Windowed A-K-MDP
Markov decision processes (MDPs) are used to support decision-making in conservation of biodiversity, but policies, even over small state spaces, can be difficult to interpret for conservation managers. K-MDP methods address this problem by building simpler MDPs with at most K abstract states. We show that the previously proposed A-K-MDP algorithm that relies on selecting a discretisation divisor using binary search can skip better abstract states. To fix this issue, we propose Windowed A-K-MDP, an algorithm that generates every distinct feasible partition induced within a declared divisor window and evaluates candidates until reaching the ideal value loss (J = 0) or exhausting the family...
| Authors | Xiangwen Yang, Frankie Cho, Iadine Chades |
|---|---|
| Primary category | cs.AI |
| Published | 12 September 2026 |
| Last updated at source | 1 October 2026 |
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