Challenges in Human-Agent Communication

• URL: http://arxiv.org/abs/2412.10380v1
• Date: Thu, 28 Nov 2024 01:21:26 GMT
• Title: Challenges in Human-Agent Communication
• Authors: Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz, Adam Fourney, Hussein Mozannar, Victor Dibia, Daniel S. Weld,
• Abstract summary: We identify and analyze twelve key communication challenges that these systems pose.<n>These include challenges in conveying information from the agent to the user, challenges in enabling the user to convey information to the agent, and overarching challenges that need to be considered across all human-agent communication.<n>Our findings serve as an urgent call for new design patterns, principles, and guidelines to support transparency and control in these systems.
• Score: 55.53932430345333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remarkable advancements in modern generative foundation models have enabled the development of sophisticated and highly capable autonomous agents that can observe their environment, invoke tools, and communicate with other agents to solve problems. Although such agents can communicate with users through natural language, their complexity and wide-ranging failure modes present novel challenges for human-AI interaction. Building on prior research and informed by a communication grounding perspective, we contribute to the study of \emph{human-agent communication} by identifying and analyzing twelve key communication challenges that these systems pose. These include challenges in conveying information from the agent to the user, challenges in enabling the user to convey information to the agent, and overarching challenges that need to be considered across all human-agent communication. We illustrate each challenge through concrete examples and identify open directions of research. Our findings provide insights into critical gaps in human-agent communication research and serve as an urgent call for new design patterns, principles, and guidelines to support transparency and control in these systems.

Related papers

• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users. This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking [87.82985288731489]
  Agentic AI has emerged as a key paradigm for intelligent communications and networking. This article emphasizes the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems.
  arXiv  Detail & Related papers  (2025-02-24T06:02:25Z)
• Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems [11.522282769053817]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in reasoning, planning, and decision-making. Researchers have begun incorporating LLMs into multi-agent systems to tackle tasks beyond the scope of single-agent setups. This survey serves as a catalyst for further innovation, fostering more robust, scalable, and intelligent multi-agent systems.
  arXiv  Detail & Related papers  (2025-02-20T07:18:34Z)
• Modular Conversational Agents for Surveys and Interviews [6.019313905775819]
  This paper introduces a modular approach and its resulting parameterized process for designing AI agents. We demonstrate the adaptability, generalizability, and efficacy of our modular approach through three empirical studies. The results suggest that the AI agent increases completion rates and response quality.
  arXiv  Detail & Related papers  (2024-12-22T15:00:16Z)
• 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)
• ChatShop: Interactive Information Seeking with Language Agents [16.879814917881895]
  desire and ability to seek new information strategically are fundamental to human learning. We analyze a popular web shopping task designed to test language agents' ability to perform strategic exploration. We show that the proposed task can effectively evaluate the agent's ability to explore and gradually accumulate information.
  arXiv  Detail & Related papers  (2024-04-15T16:35:41Z)
• A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects [100.75759050696355]
  We provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. We discuss challenges that meet the real-world application needs but require a greater research focus in the future.
  arXiv  Detail & Related papers  (2023-05-04T11:38:49Z)
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society [58.04479313658851]
  This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents. We propose a novel communicative agent framework named role-playing. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems.
  arXiv  Detail & Related papers  (2023-03-31T01:09:00Z)
• Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations [59.608216900601384]
  We study agents that learn to communicate via actuating their joints in a 3D environment. We show that under realistic assumptions, a non-uniform distribution of intents and a common-knowledge energy cost, these agents can find protocols that generalize to novel partners.
  arXiv  Detail & Related papers  (2020-10-29T19:23:10Z)
• The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning [6.18778092044887]
  Many real-world problems require the coordination of multiple autonomous agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication strategies that enable complex multi-agent coordination. We show how a single self-interested agent is capable of learning highly manipulative communication strategies that allows it to significantly outperform a cooperative team of agents.
  arXiv  Detail & Related papers  (2020-08-06T12:48:08Z)

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.