A certain type of artificial intelligence agent can learn the cause-and-effect basis of a navigation task during training.
Neural networks can learn to solve a wide range of problems, from recognizing cats in photos to steering a self-driving car. However, it is unclear whether these powerful pattern-recognition algorithms truly understand the tasks they are performing.
For example, instead of learning to detect lanes and focus on the road's horizon, a neural network tasked with keeping a self-driving car in its lane might learn to do so by watching the bushes at the side of the road.
MIT researchers have demonstrated that a specific type of neural network can learn the true cause-and-effect structure of the navigation task it is being trained to perform. Because these networks can understand the task directly from visual data, they should outperform other neural networks when navigating in a complex environment, such as one with dense trees or rapidly changing weather conditions.
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