Journal of Artificial Intelligence Research

Honey, I Shrunk the Hypothesis Space (Through Logical Preprocessing)

2026-01-29T00:07:47+00:00

Inductive logic programming (ILP) is a form of logical machine learning. The goal is to search a hypothesis space for a hypothesis that generalises training examples and background knowledge. We introduce an approach that shrinks the hypothesis space before an ILP system searches it. Our approach uses background knowledge to find rules that cannot be in an optimal hypothesis regardless of the training examples. For instance, our approach discovers relationships such as even numbers cannot be odd and prime numbers greater than 2 are odd. It then removes violating rules from the hypothesis space. We implement our approach using answer set programming and use it to shrink the hypothesis space of a constraint-based ILP system. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can substantially reduce learning times whilst maintaining predictive accuracies. For instance, given just 10 seconds of preprocessing time, our approach can reduce learning times from over 10 hours to only 2 seconds.

R-Mod: Minimal Structural Revision of S5 Epistemic Models

2026-01-15T05:11:35+00:00

Revising what an agent knows in response to new information is a central problem in formal epistemology. In doxastic logics such as KD45, belief revision proceeds by reordering plausibility: the agent simply re-ranks which worlds it considers most credible. This strategy fails for S5 knowledge. Because knowledge is factive (Kφ → φ), an agent cannot come to know phi merely by finding φ-worlds more plausible; if the actual world falsifies φ, then Kφ remains unsatisfiable regardless of any reordering. Accommodating new modal information in S5 therefore requires genuine model transformation: adjusting the equivalence-based accessibility structure, the valuation, or both.

We develop R-Mod, a selection-based revision operator that realizes this transformation as minimal structural repair. Given an S5 model and a target formula, R-Mod searches for a closest model, measured by a bisimulation-aware distance on quotient structures, that satisfies the formula while preserving S5 constraints. At the skeptical level, R-Mod satisfies success, consistency preservation, and deductive closure; classical AGM postulates such as Inclusion and Superexpansion fail due to permissible structural amplification, though we identify conditions under which they re-emerge. Computationally, the decision problem is NP-complete, and we provide tractable fragments exploiting structural locality.

While recent work has advanced AGM-style postulate analysis for S5 and topological semantics via simplicial complexes, these approaches do not provide goal-driven optimization with algorithmic guarantees. R-Mod fills this gap by combining modal invariance, explicit distance minimization, and fine-grained complexity analysis. Our results reframe revision in S5 as knowledge-model revision rather than belief revision, offering a foundation for algorithmic implementations and extensions to richer epistemic semantics.

Explaining Multivariate Decision Trees: Characterising Tractable Languages

2025-12-20T06:48:44+00:00

We study multivariate decision trees (MDTs), in particular, classes of MDTs determined by the language of relations that can be used to split feature space. An abductive explanation (AXp) of the classification of a particular instance, viewed as a set of feature-value assignments, is a minimal subset of the instance which is sufficient to lead to the same decision. We investigate when finding a single AXp is tractable. We identify tractable languages for real, integer and boolean features. Indeed, in the case of boolean languages, we provide a P/NP-hard dichotomy. We extend this dichotomy to languages defined by formulas whose literals correspond to splits of ordered domains of arbitrary finite size. Experiments indicate that MDTs can provide more compact models than classical decision trees while conserving accuracy and explainability.

TeamTTA: Efficient Multi-Device Collaboration for Open-Set Test-Time Adaptation via Cloud Integration

2026-01-11T13:39:16+00:00

Deep neural networks (DNNs) deployed on edge devices often suffer from severe performance degradation when exposed to dynamic and continually shifting environments. Test-time adaptation (TTA) has emerged as a promising solution by updating models online with incoming test data. However, edge deployment poses unique challenges: limited computational resources, latency caused by adaptation delays, and knowledge isolation across devices. The situation becomes even more complex in open-world scenarios, where the presence of unknown categories further disrupts adaptation. To overcome these limitations, we propose TeamTTA, a cloud-integrated framework designed for efficient multi-device collaboration open-set test-time adaptation. Specifically, TeamTTA aggregates reliable samples from multiple edge devices through crowdsourcing, uploads them to the cloud, and maintains a memory buffer for continual adaptation. A large vision model (LVM) in the cloud leverages its zero-shot generalization ability to filter out open-set samples and acts as a teacher model, distilling its knowledge into a replicated student edge model stored in the cloud. The adapted model parameters, or alternatively global statistics under poor network conditions, are then transmitted back to the edge devices for efficient inference. Extensive experiments on standard public TTA benchmarks, including corrupted and open-set datasets, show that TeamTTA achieves superior adaptation accuracy, robustness to distribution shifts, and communication efficiency, outperforming state-of-the-art TTA baselines. These results validate the effectiveness of integrating cloud-edge collaboration and LVM-driven knowledge distillation for real-world edge intelligence.

