Frontiers of Computer Science

, Volume 12, Issue 5, pp 939–949 | Cite as

Achieving data-driven actionability by combining learning and planning

  • Qiang Lv
  • Yixin Chen
  • Zhaorong Li
  • Zhicheng Cui
  • Ling Chen
  • Xing Zhang
  • Haihua Shen
Research Article


A main focus of machine learning research has been improving the generalization accuracy and efficiency of prediction models. However, what emerges as missing in many applications is actionability, i.e., the ability to turn prediction results into actions. Existing effort in deriving such actionable knowledge is few and limited to simple action models while in many real applications those models are often more complex and harder to extract an optimal solution.

In this paper, we propose a novel approach that achieves actionability by combining learning with planning, two core areas of AI. In particular, we propose a framework to extract actionable knowledge from random forest, one of the most widely used and best off-the-shelf classifiers. We formulate the actionability problem to a sub-optimal action planning (SOAP) problem, which is to find a plan to alter certain features of a given input so that the random forest would yield a desirable output, while minimizing the total costs of actions. Technically, the SOAP problem is formulated in the SAS+ planning formalism, and solved using a Max-SAT based approach. Our experimental results demonstrate the effectiveness and efficiency of the proposed approach on a personal credit dataset and other benchmarks. Our work represents a new application of automated planning on an emerging and challenging machine learning paradigm.


actionable knowledge extraction machine learning planning random forest 


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This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61502412, 61379066, and 61402395), Natural Science Foundation of the Jiangsu Province (BK20150459, BK20151314, and BK20140492), Natural Science Foundation of the Jiangsu Higher Education Institutions (15KJB520036), United States NSF grants (IIS-0534699, IIS-0713109, CNS-1017701), Microsoft Research New Faculty Fellowship, and the Research Innovation Program for Graduate Student in Jiangsu Province (KYLX16_1390).

Supplementary material

11704_2017_6315_MOESM1_ESM.ppt (322 kb)
Supplementary material, approximately 228 KB.


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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information EngineeringYangzhou UniversityYangzhouChina
  2. 2.Department of Computer Science and EngineeringWashington University in St. LouisSt. LouisUSA
  3. 3.School of ManagementFudan UniversityShanghaiChina
  4. 4.School of Computer and Control EngineeringUniversity of Chinese Academy of ScienceBeijingChina

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