Summary of Oil-ad: An Anomaly Detection Framework For Sequential Decision Sequences, by Chen Wang et al.
OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences
by Chen Wang, Sarah Erfani, Tansu Alpcan, Christopher Leckie
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an unsupervised method called Offline Imitation Learning based Anomaly Detection (OIL-AD) for detecting anomalies in decision-making sequences. The approach uses two extracted behavioral features: action optimality and sequential association, which are derived from a Q function and state value function learned through offline imitation learning. The proposed method can achieve outstanding online anomaly detection performance with up to 34.8% improvement in F1 score over comparable baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection is important for decision-making sequences because it helps identify unusual behavior. Most existing methods based on Reinforcement Learning (RL) are not suitable for real-world applications due to unrealistic assumptions. The proposed method, OIL-AD, uses offline imitation learning and two features: action optimality and sequential association. These features can help detect anomalies in decision-making sequences. The paper shows that OIL-AD performs well compared to other methods. |
Keywords
* Artificial intelligence * Anomaly detection * F1 score * Reinforcement learning * Unsupervised
