System Evolution for Unknown Context through Multi-action Evaluation

Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular context change. One way is for the system developers to encompass all possible context changes in the domain knowledge. Then, the system matches a context change to that in the domain knowledge and chooses the corresponding action. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, there are situations where a system encounters unknown contexts. In such cases, instead of one action being implemented and evaluated, multiple actions could be implemented concurrently. This parallel evaluation of actions could quicken the evolution time taken to select the best action suited to unknown context compared to the iterative approach. This paper proposes a framework for context-aware systems that finds the best action for unknown context through multi-action evaluation and self-adaptation. In a case study, we show how our multi-action evaluation system can be implemented for a hypothetical hotelier who uses the name-your-own-price mechanism to sell his perishable inventory.