During my latest mission, I was in charge of developing a strategy related to decentralized artificial intelligence in the context of what we call “Machine to machine economy” (M2M). In this article, I’ll explain why Multi-agent systems are key to creating new business models in the upcoming M2M economy and why this new era could be a major threat to your organization.

在我的最新任务中，我负责在所谓的“机器到机器经济”(M2M)的背景下制定与分散式人工智能相关的策略。 在本文中，我将解释为什么多代理系统是在即将到来的M2M经济中创建新业务模型的关键，以及为什么这个新时代可能对您的组织构成重大威胁。

Concretely, we expect machines to become “smarter” and soon be capable of making decisions and conducting transactions between themselves without any human interference. As a consequence, we will soon see new business models and customer relationships thanks to decentralized artificial intelligence.

具体来说，我们希望机器变得“更智能”，并很快能够在彼此之间进行决策和进行交易而不会受到任何人为干扰。 结果，由于分散的人工智能 ，我们很快就会看到新的业务模型和客户关系。

多代理系统 (Multi-agent systems)

Let us start by defining some key terms:

让我们首先定义一些关键术语：

Agents: Sophisticated computer programs that act autonomously on behalf of their users, across open and distributed environments, to solve a growing number of complex problems. Increasingly, however, applications require multiple agents that can work together. (1)

代理：复杂的计算机程序，可以在开放和分布式环境中代表用户自主执行操作，以解决日益复杂的问题。 但是，应用程序越来越需要可以一起工作的多个代理。 ( 1 )

Multi-agent system (MAS): A loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver. (2)

多主体系统(MAS)：一个松散耦合的软件主体网络，可以交互以解决超出每个问题解决者的个人能力或知识范围的问题。 ( 2 )

In simpler terms and using an example from Yoav Shoham and Kevin Leyton-Brown, you should imagine a personal software agent representing you on several e-commerce websites. For instance, let us assume that the task of this agent is to identify specific products available for sale in various online websites over time, and to purchase some of them on your behalf. In order to be successful, your agent will need to remember your preferences for products, your budget, and in general your knowledge about the environment in which it will operate.

用简单的术语，并使用Yoav Shoham和Kevin Leyton-Brown的示例，您应该想象一个在几个电子商务网站上代表您的个人软件代理。 例如，让我们假设该代理的任务是确定随时间推移可在各种在线网站上出售的特定产品，并代表您购买其中的一些产品。 为了获得成功，您的代理商需要记住您对产品的偏好，预算以及总体上对产品运行环境的了解。

Moreover, the agent will need to leverage your knowledge of other similar agents with which it will interact (in an auction, or agents representing other businesses).

此外，代理将需要利用您将与之交互的其他类似代理的知识(在拍卖中，或代表其他业务的代理)。

A collection of such agents forms a multi-agent system. To elaborate, a Multi-Agent System is “a loose ecosystem of various communicating Artificial Intelligences” (3). It is essentially the next iteration of agent-based systems. Some algorithms have proved to be quite interesting in the development of MAS (reinforcement learning, deep learning, deep convolutional networks, …).

这样的代理的集合形成多代理系统。 详细地说，多智能体系统是“一个由各种各样的交流人工智能组成的松散的生态系统”( 3 )。 从本质上讲，它是基于代理的系统的下一个迭代。 事实证明，某些算法在MAS的开发中非常有趣 (强化学习，深度学习，深度卷积网络等)。

Currently, MAS (in a decentralized artificial intelligence context) is still being researched. As such, the industrial application/scalability of a multi-agent system is still a few years away.

目前，MAS(在分散式人工智能环境中)仍在研究中。 这样，多智能体系统的工业应用/可扩展性还需要几年的时间。

A multi-agent system falls into one of two categories:

多主体系统属于以下两类之一：

As we have seen, agents can interact with each other. The communication or coordination between such agents can take many forms. However, it is also necessary to understand that they are also autonomous agents. As such, in several cases, agents have opposite objectives and, therefore, are not able to carry out any cooperative process that includes them.

如我们所见，代理可以彼此交互。 这些代理之间的通信或协调可以采取多种形式。 但是，也有必要了解它们也是自治代理。 因此，在某些情况下，代理具有相反的目标，因此无法执行包括它们的任何合作过程。

It is key to know that agents are equipped with a social capacity. This capacity can be defined by the “ability to exchange high-level messages (and not only data-bytes without an associated meaning) and carry out processes of social interaction with other agents (and/or humans) similar to those used by humans in their daily lives, establishing collective behaviours.” (4)

关键是要知道代理商具备社交能力 。 可以通过“交换高级消息(不仅是没有相关含义的数据字节)，还可以与其他代理(和/或人类)进行社交互动的能力来定义这种能力，类似于他们的日常生活，建立集体行为。” ( 4 )

Our current challenge is to build agents that can negotiate and cooperate with other agents. For example, in order to convince an agent to cooperate, it might be necessary to make a payment or offer a particular good or service.

