Multiagent uncertain reasoning and knowledge discovery with graphical models

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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.