Renewal of intelligent decision support systems laboratory

由于今天的软件的规模和复杂性，他们的关键作用，因此需要可靠性，并由于在计算机芯片和网络技术的进步，它已成为必要和可能的解决一个复杂的问题，在一个大的域由一组合作的计算机，称为代理。 根据不同的应用，代理需要根据不确定的局部知识和观察来确定其域的状态（分布式概率推理），或者识别是否存在满足其局部约束的全局解，如果存在，则找到这样的解（分布式约束满足），或者决定在面对不确定性时采取什么样的联合行动才能使他们的环境达到最理想的状态（分布式决策）。 我在9年前成立的智能决策支持系统实验室研究如何构建能够进行分布式概率推理、分布式约束满足和分布式决策的多智能体系统。 我们关注的Agent知识表示是多Agent图形模型，我们强调的性能问题是理论的合理性和计算效率。 多智能体算法执行这些任务的设计和分析，原型（目前分发给注册用户在30个国家）的实施和测试。 为了获得智能体执行任务所需的图形模型，本文还研究了从数据中发现知识和从专家中获取知识的方法。 实验室最后一次大规模购买计算机设备是在2001年。由于芯片技术的进步，现有设备的性能已经落后。 所要求的设备将更新实验室，配备最先进的计算机设备，以支持新算法的设计和评价以及原型系统的开发。

Due to scales and complexities of today's software, their critical roles and hence reliabilities required, and due to technological advances in computer chips and networking, it has become both necessary and possible to solve a complex problem in a large domain by a set of cooperating computers, called agents.  Depending on applications, agents need to determine what is the state of their domain based on uncertain local knowledge and observations (distributed probabilistic reasoning), or identify whether a global solution exists which satisfies their local constraints and if so find such a solution (distributed constraint satisfaction), or decide what is the joint action that would bring their environment to a most desirable state in the face of uncertainty (distributed decision making).      The Intelligent Decision Support Systems Laboratory that I founded 9 years ago studies how to build multiagent systems that are capable of distributed probabilistic reasoning, distributed constraint satisfaction, and distributed decision making.  Agent knowledge representation that we focus on is multiagent graphical models, and performance issues that we emphasize are theoretical soundness and computational efficiency.  Multiagent algorithms for performing these tasks are designed and analyzed, and prototypes (currently distributed to registered users in 30 countries) are implemented and tested.   To acquire graphical models needed by agents to perform their tasks, knowledge discovery from data and elicitation from expert are also investigated in the lab.    The last major purchase of computing equipment in the lab was in 2001. Due to advancement in chip technology, performance of the existing equipment is now falling behind.  The requested equipment will renew the lab with the state of the art computing equipment to support design and evaluation of new algorithms and development of prototype systems.