Automation and Contemplation for Model Adaptation in Multiagent Interactions

智能体是一种计算机系统，它在给定来自环境的感官输入后智能地行动。在许多实际应用中，代理技术已被证明是有效和可靠的解决方案，并将继续在现代社会中发挥重要作用。例如，eBay买家代理为电子市场中的人们推荐优惠交易。由自主代理运营的谷歌自动驾驶汽车已经成功地在道路上航行了数千英里。由智能软件代理控制的智能电表有助于优化家庭的能源消耗。在许多这样的应用中，自治智能体(即主体智能体)被期望通过预测公共环境中其他智能体的行为来做出理性决策。决策质量取决于建立其他代理的决策模型，然后求解模型以了解其他代理在环境中的行为。当主题代理的模型部署在真实世界的应用程序中时，它可能会失败，因为主题代理可能会收到其他代理引起的意外观察。因此，挑战在于预测其他主体的行为和解释模型失败，以便使主体主体的模型适应成功的交互。该项目的目标是通过自动构建其他主体的模型并在模型执行失败时修改自己的决策模型来提高主体主体的适应性。该项目将提出可扩展的学习算法，以基于代理交互的历史数据来构建其他代理的决策模型。这些算法还将有助于在一个新的问题域中构建模型，该问题域在实践中可能会更大和更不确定。为了解释主体主体决策模型的失败，本项目将寻找一种新的推理技术来识别失败背后最可能的原因，并相应地修改模型，使主体的决策能够适应其他主体在实时交互中的行为。该项目将在一个工具包中实施所有拟议的技术，并进行全面的测试，以评估该工具包的实用价值。我们将通过我们的行业合作伙伴，扩展个性化学习和智能电脑游戏AI引擎开发的现实世界应用。这项研究的更广泛影响将是使单个代理能够在复杂的多代理环境中理性地行动。这是在社会中整合自主代理技术的关键一步，该技术将在灾难应对、能源分配和安全操作等任务中为人类提供支持。

An agent is a computer system that acts intelligently given its sensory input from the environment. Agent technologies have proved to be effective and reliable solutions in many practical applications and will continue to play a major role in modern society. For example, the eBay buyer agent recommends good deals for people in an e-market. The Google self-driving car operated by an autonomous agent has successfully navigated thousands of miles on the road. A smart meter controlled by an intelligent software agent helps optimize energy consumption for a household. In many such applications, an autonomous agent (namely a subject agent) is expected to make a rational decision by predicting behaviors of other agents in a common environment. The decision quality relies on building decision models of the other agents and then solving the models to understand how the other agents will behave in the environment. When the subject agent's model is deployed in a real-world application, it may fail since the subject agent may receive unexpected observations incurred by other agents. Hence the challenge is about the prediction of other agents' behavior and the interpretation of model failure so as to adapt the subject agent's model for successful interactions. The goal of this project is to improve the subject agent's adaptation by automating the model construction of other agents and revising its own decision model when the model fails in the execution. This project will propose scalable learning algorithms to build decision models of other agents upon historical data of agents' interactions. The algorithms will also facilitate the model construction in a new problem domain that will be likely larger and more uncertain in practice. To interpret failures of the subject agent's decision model, this project will search for a novel reasoning technique to identify the most probable reasons behind the failures, and accordingly revise the model so that the subject agent's decisions can be adapted to the other agents' behaviors in their real-time interactions. This project will implement all the proposed techniques in a toolkit and conduct comprehensive tests to evaluate practical utilities of the toolkit. Real-world applications on personalized learning and intelligent computer game AI engine development will be extended through our industrial collaborators. The broader impact of this research will be to enable individual agents to act rationally in complex multiagent environments. This is a crucial step toward the integration of autonomous agent technology within society that will support humans in tasks such as disaster response, energy distribution and security operation.