Mechanisms of Common Ground in Human-Agent Interaction: A Systematic Review of Conversational Agent Research

Human-agent interaction is increasingly influencing our personal and work lives through the proliferation of conversational agents in various domains. As such, these agents combine intuitive natural language interactions by also delivering personalization through artificial intelligence capabilities. However, research on CAs as well as practical failures indicate that CA interaction oftentimes fails miserably. To reduce these failures, this paper introduces the concept of building common ground for more successful human-agent interactions. Based on a systematic review our analysis reveals five mechanisms for achieving common ground: (1) Embodiment, (2) Social Features, (3) Joint Action, (4) Knowledge Base, and (5) Mental Model of Conversational Agents. On this basis, we offer insights into grounding mechanisms and highlight the potentials when considering common ground in different human-agent interaction processes. Consequently, we secure further understanding and deeper insights of possible mechanisms of common ground in human-agent interaction in the future

Decision Process in Human-Agent Interaction: Extending Jason Reasoning Cycle

The main characteristic of an agent is acting on behalf of humans. Then, agents are employed as modeling paradigms for complex systems and their implementation. Today we are witnessing a growing increase in systems complexity, mainly when the presence of human beings and their interactions with the system introduces a dynamic variable not easily manageable during design phases. Design and implementation of this type of systems highlight the problem of making the system able to decide in autonomy. In this work we propose an implementation, based on Jason, of a cognitive architecture whose modules allow structuring the decision-making process by the internal states of the agents, thus combining aspects of self-modeling and theory of the min

HAI Alice -An Information-Providing Closed-Domain Dialog Corpus

International audienceThe contribution of this paper is twofold: 1) we provide a public corpus for Human-Agent Interaction (where the agent is controlled by a Wizard of Oz) and 2) we show a study on verbal alignment in Human-Agent Interaction, to exemplify the corpus' use. In our recordings for the Human-Agent Interaction Alice-corpus (HAI Alice-corpus), participants talked to a wizarded agent, who provided them with information about the book Alice in Wonderland and its author. The wizard had immediate and almost full control over the agent's verbal and nonverbal behavior, as the wizard provided the agent's speech through his own voice and his facial expressions were directly copied onto the agent. The agent's hand gestures were controlled through a button interface. Data was collected to create a corpus with unexpected situations, such as misunderstandings, (accidental) false information, and interruptions. The HAI Alice-corpus consists of transcribed audio-video recordings of 15 conversations (more than 900 utterances) between users and the wizarded agent. As a use-case example, we measured the verbal alignment between the user and the agent. The paper contains information about the setup of the data collection, the unexpected situations and a description of our verbal alignment study

LIMITS OF DISCOURSE: EXAMPLES FROM POLITICAL, ACADEMIC, AND HUMAN-AGENT INTERACTION

This contribution looks at modern discourse from two perspectives. It tries to show that the term ‘discourse’ has been expanded over the last few decades to include more phenomena and more disciplines that use it as a basis for their analyses. But it also tries to show that discourse in the sense of effective interaction has met its limits. The fundamental question is: When is discourse real discourse, i.e. more than a series of unrelated utterances and when is it coherent interactive communication? This paper does not intend to provide a new overall theoretical-methodological model, it uses examples from political discourse to demonstrate that popular discourse is often unfortunately less interactive than seems necessary, examples from academic discourse to illustrate that community conventions are being standardised more and more, and from humanoid-human discourse to argue that it is still difficult to construct agents that are recognised as discourse partners by human beings. Theoretical approaches to discuss these limits of discourse include coherence andintentionality. They can be applied to show where lack of cohesion in discourse indicates lack of cohesion in society

Generic Multimodal Ontologies for Human-Agent Interaction

Watching the evolution of the Semantic Web (SW) from its inception to these days we can easily observe that the main task the developers face while building it is to encode the human knowledge into ontologies and the human reasoning into dedicated reasoning engines. Now, the SW needs to have efficient mechanisms to access information by both humans and artificial agents. The most important tools in this context are ontologies. The last years have been dedicated to solving the infrastructure problems related to ontologies: ontology management, ontology matching, ontology adoption, but as time goes by and these problems are better understood the research interests in this area will surely shift towards the way in which agents will use them to communicate between them and with humans. Despite the fact that interface agents could be bilingual, it would be more efficient, safe and swift that they should use the same language to communicate with humans and with their peers. Since anthropocentric systems entail nowadays multimodal interfaces, it seems suitable to build multimodal ontologies. Generic ontologies are needed when dealing with uncertainty. Multimodal ontologies should be designed taking into account our way of thinking (mind maps, visual thinking, feedback, logic, emotions, etc.) and also the processes in which they would be involved (multimodal fusion and integration, error reduction, natural language processing, multimodal fission, etc.). By doing this it would be easier for us (and also fun) to use ontologies, but in the same time the communication with agents (and also agent to agent talk) would be enhanced. This is just one of our conclusions related to why building generic multimodal ontologies is very important for future semantic web applications