Congratulations to the #IJCAI2025 distinguished paper award winners

The International Joint Conference on Artificial Intelligence (IJCAI) distinguished paper awards recognise some of the best papers presented at the conference each year. This year, during the conference opening ceremony, three articles were named as distinguished papers.

And the winners are…

Combining MORL with Restraining Bolts to Learn Normative Behaviour
Emery A. Neufeld, Agata Ciabattoni and Radu Florin Tulcan

Abstract: Normative Restraining Bolts (NRBs) adapt the restraining bolt technique (originally developed for safe reinforcement learning) to ensure compliance with social, legal, and ethical norms. While effective, NRBs rely on trial-and-error weight tuning, which hinders their ability to enforce hierarchical norms; moreover, norm updates require retraining. In this paper, we reformulate learning with NRBs as a multi-objective reinforcement learning (MORL) problem, where each norm is treated as a distinct objective. This enables the introduction of Ordered Normative Restraining Bolts (ONRBs), which support algorithmic weight selection, prioritized norms, norm updates, and provide formal guarantees on minimizing norm violations. Case studies show that ONRBs offer a robust and principled foundation for RL-agents to comply with a wide range of norms while achieving their goals.

Read the paper in full here.

Boost Embodied AI Models with Robust Compression Boundary
Chong Yu, Tao Chen, Zhongxue Gan

Abstract: The rapid improvement of deep learning models with the integration of the physical world has dramatically improved embodied AI capabilities. Meanwhile, the powerful embodied AI models and their scales place an increasing burden on deployment efficiency. The efficiency issue is more apparent on embodied AI platforms than on data centers because they have more limited computational resources and memory bandwidth. Meanwhile, most embodied AI scenarios, like autonomous driving and robotics, are more sensitive to fast responses. Theoretically, the traditional model compression techniques can help embodied AI models with more efficient computation, lower memory and energy consumption, and reduced latency. Because the embodied AI models are expected to interact with the physical world, the corresponding compressed models are also expected to resist natural corruption caused by real-world events such as noise, blur, weather conditions, and even adversarial corruption. This paper explores the novel paradigm to boost the efficiency of the embodied AI models and the robust compression boundary. The efficacy of our method has been proven to find the optimal balance between accuracy, efficiency, and robustness in real-world conditions.

Read the paper in full here.

Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
Abderrahim Bendahi, Benjamin Doerr, Adrien Fradin and Johannes F. Lutzeyer

Abstract: The move-acceptance hyper-heuristic was recently shown to be able to leave local optima with astonishing efficiency (Lissovoi et al., Artificial Intelligence (2023)). In this work, we propose two modifications to this algorithm that demonstrate impressive performances on a large class of benchmarks including the classic CLIFF_d and JUMP_m function classes. (i) Instead of randomly choosing between the only-improving and any-move acceptance operator, we take this choice via a simple two-state Markov chain. This modification alone reduces the runtime on JUMP_m functions with gap parameter m from Ω(n^2m−1) to O(n^m+1). (ii) We then replace the all-moves acceptance operator with the operator that only accepts worsenings. Such a, counter-intuitive, operator has not been used before in the literature. However, our proofs show that our only-worsening operator can greatly help in leaving local optima, reducing, e.g., the runtime on JUMP functions to O(n³logn) independent of the gap size. In general, we prove a remarkably good runtime of O(n^k+1logn) for our Markov move-acceptance hyper-heuristic on all members of a new benchmark class SEQOPT_k, which contains a large number of functions having k successive local optima, and which contains the commonly studied JUMP_m and CLIFF_d functions for k=2.

Read the paper in full here.

Lucy Smith is Senior Managing Editor for AIhub.

Lucy Smith is Senior Managing Editor for AIhub.