Migratable AI : Investigating users' affect on identity and information migration of a conversational AI agent

• URL: http://arxiv.org/abs/2010.13319v2
• Date: Sat, 4 Sep 2021 21:27:54 GMT
• Title: Migratable AI : Investigating users' affect on identity and information migration of a conversational AI agent
• Authors: Ravi Tejwani, Boris Katz, Cynthia Breazeal
• Abstract summary: We present a 2x2 between-subjects study in a task-based scenario using information migration and identity migration as parameters. Our results show that users reported highest joy and were most surprised when both the information and identity was migrated; and reported most anger when the information was migrated without the identity of their agent.
• Score: 25.029958885340058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational AI agents are becoming ubiquitous and provide assistance to us in our everyday activities. In recent years, researchers have explored the migration of these agents across different embodiments in order to maintain the continuity of the task and improve user experience. In this paper, we investigate user's affective responses in different configurations of the migration parameters. We present a 2x2 between-subjects study in a task-based scenario using information migration and identity migration as parameters. We outline the affect processing pipeline from the video footage collected during the study and report user's responses in each condition. Our results show that users reported highest joy and were most surprised when both the information and identity was migrated; and reported most anger when the information was migrated without the identity of their agent.

Related papers

• A Survey on Complex Tasks for Goal-Directed Interactive Agents [60.53915548970061]
  This survey compiles relevant tasks and environments for evaluating goal-directed interactive agents. An up-to-date compilation of relevant resources can be found on our project website.
  arXiv  Detail & Related papers  (2024-09-27T08:17:53Z)
• Caution for the Environment: Multimodal Agents are Susceptible to Environmental Distractions [68.92637077909693]
  This paper investigates the faithfulness of multimodal large language model (MLLM) agents in the graphical user interface (GUI) environment. A general setting is proposed where both the user and the agent are benign, and the environment, while not malicious, contains unrelated content. Experimental results reveal that even the most powerful models, whether generalist agents or specialist GUI agents, are susceptible to distractions.
  arXiv  Detail & Related papers  (2024-08-05T15:16:22Z)
• Learning from Emotions, Demographic Information and Implicit User Feedback in Task-Oriented Document-Grounded Dialogues [59.516187851808375]
  We introduce FEDI, the first English dialogue dataset for task-oriented document-grounded dialogues annotated with demographic information, user emotions and implicit feedback. Our experiments with FLAN-T5, GPT-2 and LLaMA-2 show that these data have the potential to improve task completion and the factual consistency of the generated responses and user acceptance.
  arXiv  Detail & Related papers  (2024-01-17T14:52:26Z)
• Exploring Platform Migration Patterns between Twitter and Mastodon: A User Behavior Study [13.424528400470198]
  A recent surge of users migrating from Twitter to alternative platforms, such as Mastodon, raised questions regarding what migration patterns are. In this study, we elaborate on how we investigate these questions by collecting data over 10,000 users who migrated from Twitter to Mastodon within the first ten weeks following the ownership change of Twitter.
  arXiv  Detail & Related papers  (2023-05-16T06:07:50Z)
• Information Design in Multi-Agent Reinforcement Learning [61.140924904755266]
  Reinforcement learning (RL) is inspired by the way human infants and animals learn from the environment. Research in computational economics distills two ways to influence others directly: by providing tangible goods (mechanism design) and by providing information (information design)
  arXiv  Detail & Related papers  (2023-05-08T07:52:15Z)
• Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification [78.08536797239893]
  We propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules. MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips. We show that MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.
  arXiv  Detail & Related papers  (2023-01-02T05:17:31Z)
• INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions [47.90088587508672]
  InSCIt is a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations. We report results of two systems based on state-of-the-art models of conversational knowledge identification and open-domain question answering.
  arXiv  Detail & Related papers  (2022-07-02T06:18:12Z)
• Migratable AI: Personalizing Dialog Conversations with migration context [25.029958885340058]
  We collected a dataset from the dialog conversations between crowdsourced workers with the migration context. We trained the generative and information retrieval models on the dataset using with and without migration context. We believe that the migration dataset would be useful for training future migratable AI systems.
  arXiv  Detail & Related papers  (2020-10-22T22:23:03Z)
• Migratable AI: Effect of identity and information migration on users perception of conversational AI agents [20.23411433036311]
  We explore the effects of information migration and identity migration on user perceptions of trust, competence, likeability, and social presence. Our results suggest that identity migration had a positive effect on trust, competence, and social presence, while information migration had a positive effect on trust, competence, and likeability.
  arXiv  Detail & Related papers  (2020-07-11T15:46:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.