Challenges in Human-Agent Communication (2412.10380v1)

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.

• The paper identifies primary communication challenges spanning agent-to-user, user-to-agent, and general issues.
• The paper employs communication grounding theory to analyze transparency in conveying intentions and mitigating errors.
• The paper recommends developing robust dialog systems to enhance user understanding, trust, and effective AI collaboration.

Challenges in Human-Agent Communication

The paper, "Challenges in Human-Agent Communication," by Bansal et al., investigates the complexities and obstacles associated with human-agent interactions, particularly in the context of modern AI systems powered by generative foundation models. These agents, capable of tool use and environmental interaction, introduce unique challenges due to their sophisticated problem-solving abilities and potential failure modes, impacting both digital and physical realms.

Key Challenges and Framework

The authors categorize the encountered communication challenges into three broad categories:

1. Agent to User Communication: These challenges (A1–A5) focus on what agents can convey to users. Understanding the capabilities of an AI system is fundamental for users to make informed decisions regarding its utility. Additionally, as agents undertake tasks, conveying their planned and current actions, as well as any achieved goals or unexpected side effects, ensures transparency and trust in AI systems.
2. User to Agent Communication: Here, the challenges (U1–U3) revolve around effectively capturing the user's intentions and preferences. Accurately discerning the objectives and constraints specified by users is crucial to align outputs with expectations. Moreover, the iterative communication processes that allow users to iteratively refine their goals and provide feedback are central to improving agent performance.
3. General Communication Issues: These challenges (X1–X4) span overarching communication problems, such as ensuring consistency in agent output, determining the appropriate level of detail in communication, managing large interaction contexts, and verifying user comprehension of agent behavior.

Communication Grounding Perspective

The paper draws upon the communication grounding theory to explore these interactions, focusing on the development of common ground between agents and users. Grounding involves establishing a mutual understanding regarding the agent's abilities and behaviors and the user's goals and constraints. Addressing these challenges involves ensuring the agent's actions are comprehensible and predictable to users, which is vital for effective cooperation and collaboration.

Implications and Future Directions

The paper highlights significant implications for both practice and research. Practically, effective human-agent communication ensures user confidence and operational effectiveness, preventing costly errors and misuse of resources. Theoretically, these insights prompt the development of new frameworks and design principles to enhance agent transparency and user control mechanisms.

For future developments, the paper implies the need for robust dialog systems that intelligently manage user-agent interactions, facilitating user empowerment through seamless communication and reducing the chances of over-reliance or misinterpretation of AI capabilities.

Conclusion

In summary, the paper emphasizes the importance of developing strategies to overcome communication challenges posed by modern AI systems. This involves supporting effective information exchange between human users and agents to establish trust and ensure accuracy in collaborative endeavors. The authors call for a concerted effort in AI research to devise new design patterns and guidelines that prioritize establishing common ground, transparency, and user agency in human-agent interactions.