Software defect prediction (SDP) aims to identify high-risk defect modules in software development, optimizing resource allocation. While previous studies show that dependency network metrics improve defect prediction, most methods focus on code-based dependency graphs, overlooking developer factors. Current metrics, based on handcrafted features like ego and global network metrics, fail to fully capture defect-related information. To address this, we propose DeMuVGN, a defect prediction model that learns multi-view software dependency via graph neural networks. We introduce a Multi-view Software Dependency Graph (MSDG) that integrates data, call, and developer dependencies. DeMuVGN also leverages the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and enhance defect module identification. In a case study of eight open-source projects across 20 versions, DeMuVGN demonstrates significant improvements: i) models based on multi-view graphs improve F1 scores by 11.1% to 12.1% over single-view models; ii) DeMuVGN improves F1 scores by 17.4% to 45.8% in within-project contexts and by 17.9% to 41.0% in cross-project contexts. Additionally, DeMuVGN excels in software evolution, showing more improvement in later-stage software versions. Its strong performance across different projects highlights its generalizability. We recommend future research focus on multi-view dependency graphs for defect prediction in both mature and newly developed projects.

Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultra-portable and cost-effective, akin to the stethoscope. Nevertheless US image quality and diagnostic efficacy are still highly operator- and patient-dependent. In difficult-to-image patients, image quality is often insufficient for reliable diagnosis. In this paper, we put forth that US imaging systems can be recast as information-seeking agents that engage in reciprocal interactions with their anatomical environment. Such agents autonomously adapt their transmit-receive sequences to fully personalize imaging and actively maximize information gain in-situ. To that end, we will show that the sequence of pulse-echo experiments that a US system performs can be interpreted as a perception-action loop: the action is the data acquisition, probing tissue with acoustic waves and recording reflections at the detection array, and perception is the inference of the anatomical and or functional state, potentially including associated diagnostic quantities. We then equip systems with a mechanism to actively reduce uncertainty and maximize diagnostic value across a sequence of experiments, treating action and perception jointly using Bayesian inference given generative models of the environment and action-conditional pulse-echo observations. Since the representation capacity of the generative models dictates both the quality of inferred anatomical states and the effectiveness of inferred sequences of future imaging actions, we will be greatly leveraging the enormous advances in deep generative modelling that are currently disrupting many fields and society at large. Finally, we show some examples of cognitive, closed-loop, US systems that perform active beamsteering and adaptive scanline selection, based on deep generative models that track anatomical belief states.

Humans tell stories to make sense of the world and communicate their understanding of what happens. Storytelling supposes to be able to sort out which events are worth telling, deciding on a level of detail for describing events, selecting among possible causes the ones which are deemed worth telling. It also supposes to make use of an affective machinery for capturing an audience's attention (emotional contagion, suspense elicitation...). In the act of storytelling, structural and affective phenomena are thus combined with communicative goals in mind. This combination has indeed shown its effectiveness in this respect: the phenomenon of narrative transportation (the experience of being immersed in a story) has been linked to persuasion [27]. The narrative paradigm therefore provides an appropriate framework, in which causal reasoning about the situations narrated [53] is combined with narrative devices to encourage the audience's emotional involvement [51], to study and model how opinion is formed and evolves. Building a framework for reasoning about and unveiling storytelling mechanics could pave the way for intellectual selfdefense supporting tools, enabling citizens to arm themselves against hostile disinformation or influence campaigns. Previous works in structural narratology have studied the way stories are conveyed to their audience and seminal work from (for instance) Genette [25] or Propp [45] have previously served as the backbone inspiration for computational narrative models and storytelling systems [43].

Belief propagation is a fundamental message-passing algorithm for probabilistic reasoning and inference in graphical models. While it is known to be exact on trees, in most applications belief propagation is run on graphs with cycles. Understanding the behavior of loopy'' belief propagation has been a major challenge for researchers in machine learning, and several positive convergence results for BP are known under strong assumptions which imply the underlying graphical model exhibits decay of correlations. We show that under a natural initialization, BP converges quickly to the global optimum of the Bethe free energy for Ising models on arbitrary graphs, as long as the Ising model is \emph{ferromagnetic} (i.e. This holds even though such models can exhibit long range correlations and may have multiple suboptimal BP fixed points.

