MACA: A Multi-Agent Cognitive Adaptation Framework for Human-Agent Collaborative Decision Making

Modern web interfaces increasingly support complex decision workflows, such as travel planning and multi-criteria selection, yet remain largely static and insensitive to users' moment-to-moment cognitive states during interaction. Travel planning, in particular, requires users to synthesize dispersed information under multiple constraints, making it a representative high-load interactive decision task. This study presents MACA (Multi-Agent Cognitive Adaptation), a framework that enables real-time cognitive adaptation in web-based decision environments by integrating hierarchical Monte Carlo Tree Search with a Planner–Critic–Executor multi-agent architecture. MACA continuously estimates users' emotional and attentional states using facial expression analysis (ResEmoteNet) and gaze stability tracking (MediaPipe), and uses these signals to regulate agent collaboration, reasoning depth, and feedback pacing during interaction. We evaluated MACA in a 2×2 within-subject study (N = 30) comparing Single versus Multi-agent and Fixed versus Adaptive configurations. Results show that the Multi-Adaptive condition significantly improved decision quality (F(3,116) = 2.96, p = 0.035) while reducing mental effort (F(3,116) = 2.82, p = 0.042), yielding a 10.7% gain in decision efficiency without increasing cognitive burden. These findings demonstrate that multimodal user-state sensing combined with cooperative multi-agent reasoning can enhance interactive web-based decision making while maintaining user well-being.