Bongard et al. designed a robot with an algorithm of its stored sensory data to indirectly infer its physical structure. The robot was able to generate forward motion more adaptively by manipulating its gait to compensate for simulated injuries. Adami equates this algorithm to "dreams" of prior actions and asks whether such modeling could extend to environmental mapping algorithms. If this were possible, then a robot could explore a landscape until it is challenged by an obstacle; overnight, it could replay its actions against its model of the environment and generate (or synthesize) new actions to overcome the obstacle (i.e., "dream up" alternative strategies). It could then return the next day with a new approach to the obstacle......
This work in robotics complements current findings regarding sleep and dreaming in humans. There is now strong evidence in human sleep research showing that performance on motor and visual 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). 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. 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. This could be a way to generate unique approaches to scenarios that could be simulated. Otherwise, how else would scenario replay be an improvement over repeated trials in the environment?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. 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. This could be a way to generate unique approaches to scenarios that could be simulated. Otherwise, how else would scenario replay be an improvement over repeated trials in the environment?
After a further comment letter from C. Adami, Lipson, Zykov and Bongard (the original authors) comment:
The analogy between machine and human cognition may suggest that reported bizarre, random dreams may not be entirely random. The robot we described did not just replay its experiences to build consistent internal self-models and then "dream up" an action based on those models. Instead, it synthesized new brief actions that deliberately caused its competing internal models to disagree in their predictions, thus challenging them to falsify less plausible theories and, as a result, improving its overall knowledge of self. It is possible that the mangled experiences that people report as bizarre dreams correspond to this unconscious search for actions able to clarify their self-perceptions. Many of the intermediate candidate models and actions developed by the robot (as seen in Movie S1 in our Supporting Online Material) were indeed very contorted, but were optimized nonetheless to elucidate uncertainties. Edelman (1), Calvin (2), and others have suggested the existence of competitive processes in the brain. Perhaps the fact that human dreams appear mangled and brief is exactly because they are--as in the robot--"optimized" to challenge and improve these competing internal models?
Indeed, analogies between machines learning from past experiences and human dreaming are potentially very fruitful and may be applicable in both directions. Although robots and their onboard algorithms are clearly simpler and may bear little or no direct relation to humans and their minds, it may be much easier to test hypotheses about humans in robots. Conversely, ideas from human cognition research may help direct robotic research beyond merely serving as inspiration. Specifically, it is likely that as robots become more complex and their internal models are formed indirectly rather than being explicitly engineered and represented, indirect probing techniques developed for studying humans may become essential for analyzing machines too.
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