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Rethinking the Physical Symbol Systems Hypothesis 16th

Rethinking the Physical Symbol Systems Hypothesis

引用

16th International Conference on artificial General intelligence (AGI)

作者： Rosenbloom, Paul S. Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA

ISBN: (纸本)9783031334689;9783031334696

It is now more than a half-century since the Physical Symbol Systems Hypothesis (PSSH) was first articulated as an empirical hypothesis. More recent evidence from work with neural networks and cognitive architectures has weakened it, but it has not yet been replaced in any satisfactory manner. Based on a rethinking of the nature of computational symbols - as atoms or placeholders - and thus also of the systems in which they participate, a hybrid approach is introduced that responds to these challenges while also helping to bridge the gap between symbolic and neural approaches, resulting in two new hypotheses, one that is to replace the PSSH and the other focused more directly on cognitive architectures.

关键词： Physical Symbol Systems Hybrid Symbol Systems Cognitive Architectures Neural Networks

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Automatic Sleep Stage Classification on EEG Signals Using Time-Frequency Representation 21st

Automatic Sleep Stage Classification on EEG Signals Using Ti...

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21st International Conference on artificial intelligence in Medicine (AIME)

作者： Dequidt, Paul Seraphim, Mathieu Lechervy, Alexis Gaez, Ivan Igor Brun, Luc Etard, Olivier Normandie Univ ENSICAEN UNICAEN UMR CNRS 6072GREYCCNRS Caen France Normandie Univ UNICAEN UMR S 1075 COMETECNRS Caen France

ISBN: (纸本)9783031343438;9783031343445

Sleep stage scoring based on electroencephalogram (EEG) signals is a repetitive task required for basic and clinical sleep studies. Sleep stages are defined on 30 s EEG-epochs from brainwave patterns present in specific frequency bands. Time-frequency representations such as spectrograms can be used as input for deep learning methods. In this paper we compare different spectrograms, encoding multiple EEG channels, as input for a deep network devoted to the recognition of image's visual patterns. We further investigate how contextual input enhance the classification by using EEG-epoch sequences of increasing lengths. We also propose a common evaluation framework to allow a fair comparison between state-of-art methods. Evaluations performed on a standard dataset using this unified protocol show that our method outperforms four state-of-art methods.

关键词： Sleep scoring time-frequency representation computer vision EEG signal processing

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Non-Normal Modal Description Logics 18th

Non-Normal Modal Description Logics

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

作者： Dalmonte, Tiziano Mazzullo, Andrea Ozaki, Ana Troquard, Nicolas Free Univ Bozen Bolzano Bolzano Italy Univ Trento Trento Italy Univ Oslo Oslo Norway Univ Bergen Bergen Norway

ISBN: (纸本)9783031436185;9783031436192

Modal logics are widely used in multi-agent systems to reason about actions, abilities, norms, or epistemic states. Combined with description logic languages, they are also a powerful tool to formalise modal aspects of ontology-based reasoning over an object domain. However, the standard relational semantics for modalities is known to validate principles deemed problematic in agency, deontic, or epistemic applications. To overcome these difficulties, weaker systems of so-called non-normal modal logics, equipped with neighbourhood semantics that generalise the relational one, have been investigated both at the propositional and at the description logic level. We present here a family of non-normal modal description logics, obtained by extending ALC-based languages with non-normal modal operators. For formulas interpreted on neighbourhood models over varying domains, we provide a modular framework of terminating, correct, and complete tableau-based satisfiability checking algorithms in NExpTime. For a subset of these systems, we also consider a reduction to satisfiability on constant domain relational models. Moreover, we investigate the satisfiability problem in fragments obtained by disallowing the application of modal operators to description logic concepts, providing tight ExpTime complexity results.

