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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning

Unsupervised Salient Patch Selection for Data-Efficient Rein...

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5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Jiang, Zhaohui Weng, Paul Shanghai Jiao Tong Univ UM SJTU Joint Inst Shanghai Peoples R China

ISBN: (纸本)9783031434204;9783031434211

To improve the sample efficiency of vision-based deep reinforcement learning (RL), we propose a novel method, called SPIRL, to automatically extract important patches from input images. Following Masked Auto-Encoders, SPIRL is based on Vision Transformer models pre-trained in a self-supervised fashion to reconstruct images from randomly-sampled patches. These pre-trained models can then be exploited to detect and select salient patches, defined as hard to reconstruct from neighboring patches. In RL, the SPIRL agent processes selected salient patches via an attention module. We empirically validate SPIRL on Atari games to test its data-efficiency against relevant state-of-the-art methods, including some traditional model-based methods and keypoint-based models. In addition, we analyze our model's interpretability capabilities.

关键词： image-based deep reinforcement learning data efficiency interpretability keypoint

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Adaptive Team Behavior Planning Using Human Coach Commands 25th

Adaptive Team Behavior Planning Using Human Coach Commands

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RoboCup Symposium

作者： Musumeci, Emanuele Suriani, Vincenzo Antonioni, Emanuele Nardi, Daniele Bloisi, Domenico D. Sapienza Univ Rome Dept Comp Control & Management Engn Rome Italy Univ Basilicata Dept Math Comp Sci & & Econ Potenza Italy

ISBN: (纸本)9783031284687;9783031284694

In its operating life, an agent that needs to act in real environments is required to deal with rules and constraints that humans ask to satisfy. The set of rules specified by the human might influence the role of the agent without changing its goal or its current task. To this end, classical planning methodologies can be enriched with temporal goals and constraints that enforce non-Markovian properties on past traces. This work aims at exploring the application of real-time dynamic generation of policies whose possible trajectories are compliant with a set of Pure-Past Linear Time Logic rules, introducing novel human-robot interaction modalities for the high-level control of strategies for multiple agents. For proving the effectiveness of the proposed approach, we have carried out an evaluation on a partially observable, unpredictable, and dynamic scenario: the RoboCup soccer competition. In particular, we exploit human indications to condition the robot's behavior before or during the time of the match, as happens during human soccer matches.

关键词： Plan conditioning Multi-agent planning Robot soccer

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What Do Counterfactuals Say About the World? Reconstructing Probabilistic Logic Programs from Answers to "What If?" Queries 1

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32nd International Conference on Inductive Logic Programming (ILP)

作者： Rueckschloss, Kilian Weitkaemper, Felix Ludwig Maximilians Univ Munchen Oettingenstr 67 D-80538 Munich Germany

ISBN: (数字)9783031492990

ISBN: (纸本)9783031492983;9783031492990

A ProbLog program is a logic program with facts that only hold with a specified probability. Each ProbLog program gives rise to probability estimations for counterfactual statements of the form "A would be true, if we had forced B". This contribution studies program equivalence with respect to this counterfactual reasoning in the sense of Judea Pearl. Our main result reveals that each well-written ProbLog program with non-trivial probabilities is uniquely determined by its associated counterfactual estimations. More precisely, we give a procedure to reconstruct such a probabilistic logic program from its counterfactual output. As counterfactuals are part of our everyday language, our result indicates that they may also be a good language to express domain knowledge or readable program specifications.

关键词： Counterfactual Reasoning Program Equivalence Program Induction Structure Learning Probabilistic Logic Programming

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Learning Representations for Bipartite Graphs Using Multi-task Self-supervised Learning

Learning Representations for Bipartite Graphs Using Multi-ta...

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作者： Sethi, Akshay Gupta, Sonia Malhotra, Aakarsh Asthana, Siddhartha Mastercard AI Garage Gurugram India

