Simulating User Agents for Embodied Conversational-AI

• URL: http://arxiv.org/abs/2410.23535v1
• Date: Thu, 31 Oct 2024 00:56:08 GMT
• Title: Simulating User Agents for Embodied Conversational-AI
• Authors: Daniel Philipov, Vardhan Dongre, Gokhan Tur, Dilek Hakkani-Tür,
• Abstract summary: We build a large language model (LLM)-based user agent that can simulate user behavior during interactions with an embodied agent. We evaluate our user agent's ability to generate human-like behaviors by comparing its simulated dialogues with the TEACh dataset.
• Score: 9.402740034754455
• License:
• Abstract: Embodied agents designed to assist users with tasks must engage in natural language interactions, interpret instructions, execute actions, and communicate effectively to resolve issues. However, collecting large-scale, diverse datasets of situated human-robot dialogues to train and evaluate such agents is expensive, labor-intensive, and time-consuming. To address this challenge, we propose building a large language model (LLM)-based user agent that can simulate user behavior during interactions with an embodied agent in a virtual environment. Given a user goal (e.g., make breakfast), at each time step, the user agent may observe" the robot actions or speak" to either intervene with the robot or answer questions. Such a user agent assists in improving the scalability and efficiency of embodied dialogues dataset generation and is critical for enhancing and evaluating the robot's interaction and task completion ability, as well as for research in reinforcement learning using AI feedback. We evaluate our user agent's ability to generate human-like behaviors by comparing its simulated dialogues with the TEACh dataset. We perform three experiments: zero-shot prompting to predict dialogue acts, few-shot prompting, and fine-tuning on the TEACh training subset. Results show the LLM-based user agent achieves an F-measure of 42% with zero-shot prompting and 43.4% with few-shot prompting in mimicking human speaking behavior. Through fine-tuning, performance in deciding when to speak remained stable, while deciding what to say improved from 51.1% to 62.5%. These findings showcase the feasibility of the proposed approach for assessing and enhancing the effectiveness of robot task completion through natural language communication.

Related papers

• ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents [11.118991548784459]
  Large language model (LLM)-based agents have been increasingly used to interact with external environments. Current frameworks do not enable these agents to work with users and interact with them to align on the details of their tasks. This work introduces ReSpAct, a novel framework that combines the essential skills for building task-oriented "conversational" agents.
  arXiv  Detail & Related papers  (2024-11-01T15:57:45Z)
• Self-Directed Turing Test for Large Language Models [56.64615470513102]
  The Turing test examines whether AIs can exhibit human-like behaviour in natural language conversations. Traditional Turing tests adopt a rigid dialogue format where each participant sends only one message each time. This paper proposes the Self-Directed Turing Test, which extends the original test with a burst dialogue format.
  arXiv  Detail & Related papers  (2024-08-19T09:57:28Z)
• A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation [39.87346821309096]
  We present an addressee estimation model with improved performance in comparison with the previous SOTA. We also propose several ways to incorporate explainability and transparency in the aforementioned architecture.
  arXiv  Detail & Related papers  (2024-05-20T13:09:32Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback [42.19685958922537]
  We argue that human-AI collaboration should be interactive, with humans monitoring the work of AI agents and providing feedback that the agent can understand and utilize. In this work, we explore these directions using the challenging task defined by the IGLU competition, an interactive grounded language understanding task in a MineCraft-like world.
  arXiv  Detail & Related papers  (2023-04-21T05:37:59Z)
• Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator [37.590563896382456]
  We propose an interactive evaluation framework for Task-Oriented Dialogue (TOD) systems. We first build a goal-oriented user simulator based on pre-trained models and then use the user simulator to interact with the dialogue system to generate dialogues. Experimental results show that RL-based TOD systems trained by our proposed user simulator can achieve nearly 98% inform and success rates.
  arXiv  Detail & Related papers  (2022-10-26T07:41:32Z)
• Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents [60.27066549589362]
  Social language used by human agents is associated with greater users' responsiveness and task completion. The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element. Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
  arXiv  Detail & Related papers  (2020-12-29T08:22:48Z)
• How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics [47.20761880464552]
  generative dialogue modeling is widely seen as a language modeling task. The task demands an agent to have a complex natural language understanding of its input text to carry a meaningful interaction with an user. The automatic metrics used evaluate the quality of the generated text as a proxy to the holistic interaction of the agent.
  arXiv  Detail & Related papers  (2020-08-24T13:28:35Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)
• SPA: Verbal Interactions between Agents and Avatars in Shared Virtual Environments using Propositional Planning [61.335252950832256]
  Sense-Plan-Ask, or SPA, generates plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments. We find that our algorithm creates a small runtime cost and enables agents to complete their goals more effectively than agents without the ability to leverage natural-language communication.
  arXiv  Detail & Related papers  (2020-02-08T23:15:06Z)

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.