When agents interact with a complex environment, they must form and maintain beliefs about the relevant aspects of that environment. We propose a way to efficiently train expressive generative models in complex environments. We show that a predictive algorithm with an expressive generative model can form stable belief-states in visually rich and dynamic 3D environments. More precisely, we show that the learned representation captures the layout of the environment as well as the position and orientation of the agent. Our experiments show that the model substantially improves data-efficiency on a number of reinforcement learning (RL) tasks compared with strong model-free baseline agents.

Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems. More general counting variants of these problems, however, are still largely solved with hand-crafted solvers. To bridge this gap, we introduce belief propagation neural networks (BPNNs), a class of parameterized operators that operate on factor graphs and generalize Belief Propagation (BP). In its strictest form, a BPNN layer (BPNN-D) is a learned iterative operator that provably maintains many of the desirable properties of BP for any choice of the parameters. Empirically, we show that by training BPNN-D learns to perform the task better than the original BP: it converges 1.7x faster on Ising models while providing tighter bounds.

Neural Information Processing Systems http://nips.cc/

This paper addresses a challenging interactive task learning scenario we call rearrangement under unawareness: to manipulate a rigid-body environment in a context where the robot is unaware of a concept that's key to solving the instructed task. We propose SECURE, an interactive task learning framework designed to solve such problems by fixing a deficient domain model using embodied conversation. Through dialogue, the robot discovers and then learns to exploit unforeseen possibilities. Using SECURE, the robot not only learns from the user's corrective feedback when it makes a mistake, but it also learns to make strategic dialogue decisions for revealing useful evidence about novel concepts for solving the instructed task. Together, these abilities allow the robot to generalise to subsequent tasks using newly acquired knowledge. We demonstrate that a robot that is semantics-aware -- that is, it exploits the logical consequences of both sentence and discourse semantics in the learning and inference process -- learns to solve rearrangement under unawareness more effectively than a robot that lacks such capabilities.

Goal recognition (GR) involves inferring an agent's unobserved goal from a sequence of observations. This is a critical problem in AI with diverse applications. Traditionally, GR has been addressed using 'inference to the best explanation' or abduction, where hypotheses about the agent's goals are generated as the most plausible explanations for observed behavior. Alternatively, some approaches enhance interpretability by ensuring that an agent's behavior aligns with an observer's expectations or by making the reasoning behind decisions more transparent. In this work, we tackle a different challenge: explaining the GR process in a way that is comprehensible to humans. We introduce and evaluate an explainable model for goal recognition (GR) agents, grounded in the theoretical framework and cognitive processes underlying human behavior explanation. Drawing on insights from two human-agent studies, we propose a conceptual framework for human-centered explanations of GR. Using this framework, we develop the eXplainable Goal Recognition (XGR) model, which generates explanations for both why and why not questions. We evaluate the model computationally across eight GR benchmarks and through three user studies. The first study assesses the efficiency of generating human-like explanations within the Sokoban game domain, the second examines perceived explainability in the same domain, and the third evaluates the model's effectiveness in aiding decision-making in illegal fishing detection. Results demonstrate that the XGR model significantly enhances user understanding, trust, and decision-making compared to baseline models, underscoring its potential to improve human-agent collaboration.

Despite the significant interest in extending the AGM paradigm of belief change beyond finitary logics, the computational aspects of AGM have remained almost untouched. We investigate the computability of AGM contraction on non-finitary logics, and show an intriguing negative result: there are infinitely many uncomputable AGM contraction functions in such logics. Drastically, even if we restrict the theories used to represent epistemic states, in all non-trivial cases, the uncomputability remains. On the positive side, we identify an infinite class of computable AGM contraction functions on Linear Temporal Logic (LTL). We use B\"uchi automata to construct such functions as well as to represent and reason about LTL knowledge.