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

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Algebraic Models for Qualified Aggregation in General Rough Sets, and Reasoning Bias Discovery

Algebraic Models for Qualified Aggregation in General Rough ...

引用

International Joint Conference on Rough Sets (IJCRS)

作者： Mani, A. Indian Stat Inst Kolkata Machine Intelligence Unit 203 BT Rd Kolkata 700108 India

ISBN: (纸本)9783031509582;9783031509599

In the context of general rough sets, the act of combining two things to form another is not straightforward. The situation is similar for other theories that concern uncertainty and vagueness. Such acts can be endowed with additional meaning that go beyond structural conjunction and disjunction as in the theory of *- norms and associated implications over L-fuzzy sets. In the present research, algebraic models of acts of combining things in generalized rough sets over lattices with approximation operators (called rough convenience lattices) is invented. The investigation is strongly motivated by the desire to model skeptical or pessimistic, and optimistic or possibilistic aggregation in human reasoning, and the choice of operations is constrained by the perspective. Fundamental results on the weak negations and implications afforded by the minimal models are proved. In addition, the model is suitable for the study of discriminatory/toxic behavior in human reasoning, and of ML algorithms learning such behavior.

关键词： Abstract Approximations Rough Implications Algebraic Semantics Skeptical Reasoning Overly Optimistic Reasoning L-Fuzzy Implications Granular Operator Spaces Rough Convenience Lattices Algorithmic Bias Discovery

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Knowledge-Enhanced Hierarchical Transformers for Emotion-Cause Pair Extraction 1

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Wang, Yuwei Li, Yuling Yu, Kui Hu, Yimin Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230601 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei Peoples R China

ISBN: (数字)9783031333835

ISBN: (纸本)9783031333828;9783031333835

Emotion-cause pair extraction (ECPE) aims to extract all potential pairs of emotions and corresponding cause(s) from a given document. Current methods have focused on extracting possible emotion-cause pairs by directly analyzing the given documents on the basis of a large training set. However, there are many hard-matching emotion-cause pairs that require commonsense knowledge to understand. Exploiting only the given documents is insufficient to capture the latent semantics behind these hard-matching emotion-cause pairs, which may downgrade the performance of existing ECPE methods. To fill this gap, we propose a Knowledge-Enhanced Hierarchical Transformers framework for the ECPE task. Specifically, we first inject commonsense knowledge into the given documents to construct the knowledge-enhanced clauses. To incorporate the injected knowledge into the clause representations, we then develop a hierarchical Transformers module that leverages two different types of transformer blocks to encode knowledge-enriched clause representations at both global and local stages. Experimental results show that our method achieves state-of-the-art performance.

关键词： Emotion-cause pair extraction Commonsense knowledge Hierarchical Transformers

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Creativity, Intentions, and Self-Narratives: Can AI Really Be Creative? 22nd

Creativity, Intentions, and Self-Narratives: Can AI Really B...

引用

22nd EPIA Conference on artificial intelligence (EPIA)

作者： Giannuzzo, Anais Univ Geneva Geneva Switzerland

ISBN: (纸本)9783031490101;9783031490118

In this paper, I discuss the question of whether AI can be creative. I argue that AI-produced artworks can display features of creativity, but that the processes leading to the creative product are not creative. I distinguish between and describe the creative processes of humans and the generation-processes of AI. I identify one property of the former, which enables me to distinguish it from the latter: creative processes are instances of self-expression. An important feature of self-expressiveness, I argue, is that it can be retold in a self-narrative.

关键词： Creativity AI Process Product Narrative

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Cooperative Driving at Intersections Through Agent-Based Argumentation 24th

Cooperative Driving at Intersections Through Agent-Based Arg...

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24th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA)

作者： Mariani, Stefano Ferrari, Dario Zambonelli, Franco Univ Modena & Reggio Emilia Dept Sci & Methods Engn Reggio Emilia Italy

ISBN: (纸本)9783031212024;9783031212031

In a future of self-driving and connected vehicles, cooperative driving will be the key to guarantee that not only isolated vehicles can hit the road safely on their own, but that the collective of vehicles displays efficient and safe behaviours. Intersection crossing is arguably the most challenging problem for cooperative driving, as vehicles need to coordinate their relative movements while avoiding collisions and optimising intersection throughput. In this paper, we propose a multi-agent based approach exploiting computational argumentation to coordinate vehicles at intersections: vehicles approaching an intersection, represented by agents, argue about their right of way, while an arbitration process resolves conflicting arguments (i.e., leading to vehicle collisions) by applying a configurable conflicts resolution policy and suggesting alternative routes to vehicles. Extensive simulation results show that - in most situations - the argumentation-based approach enables increasing the overall throughput at intersections while decreasing vehicles' delay.

