Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments (2505.19361v1)

Abstract: The deployment of pre-trained perception models in novel environments often leads to performance degradation due to distributional shifts. Although recent artificial intelligence approaches for metacognition use logical rules to characterize and filter model errors, improving precision often comes at the cost of reduced recall. This paper addresses the hypothesis that leveraging multiple pre-trained models can mitigate this recall reduction. We formulate the challenge of identifying and managing conflicting predictions from various models as a consistency-based abduction problem. The input predictions and the learned error detection rules derived from each model are encoded in a logic program. We then seek an abductive explanation--a subset of model predictions--that maximizes prediction coverage while ensuring the rate of logical inconsistencies (derived from domain constraints) remains below a specified threshold. We propose two algorithms for this knowledge representation task: an exact method based on Integer Programming (IP) and an efficient Heuristic Search (HS). Through extensive experiments on a simulated aerial imagery dataset featuring controlled, complex distributional shifts, we demonstrate that our abduction-based framework outperforms individual models and standard ensemble baselines, achieving, for instance, average relative improvements of approximately 13.6% in F1-score and 16.6% in accuracy across 15 diverse test datasets when compared to the best individual model. Our results validate the use of consistency-based abduction as an effective mechanism to robustly integrate knowledge from multiple imperfect reasoners in challenging, novel scenarios.

Consistency-based Abductive Reasoning Over Perceptual Errors of Multiple Pre-trained Models in Novel Environments

The deployment of pre-trained perception models in environments that differ from their training data often results in performance degradation due to distributional shifts. The paper "Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments," addresses this problem by formulating a new approach that leverages multiple pre-trained models to mitigate recall reduction associated with error characterization and filtering using metacognitive AI.

Methodological Approach

The paper presents an abductive reasoning framework encoded in a logic program to manage and improve model predictions from multiple pre-trained models. The problem of conflicting predictions is framed as a consistency-based abduction issue, where the aim is to achieve maximal prediction coverage while keeping logical inconsistencies below a predefined threshold. The researchers propose two algorithms to tackle this knowledge representation task: an exact method based on Integer Programming (IP) and an efficient Heuristic Search (HS).

The IP formulation is structured to maximize valid predictions while considering constraints of logical consistency and model-class pair selection, intrinsically avoiding double assignments that do not comply with domain integrity. The heuristic approach, on the other hand, provides a scalable method that iteratively refines the prediction set by evaluating the trade-off between minimizing inconsistencies and maximizing coverage.

Experimental Evaluation

The paper validates its approach using an extensive set of experiments conducted on a simulated dataset, derived from aerial imagery with controlled and complex distributional shifts. Testing involved varying weather conditions to simulate novel environments that might be encountered in real-world applications like disaster response or NGO operations in remote areas. The proposed framework was shown to outperform individual models and standard ensemble baselines significantly. Results highlighted average relative improvements of approximately 13.6% in F1-score and 16.6% in accuracy across diverse test datasets compared to the best individual model.

Implications and Future Directions

The findings implicate the effectiveness of consistency-based abduction as a robust mechanism for integrating predictions from multiple imperfect reasoners. Theoretically, the work contributes to the field of metacognitive AI by offering a novel perspective on addressing perceptual errors resulting from distributional shifts. Practically, the approach provides a viable solution for deploying AI systems in novel environments without requiring extensive domain-specific training data. This has potential applications in fields such as autonomous systems operating in dynamically changing environments, and disaster response where unforeseen scenarios are frequent.

Future development in this field may explore further sophistication in logic program encoding for the deductive process, the introduction of advanced metacognitive models for even greater prediction accuracy and consistency, and exploration of how this framework can synergize with other AI paradigms such as test-time training to further enhance model adaptability and robustness.

This research exemplifies a valuable contribution to AI's growing repertoire, expanding capabilities to gracefully manage unpredictability and change in real-world applications.