Compared totheexisting sampling-based approaches, which are inapplicable to the infinite time horizon setting, wetrain aseparate deterministic neural networkthatservesasaninfinite timehorizon safety certificate.

Message passing neural network (MPNN) has recently emerged as a successful framework by achieving state-of-the-art performances on many graph-based learning tasks [25].

Theitemsbecomeavailable over a sequence of rounds, and every item must be allocated immediately and irrevocably before the next one arrives.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we dive into the world of agents, learn about responsible multimodal AI, apply generative AI to computer networks, and dig into the RoboCup@Work League. This month, Sanmay Das, Tom Dietterich, Sabine Hauert, Sarit Kraus, and Michael Littman tackled the topic of agentic AI, discussing recent developments, and lessons learned from the decades of research in the autonomous agents and multiagent systems community. The 34th International Joint Conference on Artificial Intelligence (IJCAI2025) took place in Montréal from 16-22 August, with a satellite event currently being held (from 29-31 August) in Guangzhou, China. You can find out more about the programmes of both venues here, and get a flavour of what attendees got up to in our social media round-ups: Part one Part two.

We introduce Supported Abstract Argumentation for Case-Based Reasoning (sAA-CBR), a binary classification model in which past cases engage in debates by arguing in favour of their labelling and attacking or supporting those with opposing or agreeing labels. With supports, sAA-CBR overcomes the limitation of its precursor AA-CBR, which can contain extraneous cases (or spikes) that are not included in the debates. We prove that sAA-CBR contains no spikes, without trading off key model properties

Automated planning is a form of declarative problem solving which has recently drawn attention from the machine learning (ML) community. ML has been applied to planning either as a way to test `reasoning capabilities' of architectures, or more pragmatically in an attempt to scale up solvers with learned domain knowledge. In practice, planning problems are easy to solve but hard to optimise. However, ML approaches still struggle to solve many problems that are often easy for both humans and classical planners. In this paper, we thus propose a new ML approach that allows users to specify background knowledge (BK) through Datalog rules to guide both the learning and planning processes in an integrated fashion. By incorporating BK, our approach bypasses the need to relearn how to solve problems from scratch and instead focuses the learning on plan quality optimisation. Experiments with BK demonstrate that our method successfully scales and learns to plan efficiently with high quality solutions from small training data generated in under 5 seconds.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we find out about Neural Operators, take a virtual trip to IJCAI, and try to bridge the gap between user expectations and AI capabilities. Anima Anandkumar is the inventor of Neural Operators which extend deep learning to modelling multi-scale processes in many scientific domains, including weather and climate modelling, drug discovery, and engineering design problems. In the next in our series of interviews with the 2024 AAAI Fellows, Anima tells us about Neural Operators and how she has applied them to many important science and engineering problems. Florian Tramer, Gautam Kamath and Nicholas Carlini won an International Conference on Machine Learning (ICML 2024) best paper award for their work Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining, in which they challenge the paradigm of pretraining models with public data, and then privately fine-tuning the weights with sensitive data.

This month, our trustees tackle the topic of conference reviewing. Joining the conversation are: Sanmay Das (George Mason University), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University) and Carles Sierra (CSIC). Lucy Smith: Our topic this month is the conference reviewing and publication process. It would be good to discuss some of the issues and then consider some possible improvements. Sarit Kraus: Well, where do we start…?! Carles Sierra: I mean, there are so many issues.

EXplainable AI has received significant attention in recent years. Machine learning models often operate as black boxes, lacking explainability and transparency while supporting decision-making processes. Local post-hoc explainability queries attempt to answer why individual inputs are classified in a certain way by a given model. While there has been important work on counterfactual explanations, less attention has been devoted to semifactual ones. In this paper, we focus on local post-hoc explainability queries within the semifactual `even-if' thinking and their computational complexity among different classes of models, and show that both linear and tree-based models are strictly more interpretable than neural networks. After this, we introduce a preference-based framework that enables users to personalize explanations based on their preferences, both in the case of semifactuals and counterfactuals, enhancing interpretability and user-centricity. Finally, we explore the complexity of several interpretability problems in the proposed preference-based framework and provide algorithms for polynomial cases.

Artificial Intelligence is now recognized as a general-purpose technology with ample impact on human life. This work aims at understanding the evolution of AI and, in particular Machine learning, from the perspective of researchers' contributions to the field. In order to do so, we present several measures allowing the analyses of AI and machine learning researchers' impact, influence, and leadership over the last decades. This work also contributes, to a certain extent, to shed new light on the history and evolution of AI by exploring the dynamics involved in the field's evolution by looking at papers published at the flagship AI and machine learning conferences since the first International Joint Conference on Artificial Intelligence (IJCAI) held in 1969. AI development and evolution have led to increasing research output, reflected in the number of articles published over the last sixty years. We construct comprehensive citation collaboration and paper-author datasets and compute corresponding centrality measures to carry out our analyses. These analyses allow a better understanding of how AI has reached its current state of affairs in research. Throughout the process, we correlate these datasets with the work of the ACM Turing Award winners and the so-called two AI winters the field has gone through. We also look at self-citation trends and new authors' behaviors. Finally, we present a novel way to infer the country of affiliation of a paper from its organization. Therefore, this work provides a deep analysis of Artificial Intelligence history from information gathered and analysed from large technical venues datasets and suggests novel insights that can contribute to understanding and measuring AI's evolution.