Differential Parity: Relative Fairness Between Two Sets of Decisions

2026-02-13T04:35:18+00:00

Background: With AI systems increasingly being applied to assist humans in decision-making processes such as talent hiring, school admissions, and loan approvals, there is a growing need to ensure that the resulting decisions are fair. A major challenge in analyzing fairness is that standards are highly subjective and context-dependent —- there is no consensus on what absolute fairness means in every scenario. Moreover, different standards of fairness often conflict with each other.

Objectives: To address this issue, this work aims to evaluate the relative fairness between decisions.

Methods: Instead of defining what constitutes “absolutely” fair decisions, we propose assessing the relative fairness of one decision set against another using differential parity —- two sets of decisions are considered relatively fair with respect to each other if and only if the difference between them is independent of a given sensitive attribute. The proposed notion of differential parity fairness offers three key benefits: (1) it avoids the ambiguity and contradictions inherent in defining “absolutely” fair decisions; (2) it reveals relative preferences and biases between two decision sets; and (3) it can serve as a new notion of group fairness when a reference set of decisions (e.g., ground truth) is available. One limitation of differential parity is that the two sets of decisions being compared must be made on the same data subjects. To overcome this limitation, we propose to utilize a machine learning model to bridge the gap between the two sets of decisions made on different data and approximate the differential parity metrics. In addition to differential parity and inspired by the statistical parity fairness notion, we also define relative statistical parity – the difference between the means of two sets of decisions is required to be independent of the sensitive attribute – as a weaker notion of relative fairness compared to differential parity.

Results: Theoretically, we show how the proposed metrics statistically evaluate differential parity and relative statistical parity. We also proved the feasibility of using the proposed biased bridge algorithm to approximate differential parity metrics between decisions made on different data. Empirically, we evaluated the Type I and Type II error rates of differential parity and relative statistical parity both between decisions made on the same data and on different data. Experimental results suggest that differential parity outperforms relative statistical parity by having a much lower Type II error rate in both scenarios.

Conclusions: With lower than 0.1 Type I and Type II error rates in both scenarios, the effectiveness of differential parity demonstrated in this article suggests that it is feasible and beneficial to evaluate relative bias between decisions made by different entities. We expect this to pave the way for the analysis of relative fairness in AI and beyond.

Scaling Neuro-symbolic Problem Solving: Solver-Free Learning of Constraints and Objectives

2025-11-20T01:04:07+00:00

Background: In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimisation problems from natural inputs, a task that Large Language Models seem to struggle with.

Objectives: We introduce a differentiable neuro-symbolic architecture and a loss function dedicated to learning how to solve NP-hard reasoning problems.

Methods: Our new probabilistic loss allows for learning both the constraints and the objective – possibly non-linear – of a combinatorial problem. Thus, it delivers a complete model that can be scrutinized and completed with side constraints. By pushing the combinatorial solver out of the training loop, our architecture also offers scalable training while exact inference gives access to maximum accuracy.

Results: We empirically show that it can efficiently learn how to solve NP-hard reasoning problems from natural inputs. On three variants of the Sudoku benchmark – symbolic, visual, and many-solution –, our approach requires a fraction of data and training time of other hybrid methods. On a visual Min-Cut/Max-cut task, it optimizes the regret as well as a Decision-Focused-Learning regret-dedicated loss. Finally, it efficiently learns the energy optimisation formulation of the large real-world problem of designing proteins.

A Review of Causal Decision Making

2025-11-16T08:16:38+00:00

To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes. This review aims to surface three crucial aspects of decision making through a causal lens: 1) the discovery of causal relationships through causal structure learning, 2) understanding the impacts of these relationships through causal effect learning, and 3) applying the knowledge gained from the first two aspects to support decision making via causal policy learning. Moreover, we identify challenges that hinder the broader utilization of causal decision making and discuss recent advances in overcoming these challenges. Finally, we provide future research directions to address these challenges and further enhance the implementation of causal decision making in practice, with real-world applications illustrated through the proposed causal decision-making workflow. To facilitate broader adoption, we additionally integrate relevant methods into a unified Python-based collection, offering a methodological and practical framework for the community (available at https://causaldm.github.io/Causal-Decision-Making).