我们当前的挑战是建立可以与其他代理商进行谈判和合作的代理商。 例如，为了说服代理商合作，可能需要付款或提供特定的商品或服务。

Moreover, we are also paying attention to Federated Learning.

此外，我们还关注联合学习。

Federated learning: A machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. (5)

联合学习：一种机器学习技术，可跨多个分散的边缘设备或保存本地数据样本的服务器训练算法，而无需交换它们。 ( 5 )

Indeed, we believe that AI and real-time data processing must occur on edge networks and edge devices (IoT devices, etc). Is it key for some products to collect data from sensors to make decisions in real-time with no dependency on the cloud or internet. They should also be able to learn or train themselves using algorithms onboard and share their learnings with other products.

确实，我们相信AI和实时数据处理必须在边缘网络和边缘设备(IoT设备等)上进行。 对于某些产品而言，从传感器收集数据以在不依赖云或互联网的情况下实时做出决策是关键吗？ 他们还应该能够使用船上算法学习或培训自己，并与其他产品分享他们的经验。

机器对机器的经济 (Machine-to-Machine economy)

With the rise of AI, smart sensors, decentralized P2P transactional protocols, and Blockchain, we are moving into a machine economy. Machines will eventually become economically independent. An object (ex: car) will be able to spend money and earn money. The machines will make decisions for us, which explains the AI aspect, but we need to trust them — which is where blockchain comes in.

随着AI，智能传感器，去中心化的P2P交易协议和区块链的兴起，我们正在进入机器经济。 机器最终将在经济上变得独立。 一个对象(例如：汽车)将能够花钱并赚钱。 机器将为我们做出决定，这解释了AI方面，但我们需要信任它们-这就是区块链的所在。

Our future customers will be machines with wallets.

我们未来的客户将是带有钱包的机器。

We envision a future in which machines can discover, automatically connect with other machines (using public or private networks, for instance, a mesh network), make their own choices thanks to AI (Reinforcement Learning), and independently purchase what they need. We assume that machines will sooner or later have integrated wallets too.

我们预见了机器可以发现，自动与其他机器连接(使用公共或专用网络，例如网状网络)，借助AI(强化学习)做出自己的选择以及独立购买所需机器的未来。 我们假设机器迟早也会集成钱包。

An IoT device will no longer be considered as an isolated product that needs to learn everything from scratch on its own; these devices will leverage the mass learning learned by other similar IoT devices worldwide as well. This means that intelligent systems of IoT trained by machine learning are not just becoming smarter; they are getting smarter faster over time in exponential trends.

物联网设备将不再被视为孤立的产品，需要自己从头开始学习一切； 这些设备还将利用全球其他类似物联网设备所学到的大量知识。 这意味着通过机器学习训练的物联网智能系统不仅变得更加智能； 随着时间的流逝，他们正变得越来越聪明。

Moreover, this M2M economy will also be the one in which most customers will be charged by how much they use or consume a product. This is a major shift in the way we purchase products.

此外，这种M2M经济也将是大多数客户将按使用或消费某种产品的价格来收费的一种方式。 这是我们购买产品方式的重大转变。

In the automotive industry, we could imagine vehicles that can seamlessly connect and communicate (using Multi-Agent Deep Reinforcement Learning) with other vehicles, roads, traffic lights, parking meters, gas pumps and even private companies such as Uber. More broadly, we can envision a society in which cars, drones, or buildings negotiate directly with each other to achieve their objectives without the necessity for human involvement.

在汽车行业中，我们可以想象能够与其他车辆 ，道路，交通信号灯，停车收费表，加油站甚至私人公司(例如Uber)无缝连接和通信 (使用多智能体深度强化学习)的车辆 。 更广泛地说，我们可以设想一个这样的社会：汽车，无人机或建筑物可以彼此直接协商以实现其目标，而无需人类参与。

To create new machine centered business models in the machine-to-machine economy, we must first improve our AIoT strategy by better using data network effects.

为了在机器对机器经济中创建新的以机器为中心的业务模型，我们必须首先通过更好地利用数据网络效应来改进AIoT策略。

Machines are the customer of the future …

机器是未来的客户...

From a technical perspective, building a truly autonomous and scalable machine with the ability to make decisions beyond the context of a very specific purpose is still something extremely difficult.