关键词： Cooperative driving Intersection crossing Multi-agent system Argumentation Coordination

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Information Retrieval from Legal Documents with Ontology and Graph Embeddings Approach 36th

Information Retrieval from Legal Documents with Ontology and...

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36th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Dang, Dung, V Nguyen, Hien D. Ngo, Hung Pham, Vuong T. Nguyen, Diem Univ Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Technol Univ Dublin Dublin Ireland Univ Sci Fac Math & Comp Sci Ho Chi Minh City Vietnam Sai Gon Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9783031368189;9783031368196

The legal search has great demand and a role in society. Ontology is a useful solution to represent the legal domain. This paper introduces a method to extract knowledge from law documents for building a legal knowledge base as the Legal-Onto Ontology. The method also proposes a solution to extract concepts and their relationships to create a knowledge graph. This is the foundation for solving the semantic query problem and returning the correct answer to meet the user's needs. The experimental results show that it is possible to build a law lookup application, which meets the practical requirements of users.

关键词： Knowledge Representation Knowledge graph Graph embedding Legal document

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Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting 8th

Explainable Parallel RCNN with Novel Feature Representation ...

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8th International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Shi, Jimeng Myana, Rukmangadh Stebliankin, Vitalii Shirali, Azam Narasimhan, Giri Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA

ISBN: (纸本)9783031498954;9783031498961

Accurate time series forecasting is a fundamental challenge in data science, as it is often affected by external covariates such as weather or human intervention, which in many applications, may be predicted with reasonable accuracy. We refer to them as predicted future covariates. However, existing methods that attempt to predict time series in an iterative manner with auto-regressive models end up with exponential error accumulations. Other strategies that consider the past and future in the encoder and decoder respectively limit themselves by dealing with the past and future data separately. To address these limitations, a novel feature representation strategy - shifting - is proposed to fuse the past data and future covariates such that their interactions can be considered. To extract complex dynamics in time series, we develop a parallel deep learning framework composed of RNN and CNN, both of which are used in a hierarchical fashion. We also utilize the skip connection technique to improve the model's performance. Extensive experiments on three datasets reveal the effectiveness of our method. Finally, we demonstrate the model interpretability using the Grad-CAM algorithm.

关键词： Time series

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Toward Interpretable Machine Learning: Constructing Polynomial Models Based on Feature Interaction Trees 27th

Toward Interpretable Machine Learning: Constructing Polynomi...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Jang, Jisoo Kim, Mina Bui, Tien-Cuong Li, Wen-Syan Seoul Natl Univ 1 Gwanak Ro Gwanak Gu Seoul South Korea

ISBN: (纸本)9783031333767;9783031333774

As AI has been applied in many decision-making processes, ranging from loan application approval to predictive policing, the interpretability of machine learning models is increasingly important. Interpretable models and post-hoc explainability are two approaches in eXplainable AI (XAI). We follow the argument that transparent models should be used instead of black-box ones in real-world applications, especially regarding high-stakes decisions. In this paper, we propose PolyFIT to address two major issues in XAI: (1) bridging the gap between black-box and interpretable models and (2) experimentally validating the trade-off relationship between model performance and explainability. PolyFIT is a novel polynomial model construction method assisted by the knowledge of feature interactions in black-box models. PolyFIT uses extracted feature interaction knowledge to build interaction trees, which are then transformed into polynomial models. We evaluate the predictive performance of PolyFIT with baselines using four publicly available data sets, Titanic survival, Adult income, Boston house price, and California house price. Our method outperforms linear models by 5% and 56% in classification and regression tasks on average, respectively. We also conducted usability studies to derive the trade-off relationship between model performance and explainability. The studies validate our hypotheses about the actual relationship between model performance and explainability.

