Human-oriented model predicts people’s evolving needs for better support
Human-Oriented World Model
Artificial IntelligenceComputer Vision and Pattern RecognitionMachine Learning
Summary
Helping someone well over time means understanding their current situation and guessing what might happen next. The authors propose a framework called HOWM that focuses on tracking important human states related to tasks and predicting how these change based on personal history and context. This model uses past experiences and new information to make better guesses about future behavior and adapt support accordingly. It aims to improve assistance by connecting actions with a person’s goals, preferences, and changing circumstances.
What this means in practice
- •For healthcare support teams: Enhance patient monitoring by continuously integrating current condition, history, and context to anticipate needs and improve personalized care.
- •For personalized learning developers: Design tutoring systems that adjust instruction dynamically by predicting student state changes and aligning with individual goals and preferences.
A position paper. It proposes an approach and reports no results.
Authors
Saihui Hou, Zengyun Mou, Chunshui Cao, Yongzhen Huang
Abstract
Supporting a person over repeated interactions requires interpreting their current state in context and anticipating its subsequent evolution. We formulate the \emph{Human-Oriented World Model} (HOWM) as a conceptual framework centered on task-relevant human states and their evolution. HOWM connects current observations and individual history with environmental and social context to organize continuing state estimation, personalized dynamics prediction, and action support. Dynamic expectations and new observations jointly inform the current estimate, which in turn anchors personalized forecasts based on shared knowledge and individual experience. An external policy relates forecasts under alternative conditions to the person's goals, preferences, and constraints when considering support. Actual responses and outcomes provide evidence for revising the state estimate and personal dynamic patterns, thereby informing subsequent estimation, prediction, and support. We illustrate the framework through hypothetical training and learning scenarios. Our research agenda asks whether accumulated interaction experience improves subsequent estimation and prediction, and whether using the model helps the person pursue their goals over time.