in mobile ad-hoc wireless networks and the concept of entropy. In the fourth chapter, a Mobility-based Hybrid Multicast Routing (MHMR) protocol suitable for mobile ad-hoc networks is proposed. The MHMR uses the MBC algorithm as the underlying structure. The main features that the proposed protocol introduces are the following: a) mobility based clustering and group based hierarchical structure, in order to effectively support the stability and scalability, b) group based (limited) mesh structure and forwarding tree concepts, in order to support the robustness of the mesh topologies which provides limited redundancy and the efficiency of tree forwarding simultaneously, and c) combination of proactive and reactive concepts which provide the low route acquisition delay of proactive techniques and the low overhead of reactive methods. In the fifth chapter, an architecture for supporting geomulticast services with high message delivery accuracy is presented in mobile ad-hoc wireless networks. Geomulticast is a specialized location-dependent multicasting technique, where messages are multicast to some specific user groups within a specific zone. An analytical framework which is used to evaluate the various geomulticast architectures and protocols is also developed and presented. The last chapter concludes the dissertation

See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons

Users interact with chatbots for various purposes and motivations – and for different periods of time. However, since chatbots are considered social actors and given that time is an essential component of social interactions, the question arises as to how chatbots need to be designed depending on whether they aim to help individuals achieve short-, medium- or long-term goals. Following a taxonomy development approach, we compile 22 empirically and conceptually grounded design dimensions contingent on chatbots’ temporal profiles. Based upon the classification and analysis of 120 chatbots therein, we abstract three time-dependent chatbot design archetypes: Ad-hoc Supporters, Temporary Assistants, and Persistent Companions. While the taxonomy serves as a blueprint for chatbot researchers and designers developing and evaluating chatbots in general, our archetypes also offer practitioners and academics alike a shared understanding and naming convention to study and design chatbots with different temporal profiles. © 2021 The Author