ISBN: (纸本)9783031434174;9783031434181

Representation learning for bipartite graphs is a challenging problem due to its unique structure and characteristics. The primary challenge is the lack of extensive supervised data and the bipartite graph structure, where two distinct types of nodes exist with no direct connections between the nodes of the same kind. Hence, recent algorithms utilize Self Supervised Learning (SSL) to learn effective node embeddings without needing costly labeled data. However, conventional SSL methods learn through a single pretext task, making the trained model specific to the downstream task. This paper proposes a novel approach for learning generalized representations of bipartite graphs using multi-task SSL. The proposed method utilizes multiple self-supervised tasks to learn improved embeddings that capture different aspects of the bipartite graphs, such as graph structure, node features, and local-global information. We utilize deep multi-task learning (MTL) to further assist in learning generalizable self-supervised solution. To mitigate negative transfer when related and unrelated tasks are trained in MTL, we propose a novel DST++ algorithm. The proposed DST++ optimization algorithm improves existing DST by considering task affinities and groupings for better initialization and training. The end-to-end proposed method with complimentary SSL tasks and DST++ multi-task optimization is evaluated on three tasks: node classification, link prediction, and node regression, using four publicly available benchmark datasets. The results demonstrate that our proposed method outperforms state-of-the-art methods for representation learning in bipartite graphs. Specifically, our method achieves up to 12% improvement in accuracy for node classification and up to 9% improvement in AUC for link prediction tasks compared to the baseline methods.

关键词： GNNs Multi Task Unsupervised Self-Supervised

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Modulating Virtual Affective Elicitation by Human Body Odors: Advancing Research on Social Signal Processing in Virtual Reality 17th

Modulating Virtual Affective Elicitation by Human Body Odors...

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17th International Conference on Augmented Cognition (AC)

作者： Cervera-Torres, Sergio Minissi, Maria Eleonora Greco, Alberto Callara, Alejandro Ferdowsi, Saideh Citi, Luca Maddalon, Luna Chicchi Giglioli, Irene Alice Alcaniz, Mariano Univ Politecn Valencia Human Centred Technol Res Inst HUMAN Tech Valencia Spain Univ Pisa Res Ctr E Piaggio Pisa Italy Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

ISBN: (纸本)9783031350160;9783031350177

Human sensory systems are capable of encoding emotion-related signals during social interactions (e.g., fearful or happy facial expressions). In this regard, many emotion elicitation procedures have been reported within the scope of social signal processing research. Most of these procedures rely on socially relevant stimuli, such as emotional face images, videos, and, more recently, virtual reality (VR) scenes. Unfortunately, procedures involving cross-modal interactions beyond visual and acoustic stimuli, such as olfaction, are still scarce. In this sense, neuroscience supports a close link between the olfactory and affective systems. Moreover, experimental research has reported faster appraisals of emotional face images when congruent valence-laden artificial scents were presented (e.g., positive scent-happy face;negative scent-fearful face). Interestingly, recent findings indicate that emotion-related human-body odors (HBOs) might also modulate affective appraisals during a neutral virtual reality experience. However, whether and how emotion-related HBOs modulate affective VR experiences requires further examination. Here, an approach to this research question is proposed from a Virtual Reality-based Behavioral Biomarker (VRBB) experimental framework. Concretely, in the first place, a novel affective elicitation procedure based on social-emotional VR is introduced, wherein electro-dermal activity (EDA), heart-rate variability (HRV), electroencephalography (EEG), and affective appraisals, will be accounted for. In a second step, the modulating role of HBOs will be investigated regarding those measures. This work presents the envisioned model, details of the devised VEs, and a research design to test concrete hypotheses.

关键词： Human Body Odors (HBO) Emotion Virtual Environment social signal processing

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Online State Exploration: Competitive Worst Case and Learning-Augmented Algorithms

Online State Exploration: Competitive Worst Case and Learnin...

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作者： Im, Sungjin Moseley, Benjamin Xu, Chenyang Zhang, Ruilong Univ Calif Merced Elect Engn & Comp Sci Merced CA 95343 USA Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14068 USA

ISBN: (纸本)9783031434204;9783031434211

This paper introduces the online state exploration problem. In the problem, there is a hidden d-dimensional target state. We are given a distance function between different states in the space and a penalty function depending on the current state for each incorrect guess. The goal is to move to a vector that dominates the target state starting from the origin in the d-dimensional space while minimizing the total distance and penalty cost. This problem generalizes several natural online discrete optimization problems such as multi-dimensional knapsack cover, cow path, online bidding, and online search. For online state exploration, the paper gives results in the worst-case competitive analysis model and in the online algorithms augmented with the prediction model. The results extend and generalize many known results in the online setting.

