Enabling Optimal Decisions in Rehearsal Learning under CARE Condition

Wen-Bo Du, Hao-Yi Lei, Lue Tao, Tian-Zuo Wang, Zhi-Hua Zhou

TL;DR: We present a novel approach to identify optimal decision actions in rehearsal learning.

Abstract: In the field of machine learning (ML), an essential type of decision-related problem is known as AUF (Avoiding Undesired Future): if an ML model predicts an undesired outcome, how can decisions be made to prevent it? Recently, a novel framework called *rehearsal learning* has been proposed to address the AUF problem. Despite its utility in modeling uncertainty for decision-making, it remains unclear *under what conditions* and *how* optimal actions that maximize the *AUF probability* can be identified. In this paper, we propose *CARE* (CAnonical REctangle), a condition under which the maximum AUF probability can be achieved. Under the CARE condition, we present a projection-Newton algorithm to select actions and prove that the algorithm achieves superlinear convergence to the optimal one. Besides, we provide a generalization method for adopting the algorithm to AUF scenarios beyond the CARE condition. Finally, we demonstrate that a closed-form solution exists when the outcome is a singleton variable, substantially reducing the time complexity of decision-making. Experiments validate the effectiveness and efficiency of our method.

Lay Summary:

Machine learning models sometimes predict undesirable future outcomes, like a drone risking package loss. The challenge is deciding how to act—adjusting the drone's flight, for instance—to prevent this, especially when real-world tests are costly or risky and the exact impact of actions is unclear.

Our work offers a novel method for better decision-making. We define a *CARE (CAnonical REctangle)* condition allowing precise calculation of actions that maximize the chance of good outcomes. We developed an efficient algorithm for this and a way to adapt it if *CARE* isn't fully met. For single-outcome scenarios, we provide a direct formula for the best action.

Our research offers a more reliable way to act on undesirable predictions. By directly maximizing the probability of a positive result, our method enables more effective actions than prior approaches. Its efficiency, particularly the direct formula for simpler cases, makes it practical for real-world systems to better avoid negative outcomes and achieve desired results.

Primary Area: Probabilistic Methods->Graphical Models

Keywords: decision-making, probabilistic optimization, structural model

Submission Number: 6315