Journal of Artificial Intelligence Research
The Journal of Artificial Intelligence Research (JAIR) is dedicated to the rapid dissemination of important research results to the global artificial intelligence (AI) community. The journal’s scope encompasses all areas of AI, including agents and multi-agent systems, automated reasoning, constraint processing and search, knowledge representation, machine learning, natural language, planning and scheduling, robotics and vision, and uncertainty in AI.
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Latest posts
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Robust Reward Machine Learning and Execution in Noisy Environments
Background : Reward machines (RMs) represent non-Markovian reward functions as finite-state machines over high-level propositional events. Traditionally, these events are generated by a deterministic labelling function that maps environment states to proposition sets. However,…
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Does an Intermediate Price Facilitate Algorithmic Collusion?
Extensive simulation studies of reinforcement learning algorithms in pricing environments suggest that it may be possible for them to learn to collude when including a memory of one period. Theoretical analyses of this phenomenon have focused on the setting with two prices,…
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Reasoning in Many-Valued Multi-Modal Logic: A Uniform and General Approach
Background : Multi-modal logic has found many applications in computer science, also providing a framework to model temporal and spatial information. Reasoning in this logic is crucial in many real-world applications from different fields. At the same time, these applications…
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Multi-Hypothesis Distillation of Multilingual Neural Translation Models for Low-Resource Languages
This paper explores knowledge distillation (KD) of multilingual pre-trained encoder-decoder and large language models for machine translation (MT). We argue that the teacher model's output distribution holds valuable insights for the student, beyond the approximated mode…
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Optimal Clustering with Dependent Costs in Bayesian Networks
Background : Clustering of nodes in Bayesian Networks (BNs) and related graphical models such as Dynamic BNs (DBNs) has been demonstrated to enhance computational efficiency and improve model learning. It typically involves partitioning the underlying Directed Acyclic Graph…
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Certified Neural Networks: from Verification to Synthesis
Neural networks find applications in many safety-critical systems that raise concerns about their deployment: Are we sure they never advise doing anything catastrophic? Formal verification has been recently applied to prove whether an existing neural network is certified for…
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How to Discover Short, Shorter, and the Shortest Proofs of Unsatisfiability: A Branch-and-Bound Approach for Resolution Proof Length Minimization
Background : Modern SAT solvers are indispensable reasoning tools that are widely applied in verification, planning, and combinatorial design. Beyond answering satisfiability, they also produce unsatisfiability proofs, which are valuable for certification and analysis. Since…
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Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges
Text generation has become more accessible than ever, and the growing interest in these systems, especially those using large language models, has spurred a surge in related publications. We provide a systematic literature review comprising 257 papers, covering the period from…
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Hierarchy Generation and Exploitation for Enhanced Multi-Class Classification
Background : Hierarchical Classification (HC) has long been recognized for improving predictive performance by exploiting relationships between classes. However, most tabular multi-class datasets lack predefined class hierarchies, limiting the broader applicability of…