Speaking about replay tools and information gathered in the past (see previous post), this paper entitled "What Do Robots Dream Of?" (by Christopher Adami) features this curious bit:

"How would dream-inspired algorithms work in terra incognita? A robot would spend the day exploring part of the landscape, and perhaps be stymied by an obstacle. At night, the robot would replay its actions and infer a model of the environment. Armed with this model, it could think of--that is, synthesize--actions that would allow it to overcome the obstacle, perhaps trying out those in particular that would best allow it to understand the nature of the obstacle. Informally, then, the robot would dream up strategies for success and approach the morning with fresh ideas."

This inspiration from dream is based on the discovery of cognitive processes that occur during sleep:

"There is now strong evidence in human sleep research showing that performance on motor (1) and visual (2) tasks is strongly dependent on sleep, with improvements consistently greater when sleep occurs between test and retest. This is generally believed to be related to neural recoding processes that are possibly connected to dreaming during sleep (3). However, when one considers human dreaming, it is not a simple replay of daily scenarios. It has complex, distorted images from a vast variety of times and places in our memory, arranged in a random, bizarre fashion (4). If we are to model such activity in robots, we would need to have some form of "sleep" algorithm that randomizes memory and combines it in unique arrays."

Why do I blog this? gathering some thoughts about histories of interaction and the usage of asynchrone data to foster more adaptive behavior.