This landmark achievement was a decade ahead of its time. AlphaGo's 4-1 victory in Seoul, South Korea, on March 2016 was watched by over 200 million people worldwide. AlphaGo won the first ever game against a Go professional with a score of 5-0.ĪlphaGo then competed against legendary Go player Mr Lee Sedol, the winner of 18 world titles, who is widely considered the greatest player of the past decade. In October 2015, AlphaGo played its first match against the reigning three-time European Champion, Mr Fan Hui. AlphaGo went on to defeat Go world champions in different global arenas and arguably became the greatest Go player of all time. This process is known as reinforcement learning. Over time, AlphaGo improved and became increasingly stronger and better at learning and decision-making. Then we had it play against different versions of itself thousands of times, each time learning from its mistakes. We introduced AlphaGo to numerous amateur games to help it develop an understanding of reasonable human play. The other neural network, the “value network”, predicts the winner of the game. One neural network, the “policy network”, selects the next move to play. These neural networks take a description of the Go board as an input and process it through a number of different network layers containing millions of neuron-like connections. We created AlphaGo, a computer program that combines advanced search tree with deep neural networks. Encouraging results are obtained and discussed.To capture the intuitive aspect of the game, we needed a new approach. A relevant use case is introduced and discussed to demonstrate the feasibility of the intended vision.
The framework vision is achieved by exploiting (i) the capabilities of programmable data planes to enable real-time in-network telemetry collection (ii) the potential of P4 – as an important example of data plane programming languages – and AI to (re)write the source code of network components in a fashion that the network becomes capable of automatically translating a policy intent into executable actions that can be enforced on the network components and (iii) the potential of blockchain and federated learning to enable decentralized, secure and trustable knowledge sharing between domains. In this vein, this article proposes a novel framework to empower fully distributed trustworthy SelfDNs across multiple domains. Recent advances in automation, data analysis, artificial intelligence, distributed ledger technologies (e.g., Blockchain), and data plane programming techniques have sparked the hope of the researchers’ community in exploring and leveraging these techniques towards realizing the much-needed vision of trustworthy self-driving networks (SelfDNs).
Along with the high demand for network connectivity from both end-users and service providers, networks have become highly complex and so has become their lifecycle management.
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