关键词： Multi agent systems

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Hybrid Approaches to Sentiment Analysis of Social Media Data 15th

Hybrid Approaches to Sentiment Analysis of Social Media Data

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15th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Thanh Luan Nguyen Thi Thanh Sang Nguyen Kozierkiewicz, Adrianna Vietnam Natl Univ VNU HCMC Int Univ Sch Comp Sci & Engn Ho Chi Minh City Vietnam Wroclaw Univ Sci & Technol Fac Comp Sci & Management Wroclaw Poland

ISBN: (纸本)9789819958368;9789819958375

Today, social media is accessible all around the world. It has become an online place for reviewing products or services. Social media is an appealing resource for enterprises looking to monitor user attitudes because of the massive volume of posts. It has gathered much attention in the field of sentiment analysis. Sentiment analysis models could be classified into three categories: Lexicon-based, classical Machine Learning, and Deep Learning model. The classification methods have received a lot of attention in the research area. Although supervised learning algorithms are crucial components, the preprocessing step greatly impacts the accuracy of the sentiment analysis task. In preprocessing, the negation handling method needs to get more attention as the most popular technique creates redundant features. This research proposes a negation handling method that utilizes the lexicon-model VADER (Valence Aware Dictionary for sEntiment Reasoning) for concentrating on high-sentiment words. The proposed negation handling method improves the accuracy of the classical machine learning algorithms Logistic Regression and Naive Bayes. In addition, the research proposes an approach to handling elongated words and emoji characters instead of removing them. An experiment is conducted for a comparison of the proposed methods and the chosen base techniques.

关键词： Sentiment Analysis Negation Handling Preprocessing

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Continuous Depth Recurrent Neural Differential Equations

Continuous Depth Recurrent Neural Differential Equations

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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： Anumasa, Srinivas Gunapati, Geetakrishnasai Srijith, P. K. Indian Inst Technol Hyderabad Dept Comp Sci & Engn Hyderabad India

ISBN: (纸本)9783031434143;9783031434150

Recurrent neural networks (RNNs) have brought a lot of advancements in sequence labeling tasks and sequence data. However, their effectiveness is limited when the observations in the sequence are irregularly sampled, where the observations arrive at irregular time intervals. To address this, continuous time variants of the RNNs were introduced based on neural ordinary differential equations (NODE). They learn a better representation of the data using the continuous transformation of hidden states over time, taking into account the time interval between the observations. However, they are still limited in their capability as they use the discrete transformations and a fixed discrete number of layers (depth) over an input in the sequence to produce the output observation. We intend to address this limitation by proposing RNNs based on differential equations which model continuous transformations over both depth and time to predict an output for a given input in the sequence. Specifically, we propose continuous depth recurrent neural differential equations (CDR-NDE) which generalize RNN models by continuously evolving the hidden states in both the temporal and depth dimensions. CDR-NDE considers two separate differential equations over each of these dimensions and models the evolution in temporal and depth directions alternatively. We also propose the CDR-NDE-heat model based on partial differential equations which treats the computation of hidden states as solving a heat equation over time. We demonstrate the effectiveness of the proposed models by comparing against the state-of-the-art RNN models on real world sequence labeling problems.

关键词： Neural networks differential equations sequence labeling

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Topic Modelling and Frame Identification for Political Arguments 1

引用

21st International Conference of the Italian-Association-for-artificial-intelligence (AIxIA)

作者： Haddadan, Shohreh Cabrio, Elena Soto, Axel J. Villata, Serena Univ Luxembourg Esch Sur Alzette Luxembourg Univ Cote dAzur CNRS INRIA I3S Nice France Univ Nacl Sur Bahia Blanca Argentina UNS CONICET Inst Comp Sci & Engn Bahia Blanca Argentina

ISBN: (数字)9783031271816

ISBN: (纸本)9783031271809;9783031271816

Presidential debates are one of the most salient moments of a presidential campaign, where candidates are challenged to discuss the main contemporary and historical issues in a country. These debates represent a natural ground for argumentative analysis, which has been always employed to investigate political discourse structure in philosophy and linguistics. In this paper, we take the challenge to analyse these debates from the topic modeling and framing perspective, to enrich the investigation of these data. Our contribution is threefold: first, we apply transformer-based language models (i.e., BERT and RoBERTa) to the classification of generic frames showing that these models improve the results presented in the literature for frame identification;second, we investigate the task of topic modelling in political arguments from the U.S. presidential campaign debates, applying an unsupervised machine learning approach;and finally, we discuss various visualisations of the identified topics and frames from these U.S. presidential election debates to allow a further interpretation of such data.

关键词： Argument mining Framing Political debates

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My Actions Speak Louder Than Your Words: When User Behavior Predicts Their Beliefs About Agents' Attributes 4th

My Actions Speak Louder Than Your Words: When User Behavior ...