Rational Silence and False Polarization: How Viewpoint Organizations and Recommender Systems Distort the Expression of Public Opinion

2025-11-06T15:26:24+00:00

Social media platforms are one of the most important domains in which artificial intelligence (AI) has already transformed the nature of economic and social interaction. AI enables the massive scale and highly personalized nature of online information sharing that we now take for granted. Extensive attention has been devoted to the polarization that social media platforms appear to facilitate. However, a key implication of the transformation we are experiencing due to these AI-powered platforms has received much less attention: how platforms impact what observers of online discourse come to believe about community views. These observers include policymakers and legislators, who look to social media to gauge the prospects for policy and legislative change, as well as developers of AI models trained on large-scale internet data, whose outputs may similarly reflect a distorted view of public opinion. In this paper, we present a nested game-theoretic model to show how observed online opinion is produced by the interaction of the decisions made by users about whether and with what rhetorical intensity to share their opinions on a platform, the efforts of viewpoint organizations (such as traditional media and advocacy organizations) that seek to encourage or discourage opinion-sharing online, and the operation of AI-powered recommender systems controlled by social media platforms. We show that signals from ideological viewpoint organizations encourage an increase in rhetorical intensity, leading to the rational silence of moderate users. This, in turn, creates a polarized impression of where average opinions lie. We also show that this observed polarization can also be amplified by recommender systems that, pursuant to a platform’s incentive to maximize engagement, encourage the formation of viewpoint communities online that end up seeing a skewed sample of opinion. Unlike existing models, these well-known online phenomena are not here attributed to distortion in the formation of opinions nor to the seeking out of like-minded others, but rather to the interaction of the incentives of users, viewpoint organizations, and platforms implementing recommender systems. In addition to showing how these interactions can play out in simulations, we also identify practical strategies platforms can implement, such as reducing exposure to signals from ideological viewpoint organizations and a tailored approach to content moderation.

General Supervised Learning Framework for Open World Classification

2025-11-10T15:48:42+00:00

In open-world supervised learning for classification, the training data is incomplete with respect to the full set of relevant classes in the application domain. Most existing research on this problem focuses on computer vision, and many of the proposed methodologies are intrinsically tied to specific machine learning algorithms or data types. However, real-world open-world settings may arise in a wide array of problem contexts, each with its own data type and classifier requirements. Although existing research emphasizes the identification of unknown sets or classes, it does not sufficiently address automatically categorizing these new classes and updating predictive models. In this work, we present a framework that addresses all aspects of the open world classification pipeline. The proposed approach is data- and model-agnostic, making it versatile across different domains. Our framework performs automatic identification and categorization of unknown instances into distinct new classes while dynamically updating predictive models without human intervention. We evaluate it on diverse data types, including images, text, and sensor data, demonstrating effectiveness across experiments with accuracy improvements ranging from 27 to 69 percentage points. To assess robustness and provide practical guidance, we conduct comprehensive sensitivity analysis examining the impact of key parameters including the number of known classes, the Chebyshev confidence parameter, the itemset size parameter, and base classifier quality. Additionally, we provide insights into practical applications through a case study on social media analytics for disaster response, highlighting the adaptability of the framework in real-world scenarios.

Label-Aware Pseudo-Training Sample Generation for Text Classification

2026-01-10T07:16:18+00:00

Deep learning models excel in various Natural Language Processing (NLP) tasks, but their performance (excluding approaches like zero-shot learning or few-shot learning) relies on ample data, posing challenges in fields with limited datasets. To address the poverty in the size of training data, a number of approaches could be taken, such as multi-task learning and data augmentation. Aiming to leverage Large Language Models (LLMs), we propose a data augmentation algorithm. It subtly alters sentences by inserting random words and utilizes LLMs to find the most fitting replacements within their embedding space. Taking inspiration from Prompt Tuning, the focus shifts from optimizing the input prompt to updating the inserted tokens’ embedding vectors by maximizing the conditional generation probability. This allows for vast sample generation while implicitly benefiting from the knowledge within LLMs. The results from our extensive set of experiments on various benchmark text classification tasks show a substantial improvement over the non-augmented outcomes.