关键词： eXplainable AI transparent models polynomial model explainability evaluation

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Interactive Link Prediction as a Downstream Task for Foundational GUI Understanding Models 1

引用

46th German Conference on artificial intelligence (KI)

作者： Johns, Christoph Albert Barz, Michael Sonntag, Daniel Aarhus Univ Aarhus Denmark German Res Ctr Artificial Intelligence DFKI Saarbrucken Germany Oldenburg Univ Appl Artificial Intelligence Oldenburg Germany

ISBN: (数字)9783031426087

ISBN: (纸本)9783031426070;9783031426087

AI models that can recognize and understand the semantics of graphical user interfaces (GUIs) enable a variety of use cases ranging from accessibility to automation. Recent efforts in this domain have pursued the development of a set of foundation models: generic GUI understanding models that can be used off-the-shelf to solve a variety of GUI-related tasks, including ones that they were not trained on. In order to develop such foundation models, meaningful downstream tasks and baselines for GUI-related use cases will be required. In this paper, we present interactive link prediction as a downstream task for GUI understanding models and provide baselines as well as testing tools to effectively and efficiently evaluate predictive GUI understanding models. In interactive link prediction, the task is to predict whether tapping on an element on one screen of a mobile application (source element) navigates the user to a second screen (target screen). If this task is solved sufficiently, it can demonstrate an understanding of the relationship between elements and components across screens and enable various applications in GUI design automation and assistance. To encourage and support research on interactive link prediction, this paper contributes (1) a pre-processed large-scale dataset of links in mobile applications (18,830 links from 5,362 applications) derived from the popular RICO dataset, (2) performance baselines from five heuristic-based and two learning-based GUI understanding models, (3) a small-scale dataset of links in mobile GUI prototypes including ratings from an online study with 36 end-users for out-of-sample testing, and (4) a Figma plugin that can leverage link prediction models to automate and assist mobile GUI prototyping.

关键词： Link prediction GUI prototyping Mobile GUIs Online machine learning

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Buy One Get 14 Free: Evaluating Local Reductions for Modal Logic 29th

Buy One Get 14 Free: Evaluating Local Reductions for Modal L...

引用

29th International Conference on Automated Deduction (CADE)

作者： Nalon, Claudia Hustadt, Ullrich Papacchini, Fabio Dixon, Clare Univ Brasilia Dept Comp Sci Brasilia DF Brazil Univ Liverpool Dept Comp Sci Liverpool Merseyside England Lancaster Univ Leipzig Sch Comp & Commun Leipzig Germany Univ Manchester Dept Comp Sci Manchester Lancs England

ISBN: (纸本)9783031384998;9783031384981

We are interested in widening the reasoning support for propositional modal logics in the so-called modal cube. The modal cube consists of extensions of the basic modal logic K with an arbitrary combination of the modal axioms B, D, T, 4 and 5. We revisit recently developed local reductions from all logics in the modal cube to a normal form comprising sets of clausal formulae with associated modal levels. We extend these reductions further to the basic modal logic K, called definitional reductions. This enables any prover for K to be used to solve the satisfiability problem for all logics in the modal cube. We also present alternative, axiomatic, reductions based on ideas originally proposed by Kracht, providing new theoretical results and improved bounds on the size of the reductions. We compare both sets of reductions combined with state-of-the-art provers for K on a large set of parametric benchmarks for all logics in the modal cube. The results show that the provers perform better with reductions based on the clausal normal form than the axiomatic reductions.

关键词： Geometry

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On Relation Between Facial Expressions and Emotions 16th

On Relation Between Facial Expressions and Emotions

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16th International Conference on artificial General intelligence (AGI)

作者： Samsonovich, Alexei, V Sidorov, Alexandr Inozemtsev, Alexandr Natl Res Nucl Univ MEPhI Moscow Russia

ISBN: (纸本)9783031334689;9783031334696

Human face is used to express affects and feelings, either involuntary or deliberately. How many dimensions of emotional flavors can be robustly distinguished in facial expressions, across individuals and cultures? Here we offer an answer and develop a practical approach to generate synthetic emotional facial expressions. Results can be used in studies of synthetic emotions.

关键词： machine learning emotion modeling Facial Action Coding System (FACS) face attribute analysis DeepFace

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