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4th International Conference on artificial intelligence in HCI (AI-HCI) Held as Part of the 25th International Conference on Human-Computer Interaction (HCII)

作者： Gurney, Nikolos Pynadath, David, V Wang, Ning Univ Southern Calif Inst Creat Technol Viterbi Sch Engn Comp Sci Dept Los Angeles CA 90007 USA

ISBN: (纸本)9783031358937;9783031358944

An implicit expectation of asking users to rate agents, such as an AI decision-aid, is that they will use only relevant information-ask them about an agent's benevolence, and they should consider whether or not it was kind. Behavioral science, however, suggests that people sometimes use irrelevant information. We identify an instance of this phenomenon, where users who experience better outcomes in a human-agent interaction systematically rated the agent as having better abilities, being more benevolent, and exhibiting greater integrity in a post hoc assessment than users who experienced worse outcomes-which were the result of their own behavior-with the same agent. Our analyses suggest the need for augmentation of models so they account for such biased perceptions as well as mechanisms so that agents can detect and even actively work to correct this and similar biases of users.

关键词： Agent factors Trait attribution Cognitive bias Agent-user interactions

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Enhancing Robustness of Prototype with Attentive Information Guided Alignment in Few-Shot Classification 27th

Enhancing Robustness of Prototype with Attentive Information...

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

作者： Kim, Tae-Hyung Nam, Woo-Jeoung Lee, Seong-Whan Korea Univ Dept Artificial Intelligence Seoul South Korea Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9783031333736;9783031333743

In this paper, we carefully revisit the issues of conventional few-shot learning: i) gaps in highlighted features between objects in support and query samples, and ii) losing the explicit local properties due to global pooled features. Motivated by them, we propose a novel method to enhance robustness in few-shot learning by aligning prototypes with abundantly informed ones. As a way of providing more information, we smoothly augment the support image by carefully manipulating the discriminative part corresponding to the highest attention score to consistently represent the object without distorting the original information. In addition, we leverage word embeddings of each class label to provide abundant feature information, serving as the basis for closing gaps between prototypes of different branches. The two parallel branches of explicit attention modules independently refine support prototypes and information-rich prototypes. Then, the support prototypes are aligned with superior prototypes to mimic rich knowledge of attention-based smooth augmentation and word embeddings. We transfer the imitated knowledge to queries in a task-adaptive manner and cross-adapt the queries and prototypes to generate crucial features for metric-based few-shot learning. Extensive experiments demonstrate that our method consistently outperforms existing methods on four benchmark datasets.

关键词： Few-shot classification Data augmentation Attention mechanism

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QA-Matcher: Unsupervised Entity Matching Using a Question Answering Model 1

引用

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

作者： Hayashi, Shogo Dong, Yuyang Oyamada, Masafumi BizReach Inc Tokyo Japan NEC Corp Ltd Tokyo Japan NEC Corp Ltd Tokyo Japan

ISBN: (数字)9783031333835

ISBN: (纸本)9783031333828;9783031333835

Entity matching (EM) is a fundamental task in data integration, which involves identifying records that refer to the same real-world entity. Unsupervised EM is often preferred in real-world applications, as labeling data is often a labor-intensive process. However, existing unsupervised methods may not always perform well because the assumptions for these methods may not hold for tasks in different domains. In this paper, we propose QA-Matcher, an unsupervised EM model that is domain-agnostic and doesn't require any particular assumptions. Our idea is to frame EM as question answering (QA) by utilizing a trained QA model. Specifically, we generate a question that asks which record has the characteristics of a particular record and a passage that describes other records. We then use the trained QA model to predict the record pair that corresponds to the question-answer as a match. QA-Matcher leverages the power of a QA model to represent the semantics of various types of entities, allowing it to identify identical entities in a QA-like fashion. In extensive experiments on 16 real-world datasets, we demonstrate that QA-Matcher outperforms unsupervised EM methods and is competitive with supervised methods.

关键词： entity matching question answering

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Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense? 1

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46th German Conference on artificial intelligence (KI)

作者： Wilken, Nils Cohausz, Lea Bartelt, Christian Stuckenschmidt, Heiner Univ Mannheim Inst Enterprise Syst Mannheim Germany Univ Mannheim Data & Web Sci Grp Mannheim Germany

ISBN: (数字)9783031426087

ISBN: (纸本)9783031426070;9783031426087

Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., considers trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition) for goal recognition. In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.

关键词： Online Goal Recognition Classical Planning Planning Landmarks

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