Learning and Meta-Learning

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Computational agents will be able to express any computational algorithm, AI, or a machine learning engine, and will also be able to access information about their own and other agents’ capabilities through NuNet, as well as the history and activity in the network. Therefore, agents will be able to learn from experience about the credibility, efficiency, and security of other agents, and also about other dimensions and activities happening in the network. Different meta-agents may start to specialize in analyzing other agents’ reputations and rating their performance, and then providing this information to other agents in exchange for tokens or information. These intricate interactions ultimately will give rise to a decentralized ecosystem of reputation systems within the network, that humans and machine agents will be able to examine and rely upon when designing computational workflows. Overall, these capabilities will allow individual agents to learn from their own, or network, experience and become better at performing their tasks, and allow them to be adaptive to changing circumstances, new algorithms, cutting-edge AI engines, and novel use cases.