A Validation Method of Self-Adaptive Strategy Based on POMDP

Abstract

Self-Adaptive Systems (SASs) can dynamically adjust themselves to adapt to the changes, by planning some strategies to guide the adjustments. However, many uncertainties in runtime affect the efficiency of strategies and the ability to attain goals for SASs. So, it is necessary to validate strategies before they are executed. At present, most of the validation methods ignore the fact that less data can be observed at runtime, so it is difficult to describe the state accurately with these data. Most methods do not support uncertain state reasoning, but state transition is uncertain. This will result in the verification method not dealing well with the uncertainties. This paper proposes a strategy validation method based on Partially Observable Markov Decision Process, and makes several key contributions: (1) a uncertain state model that not only support description of states through partial information, but also describe the uncertain transitions of states, (2) a validation method to validate whether the strategies meets the requirements at runtime, (3) a strategy correction method to get effective strategies as quickly as possible.