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The ability to predict the future is in some cases seen as a form of intelligence. Our brains are adjusted so that they can quickly compare the different outcomes of our actions. How do they do this?
Cully et al. show that robots can learn to recover quickly from physical damage. The underlying idea is that they must adopt a new strategy to be able to continue. This looks like instinct, but what the robots do is compare their previously learned strategies with each other so that the best strategy is chosen.
Three things are needed to make an accurate prediction about your behavior: experience, understanding how the world works and being able to judge how your own actions contrast with those of others.
Previous studies state that the ability to plan depends on the ability to program a representation of the world in a robot. If this succeeds, how can future actions be looked up quickly and efficiently?
In the research by Cully et al., The robots were asked to find the best strategy after being damaged. What happened was that robots, before they were damaged, had a baseline with possible solutions. After the damage, the possible movements were tried before they decided which behavior would best compensate for the damage.
Because of the embodiment of the robot, there are only a limited number of actions that can be performed the robot. The authors looked at all kinds of actions that a robot could perform and looked at the suitability of every motor movement. This was, for example, measured in "distance that the robot can travel". The robots were able to learn the new actions through special-purpose machine-learning algorithms.
The special-purpose machine-learning algorithms are not the same as the cognitive systems that we have, but both are strongly limited by embodiment. What you can and cannot do with your body is something you have to learn through experience and trial and error .
It is very difficult to imitate the functioning of our brains: the aforementioned algorithm was designed by researchers, while the way in which our brains have adjusted themselves is the result of millions of years of survival of the fittest . In the past it has not been possible to imitate our brains in terms of fast, intuitive and situation-specific behavior. That is why it is better to focus on adaptive and evolutionary algorithms.
Summaries per article with Artificial intelligences and Neurocognition at Leiden University 20/21
Summaries per article with Artificial intelligences and Neurocognition at Leiden University 20/21
Study Guide with article summaries for Artificial Intelligence at Leiden University
Article summaries with Artificial Intelligence at Leiden University
Table of content
- Modeling visual recognition from neurobiological constraints
- Achieving machine realization of truly human-like intelligence
- Untangling invariant object recognition
- Computing machinery and intelligence
- Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project
- Robots with instincts
- Male and female robots
- Speed of processing in the human visual system
- Sparse but not ""Grandmother-cell"" coding in the medial temporal lobe
- Perceptrons
- Learning and neural plasticity in visual object recognition
- Breaking position-invariant object recognition
- A feedforward architecture accounts for rapid categorization
- Hierarchical models of object recognition in cortex
- Articlesummaries with prescribed articles for AI - 2020/2021
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