arXiv:2602.02843v3 Announce Type: replace Abstract: When deciding how to act under uncertainty, agents may choose to act to reduce uncertainty or they may act despite that uncertainty. In communicative settings, an important way of reducing uncertainty is by asking clarification questions (CQs). We predict that the decision to ask a CQ depends on both contextual uncertainty and the cost of alternative actions, and that these factors interact: uncertainty should matter most when acting incorrectly is costly. We formalize this interaction in a computational model based on expected regret: how mu
The increasing complexity of AI systems and their deployment in real-world communicative settings necessitates a deeper understanding of how uncertainty and cost influence their decision-making processes.
Understanding how AI agents manage uncertainty and the costs associated with their actions is crucial for developing more reliable, safe, and efficient autonomous systems that can interact intelligently with humans.
This research provides a formal model for AI agents to decide between acting immediately or seeking clarification, potentially leading to more nuanced and context-aware AI communication strategies.
- · AI developers
- · Robotics
- · Human-AI interaction research
- · Autonomous systems
- · Systems with simplistic decision models
- · AI models lacking uncertainty handling
- · Applications requiring high-reliability communication
AI agents will become more adept at identifying when to ask for clarification versus making assumptions, leading to fewer errors in complex tasks.
Improved communication strategies in AI systems could enhance collaboration between humans and machines, especially in high-stakes environments.
The development of sophisticated uncertainty-aware communication models could pave the way for AI systems that proactively manage ambiguity in novel or rapidly changing situations.
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