Houzé, É.; Dessalles, J.-L.; Diaconescu, A.; Menga, D. What Should I Notice? Using Algorithmic Information Theory to Evaluate the Memorability of Events in Smart Homes. Entropy2022, 24, 346.
Houzé, É.; Dessalles, J.-L.; Diaconescu, A.; Menga, D. What Should I Notice? Using Algorithmic Information Theory to Evaluate the Memorability of Events in Smart Homes. Entropy 2022, 24, 346.
Houzé, É.; Dessalles, J.-L.; Diaconescu, A.; Menga, D. What Should I Notice? Using Algorithmic Information Theory to Evaluate the Memorability of Events in Smart Homes. Entropy2022, 24, 346.
Houzé, É.; Dessalles, J.-L.; Diaconescu, A.; Menga, D. What Should I Notice? Using Algorithmic Information Theory to Evaluate the Memorability of Events in Smart Homes. Entropy 2022, 24, 346.
Abstract
With the increasing number of connected devices, complex systems such as smart homes record a multitude of events of various types, magnitude and characteristics. Current systems struggle to identify which events can be considered more memorable than others. In contrast, human are able to quickly categorize some events as being more “memorable” than others. They do so without relying on knowledge of the system’s inner working or large previous datasets. Having this ability would allow the system to: i) identify and summarize a situation to the user by presenting only memorable events; ii) suggest the most memorable events as possible hypotheses in an abductive inference process. Our proposal is to use Algorithmic Information Theory to define a “memorability” score by retrieving events using predicative filters. We use smart-home examples to illustrate how our theoretical approach can be implemented in practice.
Keywords
Kolmogorov Complexity; Algorithmic Information Theory; Simplicity; Abduction; Memorability
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
