Dynamic programming in in uence diagrams with decision circuits

TL;DR

This paper introduces a method for constructing compact decision circuits that enable efficient dynamic programming in influence diagrams, leveraging their structure for faster evaluation and analysis.

Contribution

It presents a novel approach to build more compact decision circuits for influence diagrams with separable value functions, improving computational efficiency.

Findings

Decision circuits can be optimized for influence diagrams with specific structures.

The approach allows for efficient evaluation and sensitivity analysis.

Compact circuits lead to faster decision-making processes.

Abstract

Decision circuits perform efficient evaluation of influence diagrams, building on the ad- vances in arithmetic circuits for belief net- work inference [Darwiche, 2003; Bhattachar- jya and Shachter, 2007]. We show how even more compact decision circuits can be con- structed for dynamic programming in influ- ence diagrams with separable value functions and conditionally independent subproblems. Once a decision circuit has been constructed based on the diagram's "global" graphical structure, it can be compiled to exploit "lo- cal" structure for efficient evaluation and sen- sitivity analysis.