从技术角度来看，构建真正的自治且可扩展的机器并具有超出特定目的范围进行决策的能力仍然非常困难。

The transformation of our products follows, more or less, the same roadmap established by the Commonwealth Bank of Australia. We three necessary steps in the evolution of the machine-to-machine economy

我们产品的转型或多或少地遵循了澳大利亚联邦银行制定的相同路线图。 我们在机器对机器经济发展中的三个必要步骤

新机遇与挑战 (New opportunities & threats)

Transitioning into an economy in which most customers are machines will have a major impact.

向大多数客户为机器的经济过渡将产生重大影响。

• How do you convince an AI agent to purchase from your company?

  您如何说服AI代理商向您的公司购买？

• How do you manage “customer” loyalty in the M2M economy?

  您如何管理M2M经济中的“客户”忠诚度？

• How can you ensure to remain relevant in this machine powered economy?

  您如何确保在这种以机器为动力的经济中保持相关性 ？

This situation will create new dynamics in your industry. Today’s customers who will rely on AI agents might shift to more “AI-friendly” businesses or some companies might create specific exclusive environments. At the same time, you must take into consideration that your products might independently generate additional revenue.

这种情况将在您的行业中创造新的动力。 如今，将依赖于AI代理的客户可能会转向更具“ AI友好性”的业务，或者某些公司可能会创建特定的专有环境。 同时，您必须考虑到您的产品可能独立产生额外的收入。

For instance, an autonomous car owner, in the M2M economy, can benefit from providing rides to other people or by selling or renting data sets to AI companies. One of our projects is to think about an incentive for this case. Indeed, as an incentive, the car could receive bitcoin or some tokens.

例如，在M2M经济中，自动驾驶汽车所有者可以从为他人提供乘车服务或通过向AI公司出售或租借数据集中受益。 我们的项目之一是考虑这种情况的诱因。 确实，作为激励措施，汽车可以接收比特币或一些代币。

We are already concerned with the concept of trust between agents. Indeed, trust can be seen as the quality and quantity of interactions among agents: the more interactions occur between two parts, the more one trusts the other. Would it be easy to change this aspect in a competitive market driven by decentralized AI?

我们已经关心代理之间的信任的概念。 确实，信任可以看作是代理之间交互的质量和数量：两个部分之间发生的交互越多，一个成员对另一个成员的信任就越多。 在去中心化AI驱动的竞争性市场中改变这一方面是否容易？

This issue of trust between agents can be solved by the Dynamic Interaction Based Reputation Model (DIB-RM) that was introduced to capture this dynamic property of trust. This model computes a reputation value for each agent on the system combining different dynamic factors. The reputation value is updated at each interaction.

可以通过引入基于动态交互的信誉模型(DIB-RM)来解决代理之间的信任问题， 该模型用于捕获信任的这种动态属性。 此模型结合了不同的动态因素，为系统上的每个代理计算信誉值。 信誉值在每次交互时都会更新。

Beyond obvious technical issues, companies must also transition from siloed solutions to a shared and trustworthy method of communication. Indeed, MAS can only be feasible for all industries if special protocols are developed for it. It will be hard for MAS to work under current data-driven protocols.

除了明显的技术问题之外，公司还必须从孤立的解决方案过渡到共享且可信赖的通信方法。 的确，只有为MAS开发了专用协议，MAS才对所有行业都可行。 MAS将很难在当前的数据驱动协议下工作。

有关更多信息，我建议以下链接： (For more information, I recommend the following links:)

• Dawn of the machine-to-machine age and its implications for insurance

  机器对机器时代的黎明及其对保险业的影响

• Why Multi-Agent Systems will Make AI Better

  为什么多智能体系统将使AI更好

• Distributed Artificial Intelligence (Part I): A primer on MAS, ABM, and Swarm Intelligence

  分布式人工智能(第一部分)：MAS，ABM和Swarm Intelligence入门

• Introductory Chapter: Multi-Agent Systems

  介绍性章节：多智能体系统

• Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning

  协同多主体深度强化学习的微观交通模拟

• Multiagent Systems in Automotive Applications

  汽车应用中的多代理系统

• Welcome to the machine-to-machine economy

  欢迎来到机器对机器经济

• Multi-Agent Systems

  多代理系统

• Reputation in M2M Economy

  在M2M经济中的声誉

• Towards dynamic interaction-based reputation models

  建立基于动态交互的信誉模型

• Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security

  使用多代理系统和人工智能来监视和改善云性能和安全性

• Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations

  多主体系统：算法，博弈论和逻辑基础

• Multi-agent systems and decentralized artificial super intelligence

  多智能体系统和分散式人工智能