Belief-Space Control for Personalized Cancer Treatment via Active Inference
Cancer treatment is at the core a sequential decision-making problem with partial observability, latent patient heterogeneity, and explicit constraints on the budget for medical measurements. Unlike standard Reinforcement Learning (RL) approaches that control state trajectories, cancer treatments permanently modify patients' transition dynamics, changing how states evolve over time. We model cancer treatment as a belief-space planning problem using active inference, deriving an expected free-energy objective that unifies goal-directed control and information acquisition under measurement budgets without. We implement this framework using real clinical cancer data from the AACR Project GEN...
| Authors | Deniz Sargun, H. Bugra Tulay, C. Emre Koksal |
|---|---|
| Primary category | cs.AI |
| Published | 9 June 2026 |
| Last updated at source | 17 June 2026 |
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