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This paper surveys Machine Learning approaches to build predictive models that know what they don&#39;t know. The consequential action of this knowledge can consist of abstaining from providing an output (rejection), deferring to another model (dynamic model selection), deferring to a human expert (learning to defer), or informing the user (uncertainty estimation). We formally state the problems each approach solves and point to key references. We discuss open issues that deserve investigation from the scientific community.

AAAI 2025

Things Machine Learning Models Know That They Don’t Know

### Welcome to CogSci Conference 2025!

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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

This paper surveys Machine Learning approaches to build predictive models that know what they don't know. The consequential action of this knowledge can consist of abstaining from providing an output (rejection), deferring to another model (dynamic model selection), deferring to a human expert (learning to defer), or informing the user (uncertainty estimation). We formally state the problems each approach solves and point to key references. We discuss open issues that deserve investigation from the scientific community.

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)<br><br><br>

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)<br><br> 

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



A *citizens' assembly* is a group of people who are randomly selected to represent a larger population in a deliberation. While this approach has successfully strengthened democracy, it has certain limitations that suggest the need for assemblies to form and associate more organically. In response, we propose *federated assemblies*, where assemblies are interconnected, and each parent assembly is selected from members of its child assemblies. The main technical challenge is to develop random selection algorithms that meet new representation constraints inherent in this hierarchical structure. We design and analyze several algorithms that provide different representation guarantees under various assumptions on the structure of the underlying graph.

Federated Assemblies

Multi-Agent Path Finding (MAPF) is a critical component of logistics and warehouse management, which focuses on planning collision-free paths for a team of robots in a known environment. Recent work introduced a novel MAPF approach, LNS2, which proposed to repair a quickly-obtainable set of infeasible paths via iterative re-planning, by relying on a fast, yet lower-quality, priority-based planner. At the same time, there has been a recent push for Multi-Agent Reinforcement Learning (MARL) based MAPF algorithms, which let agents learn decentralized policies that exhibit improved cooperation over such priority planning, although inevitably remaining slower. In this paper, we introduce a new MAPF algorithm, LNS2+RL, which combines the distinct yet complementary characteristics of LNS2 and MARL to effectively balance their individual limitations and get the best from both worlds. During early iterations, LNS2+RL relies on MARL for low-level re-planning, which we show eliminates collisions much more than a priority-based planner. There, our MARL-based planner allows agents to reason about past and future/predicted information to gradually learn cooperative decision-making through a finely designed curriculum learning. At later stages of planning, LNS2+RL adaptively switches to priority-based planning to quickly resolve the remaining collisions, naturally trading-off solution quality and computational efficiency. Our comprehensive experiments on challenging tasks across various team sizes, world sizes, and map structures consistently demonstrate the superior performance of LNS2+RL compared to many MAPF algorithms, including LNS2, LaCAM, and EECBS. In maps with complex structures, the advantages of LNS2+RL are particularly pronounced, with LNS2+RL achieving a success rate of over 50\% in nearly half of the tested tasks, while that of LaCAM and EECBS falls to 0%.We finally experimentally validate our algorithm in a hybrid simulation of a warehouse mockup involving a team of 100 (real-world and simulated) robots.

LNS2+RL: Combining Multi-agent Reinforcement Learning with Large Neighborhood Search in Multi-agent Path Finding

We study the problem of realizing strategies for an LTLf goal specification while ensuring that at least an LTLf backup specification is satisfied in case of unreliability of certain input variables.
We formally define the problem and characterize its worst-case complexity as 2EXPTIME-complete, like standard LTLf synthesis.
Then we devise three different solution techniques:  one based on direct automata manipulation, which is 2EXPTIME, one disregarding unreliable input variables by adopting a belief construction, which is 3EXPTIME, and one leveraging second-order quantified LTLf (QLTLf), which is 2EXPTIME, and allows for a direct encoding into monadic second-order logic, which in turn is worst-case nonelementary. 
We prove their correctness and evaluate them against each other empirically.  
Interestingly, theoretical worst-case bounds do not translate into observed performance; the MSO technique performs best, followed by belief construction and direct automata manipulation. As a byproduct of our study, we provide a general synthesis procedure for arbitrary QLTLf specifications.

LTLf Synthesis Under Unreliable Input

Many approaches to program synthesis perform a combinatorial search within a large space of programs to find one that satisfies a given specification. To tame the search space blowup, previous works introduced probabilistic and neural approaches to guide this combinatorial search by inducing heuristic cost functions. Best-first search algorithms ensure to search in the exact order induced by the cost function, significantly reducing the portion of the program space to be explored. We present a new best-first search algorithm called Eco Search, which is the first no-delay algorithm: the amount of compute required between outputting two programs is constant, and in particular does not increase over time. This key property yields important speedups: we observe that Eco Search outperforms its predecessors on two classical domains.

Eco Search: A No-delay Best-First Search Algorithm for Program Synthesis

Camera-based 3D semantic scene completion (SSC) provides dense geometric and semantic perception for autonomous driving. However, images provide limited information making the model susceptible to geometric ambiguity caused by occlusion and perspective distortion. Existing methods often lack explicit semantic modeling between objects, limiting their perception of 3D semantic context. To address these challenges, we propose a novel method VLScene: Vision-Language Guidance Distillation for Camera-based 3D Semantic Scene Completion. The key insight is to use the vision-language model to introduce high-level semantic priors to provide the object spatial context required for 3D scene understanding. Specifically, we design a vision-language guidance distillation process to enhance image features, which can effectively capture semantic knowledge from the surrounding environment and improve spatial context reasoning. In addition, we introduce a geometric-semantic sparse awareness mechanism to propagate geometric structures in the neighborhood and enhance semantic information through contextual sparse interactions. Experimental results demonstrate that VLScene achieves rank-1st performance on challenging benchmarks—SemanticKITTI and SSCBench-KITTI-360, yielding remarkably mIoU scores of 17.52 and 19.10, respectively. The source code will be released upon acceptance.

VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene Completion

In recent years, deep learning has revenue in the field of automated medical landmark detection. Nonetheless, prevailing research on this field predominantly addresses either single-center scenarios or domain adaptation settings. In practical environments, the acquisition of multi-center data faces privacy concerns, coupled with the time-intensive and costly nature of data collection and annotation. These challenges substantially impede the broader application of deep learning-based medical landmark detection. To mitigate these issues, we propose a novel framework for domain-generalized medical landmark detection that relies solely on single-center data for training. Considering the availability of numerous public medical segmentation datasets, we design a simple yet effective method that utilizes single-center segmentation to enhance the domain generalization capabilities of the landmark detection task. Specifically, we introduce a novel boundary-aware pre-training approach designed to focus the model on regions pertinent to landmarks. To further enhance the robustness and generalization capabilities during pre-training, we have derived a mixing loss function and proof its effectiveness in theory and practice. Extensive experiments conducted on our new domain generalization benchmark for medical landmark detection demonstrate the superiority of our approach. All benchmark data and accompanying code from this study will be made publicly available upon acceptance, thereby contributing to the community.

Domain Generalized Medical Landmark Detection via Robust Boundary-Aware Pre-Training

Abstract visual reasoning (AVR) is a critical ability of humans, and it has been widely studied, but arithmetic visual reasoning, a unique task in AVR to reason over number sense, is less studied in the literature. To facilitate this research, we construct a Machine Number Reasoning (MNR) dataset to assess the model's ability in arithmetic visual reasoning over number sense and spatial layouts. To solve the MNR tasks, we propose a Dual-branch Arithmetic Regression Reasoning (DARR) framework, which includes an Intra-Image Arithmetic Regression Reasoning (IIARR) module and a Cross-Image Arithmetic Regression Reasoning (CIARR) module. The IIARR includes a set of Intra-Image Regression Blocks to identify the correct number orders and the underlying arithmetic rules within individual images, and an Order Gate to determine the correct number order. The CIARR establishes the arithmetic relations across different images through a '3-to-1' regressor and a set of '2-to-1' regressors, with a Selection Gate to select the most suitable ‘2-to-1' regressor and a gated fusion to combine the two kinds of regressors. Experiments on the MNR dataset show that the DARR outperforms state-of-the-art models for arithmetic visual reasoning.

DARR: A Dual-Branch Arithmetic Regression Reasoning Framework for Solving Machine Number Reasoning

The future of Artificial Intelligence demands a paradigm shift towards multisensory perception—to systems that can digest ongoing multisensory observations, that can discover structure in unlabeled raw sensory data, and that can intelligently fuse useful information from different sensory modalities for decision making. While we humans perceive the world by looking, listening, touching, smelling, and tasting, traditional form of machine intelligence mostly focuses on a single sensory modality, particularly vision. Therefore, my research, which I call multisensory machine intelligence, aims to empower machines to emulate and enhance human capabilities in seeing, hearing, and feeling, ultimately enabling them to comprehensively perceive, understand, and interact with the multisensory world. In my AAAI-25 new faculty highlight talk, I will present my research that studies two important aspects of the multisensory world: 1) multisensory objects, and 2) multisensory space. In both aspects, I will talk about how we design systems to reliably capture multisensory data from real-world objects and space, how we effectively model them with differentiable simulation algorithms that build a unified multisensory representation to virtualize real objects, and how we explore creative cross-modal/multi-modal applications with sight, sound, and touch in vision, graphics, and robotics. In the end, I will briefly conclude with my future plans.

Multisensory Machine Intelligence

In many human-robot collaboration and multi-agent tasks, it is vital to model the partners and estimate their objectives to efficiently collaborate/interact with them. While learning from demonstrations is the most common approach for this, it is very data-hungry, which we cannot afford in many settings including robotics, and demonstrations are unreliable in a surprisingly large number of domains, including those we think humans perform reasonably well, e.g., driving. In this talk, I will start with introducing comparison-based feedback and explain why it does not suffer from most of the problems that demonstrations have, but is still data-hungry. To address this problem, I will propose comparative language based feedback and active learning techniques, which will result in (1) a new type of human feedback, and (2) an active querying algorithm that optimizes the information the AI agent will elicit from the human. I will conclude the talk by discussing what other types of human feedback exist, e.g., interventions or hand gestures, and how we can incorporate them into the existing learning algorithms.

Efficient Robot Learning via Interaction with Humans

Artificial Intelligence (AI) has revolutionized fields like computer vision and natural language processing, yet its impact on robotics remains limited by challenges in long-horizon decision-making and complex physical interactions. My research pioneers robot learning algorithms that exploit (predict, perceive, plan, and reason about) physical interaction as a core component of artificial intelligence, pushing beyond passive solutions in domains such as perception, navigation, and manipulation. By leveraging techniques in imitation learning and hierarchical reinforcement learning, my work empowers robots to learn from human demonstrations, navigate interactively in real-world environments, and gather information through purposeful interactions. In my talk, I will explain how these advances are critical for robots to become useful helpers in human environments, opening the door to the next generation of household robots. I will present several AI algorithmic innovations to integrate physical interactions in computation procedures and outline the path toward developing continually learning robots capable of operating autonomously in unstructured human environments, enhancing their utility as adaptable and intelligent assistants.

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