Читать книгу Cyberphysical Smart Cities Infrastructures. Optimal Operation and Intelligent Decision Making онлайн

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Every agent has a set of abilities and is good in them to an extent. The point of interest in MAS is how a sophisticated global behavior can emerge from a population of agents working together. A real‐life example of such behavior can be found in insects like ants and bees [58, 59]. One of the interesting goals of this research is to ultimately make agents that could self‐repair [60, 61].

The emerging behavior of MAS can be tailored by researchers to let the group of agents tackle various tasks such as rescue missions, traffic control, fun sports events, surveillance, and many more. Additionally, when fused with other fields, unexpected outcomes can occur. Take “Talking Heads” experiment by Luc Steels [62, 63] as an example showing a common vocabulary emerging through the interaction of agents with each other and their environment via a language game.

Now that we know about the fields and tasks that embodied AI can shine in, the question is how our agents should be trained. One may say it is good to directly train in the physical world and expose them to its richness. Although a valid solution, this choice comes with a few drawbacks. First, the training process in the real world is slow, and the process cannot be sped up or parallelized. Second, it is very hard to control the environment and create custom scenarios. Third, it is expensive, both in terms of power and time. Fourth, it is not safe, and improperly trained or not fully trained robots can hurt themselves, humans, animals, and other assets. Fifth, for the agent to generalize the training, it has to be done in plenty of different environments that is not feasible in this case.