Conference proceeding
xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis
KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, pp.1334-1343
01/01/2020
DOI: 10.1145/3394486.3403186
Abstract
To make daily decisions, human agents devise their own "strategies" governing their mobility dynamics (e.g., taxi drivers have preferred working regions and times, and urban commuters have preferred routes and transit modes). Recent research such as generative adversarial imitation learning (GAIL) demonstrates successes in learning human decision-making strategies from their behavior data using deep neural networks (DNNs), which can accurately mimic how humans behave in various scenarios, e.g., playing video games, etc. However, such DNN-based models are "black box" models in nature, making it hard to explain what knowledge the models have learned from human, and how the models make such decisions, which was not addressed in the literature of imitation learning. This paper addresses this research gap by proposing xGAIL, the first explainable generative adversarial imitation learning framework. The proposed xGAIL framework consists of two novel components, including Spatial Activation Maximization (SpatialAM) and Spatial Randomized Input Sampling Explanation (SpatialRISE), to extract both global and local knowledge from a well-trained GAIL model that explains how a human agent makes decisions. Especially, we take taxi drivers' passenger-seeking strategy as an example to validate the effectiveness of the proposed xGAIL framework. Our analysis on a large-scale real-world taxi trajectory data shows promising results from two aspects: i) global explainable knowledge of what nearby traffic condition impels a taxi driver to choose a particular direction to find the next passenger, and ii) local explainable knowledge of what key (sometimes hidden) factors a taxi driver considers when making a particular decision.
Details
- Title: Subtitle
- xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis
- Creators
- Menghai Pan - Worcester Polytechnic InstituteWeixiao Huang - Worcester Polytechnic InstituteYanhua Li - Worcester Polytechnic InstituteXun Zhou - University of IowaJun Luo - Lenovo
- Resource Type
- Conference proceeding
- Publication Details
- KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, pp.1334-1343
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/3394486.3403186
- Number of pages
- 10
- Grant note
- SAFER-SIM University Transportation Center IIS-1942680; CNS-1657350; CMMI-1831140 / NSF; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2020
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380478602771
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