RI: Small: Adaptive Metareasoning for Bounded Rational Agents

元推理是一个智能代理监控和控制自己的思维过程，以便及时产生有效行动的过程。 就像人们必须决定何时停止思考并采取行动一样，人工智能系统也需要能够中断他们的决策过程并致力于行动或计划。 虽然人们经常使用启发式方法来确定中断时间，但该项目提供了元推理技术，可以优化计算的价值，并在采取行动的紧迫性超过持续计算的预期收益时停止计划。 该项目通过提供易于使用的现成自适应元推理技术来控制它们，从而改变研究人员和从业人员创建响应式规划系统的能力。 其他影响更广泛的领域包括指导学生研究人员，特别关注代表性不足的群体，一系列针对当地学校的推广活动，有针对性的活动，以增加计算机科学的多样性，以及工业合作。该方法使用规划算法，可以随时中断，在运行时间和结果质量之间进行权衡。 为了利用这种权衡，新的元推理技术的开发，克服了现有方法的缺点。 其关键思想是通过创建新的方法来预测性能，并快速将预测适应手头的特定问题实例，从而取代对大量离线实验的依赖。 该项目回答了关于以最小的计算开销优化元级控制的可行性、效率和可扩展性的基本问题。 主要贡献如下：（1）在线性能预测方法，其用于优于现有技术方法的任意时间算法的有效元级控制;（2）使用强化学习技术在线创建和适配元级控制策略的新颖方法;（3）上述方法的扩展，以控制任意时间算法的组合，允许使用共享中间解表示从一个算法转换到另一个算法;以及（4）上述方法的扩展以控制可调任意时间算法的内部操作。 该团队使用一系列基于不同编程范式的随时算法对复杂计算任务的新元推理技术进行评估，并展示了相对于现有元推理技术的易用性和显着性能增益。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Metareasoning is the process by which an intelligent agent monitors and controls its own thought processes so as to produce effective action in a timely manner. Just as people must decide when to stop thinking and take action, AI systems also need to be able to interrupt their decision-making process and commit to an action or plan. While people often use heuristic methods to determine the interruption time, this project offers metareasoning techniques that optimize the value of computation and stop planning when the urgency to take action outweighs the anticipated benefit of continued computation. The project transforms the ability of researchers and practitioners to create responsive planning systems by offering easy-to-use, off-the-shelf adaptive metareasoning techniques to control them. Additional areas of broader impact include mentoring of student researchers with special attention to underrepresented groups, a range of outreach activities to local schools, targeted activities to increase diversity in computer science, and industrial collaborations.The approach uses planning algorithms that can be interrupted at any time, offering a tradeoff between runtime and quality of results. To take advantage of this tradeoff, novel metareasoning techniques are developed that overcome the drawbacks of existing methods. The key idea is to replace the reliance on extensive offline experiments by creating new ways to predict performance and adapt the prediction quickly to the specific problem instance at hand. The project answers fundamental questions about the feasibility, efficiency, and scalability of optimizing meta-level control with minimal computational overhead. The main contributions are: (1) online performance prediction methods for efficient meta-level control of anytime algorithms that outperform state-of-the-art methods; (2) a novel approach to create and adapt meta-level control policies online using reinforcement learning techniques; (3) extensions of the above methods to control a portfolio of anytime algorithms, allowing transitions from one algorithm to another using shared intermediate solution representations; and (4) extensions of the above methods to control the internal operation of adjustable anytime algorithms. The team evaluates the new metareasoning techniques on complex computational tasks using a range of anytime algorithms based on different programming paradigms and demonstrates ease of use and significant performance gains relative to existing metareasoning techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.