20. March, 2019

How does a maze help to demonstrate Fetch.AI’s technology?

At Fetch.AI we are creating the infrastructure for tomorrow’s digital economy. We remain well on course for our mainnet launch later in the year, but this has been no easy task. Nor should it be – otherwise everyone would already have developed the technology themselves. Similarly, the concepts behind the technology of the future aren’t always straightforward for newcomers of the project to comprehend.

Fetch.AI operates at the convergence of innovations in artificial intelligence, machine learning and blockchain technology. On the network, Autonomous Economic Agents (AEAs) represent individuals and their assets, product manufacturers and service providers. These can interact with each other to execute economic exchanges without human supervision to construct solutions to complex problems. The adaptive and self-organising collective intelligence of the network enables this to take place.

In the Fetch.AI ecosystem, agents are able to communicate with each other using a universal language. This allows the agents to find each other, broker deals and complete transactions to find answers to problems humans would not have the time nor cognitive ability to solve by themselves. The new digital economy will need many of the same features that we see in existing markets such as communication, negotiation and trust. To demonstrate these features, we studied agents attempting to find an exit from a maze. In these simulations, all agents use a depth-first search algorithm to traverse the maze.

If we extend the simulations to many agents, their inability to coordinate leads to a wastage of resources. When an agent finds a way out of the maze, it doesn’t affect any of the other agents in the network as data cannot be exchanged. The crucial information that has been uncovered is immediately lost and the remaining agents continue their isolated efforts to find the exit. This is depicted in the video by the behaviour of the agents in the maze on the left of the screen.  

By contrast, the maze on the right side of the screen during the video illustrates what happens when we use the Fetch.AI framework to enable value exchange. Fetch.AI’s machine learning infrastructure enables collective intelligence. This allows agents to communicate with one another to increase economic efficiency. Deciding who to trade with, how and when to negotiate and determining the correct value for the information are all complex problems. When an agent successfully locates the exit, the yellow arcs show the value and knowledge of the exit being communicated between the agents and their movement being coordinated towards it as a result.

The Fetch.AI framework enables agents to represent a limitless number of people and assets. Machines fitted with agents will be able to communicate with each other, trading data from a huge range of sources to find the best solution. But let’s keep this simple for now by using a straightforward example. An agent in a dishwasher would be able to trade and negotiate with energy providers, whether they are localised networks or the national grid, to discover the cheapest time to function. The agent would then be able to autonomously switch the dishwasher on and off at the correct times. This benefits both the owner of the dishwasher and the energy provider. The owner of the dishwasher returns home to clean plates having spent the minimum amount on energy. Meanwhile, the energy provider has been able to minimise peaks and troughs in demand by deploying an agent to operate autonomously on the grid.

The effect of this technology can be multiplied across numerous household devices thanks to our unique, scalable ledger. By trading data, they can work together to provide a seamless, contextualised experience. The benefits of such technology would also radically improve efficiency in sectors such as transport and supply chains.

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