关键词： Online Search Online Algorithms Competitive Ratio Learning-augmented Algorithms Worst-case Analysis

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Dialogue State Tracking with a Dialogue-Aware Slot-Level Schema Graph Approach 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Yang, Jie Song, Hui Xu, Bo Zhou, Haoran Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (数字)9783031402890

ISBN: (纸本)9783031402883;9783031402890

Dialogue State Tracking (DST) aims to keep dialogue states updated over the whole dialogue. Recently, slot self-attention mechanism and token-level schema graph are both proposed to capture slot relations based on prior knowledge or human experience, avoiding the independent prediction of slot values. However, they fall short in modeling the correlations among slots across domain, and the dialogue history encoding method injects noises into the slot representations. To address these issues, we propose a novel slot-level schema graph to involve high cooccurrence slot relations across domain. A two layers network is then adopted to force slots to pay attention only on the relevant dialogue context and related slots successively. We make a further comparison study in modeling slot relation to quantify that the improvement of our schema graph is superior to slot self-attention. Empirical results on benchmark datasets (i.e., MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.4) show that our approach outperforms strong baselines in both predefined ontology-based DST and open vocabulary-based DST methods.

关键词： Dialogue State Tracking Schema Graph Slot Self-Attention

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Comprehensive Analysis of Different Techniques for Data Augmentation and Proposal of New Variants of BOSME and GAN 18th

Comprehensive Analysis of Different Techniques for Data Augm...

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18th International Conference on Hybrid artificial intelligence Systems

作者： Garmendia-Orbegozo, Asier David Nunez-Gonzalez, Jose Angel Anton Gonzalez, Miguel Grana, Manuel Univ Basque Country UPV EHU Dept Appl Math Eibar 20600 Spain Basque Res & Technol Alliance BRTA TECNALIA San Sebastian 20009 Spain Univ Basque Country UPV EHU Computat Intelligence Grp San Sebastian 20018 Spain

ISBN: (纸本)9783031407246;9783031407253

In many environments in which detection of minority class instances is critical, the available data intended for training Machine Learning models is poorly distributed. The data imbalance usually produces deterioration of the trained model by generalisation of instances belonging to minority class predicting as majority class instances. To avoid these, different techniques have been adopted in the literature and expand the original database such as Generative Adversarial Networks (GANs) or Bayesian network-based over-sampling method (BOSME). Starting from these two methods, in this work we propose three new variants of data augmentation to address data imbalance issue. We use traffic event data from three different areas of California divided in two subgroups attending their severity. Experiments show that top performance cases where reached after using our variants. The importance of data augmentation techniques as preprocessing tool has been proved as well, as a consequence of performance drop of systems in which original databases with imbalanced data where used.

关键词： Data augmentation Data imbalance GANs

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Computing MUS-Based Inconsistency Measures 18th

Computing MUS-Based Inconsistency Measures

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18th European Conference on Logics in artificial intelligence (JELIA)

作者： Kuhlmann, Isabelle Niskanen, Andreas Jarvisalo, Matti Univ Hagen Hagen Germany Univ Helsinki Helsinki Finland

ISBN: (纸本)9783031436185;9783031436192

We detail two instantiations of a generic algorithm for the problematic and MUS-variable-based inconsistency measures, based on answer set programming and Boolean satisfiability (SAT). Empirically, the SAT-based approach allows for more efficiently computing the measures when compared to enumerating all minimal correction subsets of a knowledge base.

关键词： Inconsistency measurement minimal unsatisfiability

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Cold-Start Multi-hop Reasoning by Hierarchical Guidance and Self-verification

Cold-Start Multi-hop Reasoning by Hierarchical Guidance and ...

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作者： Xu, Mayi Sun, Ke Li, Yongqi Qian, Tieyun Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Intellectual Comp Lab Cultural Heritage Wuhan Peoples R China

ISBN: (纸本)9783031434143;9783031434150

Multi-hop reasoning has attracted wide attention for knowledge graph (KG) completion since it can provide interpretable reasoning paths. Most prior multi-hop reasoning studies assume the KGs are static with fixed entities. However, in real applications, KGs are often dynamic since new entities will emerge continuously in the form of new fact triplets. In this paper, we are particularly interested in the cold-start scenario toward dynamic KGs to facilitate more practical multi-hop reasoning, which aims to explore the reasoning paths between emerging entities and existing entities. There are two challenging issues arising from this scenario: i) lacking precise guidance since available information for emerging entities is extremely limited in the cold-start scenario, ii) lacking explicit path since the emerging entities and existing ones are isolated. To address these issues, we propose a generation-based model, namely SelfHier, to explore the reasoning paths by hierarchical guidance and self-verification strategies. The hierarchical guidance strategy guides the reasoning process using hierarchical fine-grained sub-relations and coarse-grained clusters. The self-verification strategy constructs explicit reasoning paths by supplementing some missing fact triplets. Experimental results prove that SelfHier performs well in the cold-start scenario on dynamic KGs and also significantly outperforms existing multi-hop reasoning methods in the standard scenario on static KGs.

关键词： Multi-hop Reasoning Cold-start Hierarchical Guidance Self-verification

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