Multiagent uncertain reasoning and knowledge discovery with graphical models

由于当今软件的规模和复杂性以及所需的可靠性，并且由于计算机网络的进步和处理器成本的降低，通过一组协作的计算机（称为代理）来解决大型域中的复杂问题已经变得既必要又可能。 代理的一个共同任务是确定他们的域的状态，以便他们采取相应的行动。我将研究执行这种推理的替代方法之间的根本区别：紧耦合，代理交换有限的信念，松耦合，代理将来自另一个代理的所有内容视为观察。 构建这样的代理是具有挑战性的软件开发人员。我将研究如何自动化一些步骤的建设，例如，自动增强Agent接口，提高推理效率。这种代理的一个应用领域是供应链中的协同设计。 没有现有的方法，使代理人有效地构建一个最佳的设计。 继续我的进展，在上一个时期，我将分析的条件下，最佳设计可以有效地计算代理和开发这样一种方法。我研究的代理人配备了表示为概率图形模型的知识。 从数据中学习这样的模型是自动化代理开发的一种方法。 我的研究发现，一类称为PI模型的模型不能通过常用方法学习。 如果智能体配备了错误学习的模型，它可能会在执行任务时出错。 为了有效地学习这些模型，需要深入了解它们是如何由独立参数组成的。在我的最后一个研究阶段，对PI模型的一些子类已经有了这样的认识。我将进一步研究，以实现对整个PI模型类的透彻理解。 一旦PI模型的整个频谱的组成被理解，可以开发新一代的学习方法，这将提高从学习模型构建的代理的可靠性。

Due to the scale and complexity of today's software and the reliability required, and due to the advance in computer network and reduction in processor cost, it has become both necessary and possible to solve a complex problem in a large domain by a set of cooperating computers, called agents.  A common task of agents is to determine what is the state of their domain in order for them to act accordingly. I will study fundamental difference between alternative ways to perform such reasoning: tightly coupled, where agents exchange limited beliefs, and loosely coupled, where an agent treats everything from another agent as observations.  Constructing such agents is challenging to software developers. I will investigate ways to automate some steps of construction, e.g., to enhance agent interfaces automatically for improved reasoning efficiency. One application area of such agents is collaborative design in supply chains.  No existing method enables agents to construct an optimal design efficiently.  Continuing my progress in the last period, I will analyze the conditions under which an optimal design can be efficiently computed by agents and develop such a method. The agent that I study is equipped with knowledge represented as a probabilistic graphical model.  Learning such a model from data is one way to automate agent development.  My research found that a class of models, called PI models, cannot be learned by commonly used methods.  If an agent is equipped with an incorrectly learned model, it may make mistakes in carrying out its tasks.  In order to learn such models effectively, a deep understanding is needed on how they are composed from independent parameters. Such understanding on some subclasses of PI models has been obtained in my last period of research. I will further investigate to achieve thorough understanding for the entire class of PI models.  Once the composition of the entire spectrum of PI models is understood, a new generation of learning methods can be developed which will improve the reliability of the agents built from the learned models.