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2016
Springer

An Iterative Decision-Making Scheme for Markov Decision Processes and Its Application to Self-adaptive Systems

3 years 17 days ago
An Iterative Decision-Making Scheme for Markov Decision Processes and Its Application to Self-adaptive Systems
Abstract. Software is often governed by and thus adapts to phenomena that occur at runtime. Unlike traditional decision problems, where a decision-making model is determined for reasoning, the adaptation logic of such software is concerned with empirical data and is subject to practical constraints. We present an Iterative Decision-Making Scheme (IDMS) that infers both point and interval estimates for the undetermined transition probabilities in a Markov Decision Process (MDP) based on sampled data, and iteratively computes a confidently optimal scheduler from a given finite subset of schedulers. The most important feature of IDMS is the flexibility for adjusting the criterion of confident optimality and the sample size within the iteration, leading to a tradeoff between accuracy, data usage and computational overhead. We apply IDMS to an existing self-adaptation framework Rainbow and conduct a case study using a Rainbow system to demonstrate the flexibility of IDMS.
Guoxin Su, Taolue Chen, Yuan Feng, David S. Rosenb
Added 03 Apr 2016
Updated 03 Apr 2016
Type Journal
Year 2016
Where FASE
Authors Guoxin Su, Taolue Chen, Yuan Feng, David S. Rosenblum, P. S. Thiagarajan
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