基于混合线性规划的资源受限Agent信息论抽象
Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming
摘要：
在本文中，一个混合整数线性规划制定的问题，获得任务相关的，多分辨率，图形抽象的资源受限的代理。该公式利用信息理论信号压缩的概念，特别是信息瓶颈（IB）方法，提出了一个图形抽象问题作为多分辨率树空间上的最佳编码器搜索。抽象出现在一个任务相关的方式作为代理信息处理约束的函数，并不提供给系统的先验。我们详细介绍了我们的配方，并显示如何可以实现的问题作为一个整数线性规划。一个非平凡的数值例子来证明我们的方法来获得层次树抽象的资源有限的代理的效用。
英文摘要：
In this paper, a mixed-integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, graph abstractions for resource-constrained agents is presented. The formulation leverages concepts from information-theoretic signal compression, specifically the information bottleneck (IB) method, to pose a graph abstraction problem as an optimal encoder search over the space of multi-resolution trees. The abstractions emerge in a task-relevant manner as a function of agent information-processing constraints, and are not provided to the system a priori. We detail our formulation and show how the problem can be realized as an integer linear program. A non-trivial numerical example is presented to demonstrate the utility in employing our approach to obtain hierarchical tree abstractions for resource-limited agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
