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2nd International Conference on Artificial Intelligence Planning systems, AIPS 1994

作者： Young, R. Michael Pollack, Martha E. Moore, Johanna D. Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Computer Science University of Pittsburgh PittsburghPA15260 United States Learning Research and Development Center University of Pittsburgh PittsburghPA15260 United States

ISBN: (纸本)0929280563

We describe DPOCL, a partial-order causal link planner that includes action decomposition. DPOCL builds directly on the SNLP algorithm (McAllester & Rosenblitt 1991), and hence is clear and simple, and can readily be integrated with other SNLP extensions. In addition, DPOCL is specifically designed to handle partially specified action decompositions. Plan generation in DPOCL exploits the planner's ability to fill in the missing pieces of a partially specified subplan in a way that uses the existing context of the larger plan bang constructed. Copyright © 1994, AAAI (***). All rights reserved.

关键词： Decomposition

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Learning Subjective Nouns using Extraction Pattern Bootstrapping 7

Learning Subjective Nouns using Extraction Pattern Bootstrap...

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7th Conference on Natural Language Learning, CoNLL 2003 at HLT-NAACL 2003

作者： Riloff, Ellen Wiebe, Janyce Wilson, Theresa School of Computing University of Utah Salt Lake CityUT84112 United States Department of Computer Science University of Pittsburgh PittsburghPA15260 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States

We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjective nouns, discourse features, and subjectivity clues identified in prior research. The bootstrapping algorithms learned over 1000 subjective nouns, and the subjectivity classifier performed well, achieving 77% recall with 81% precision. © 2003 Proceedings of the 7th Conference on Natural Language Learning, CoNLL 2003 at HLT-NAACL 2003. All rights reserved.

关键词： Extraction

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Mixing Strict and Defeasible Inheritance 7

Mixing Strict and Defeasible Inheritance

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7th National Conference on Artificial Intelligence, AAAI 1988

作者： Horty, John F. Thomason, Richmond H. Computer Science Department Carnegie Mellon University PittsburghPA15213 United States Philosophy Department University of Maryland College ParkMD20742 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States

ISBN: (纸本)0262510553

Commonsense or expert knowledge of any rich domain involves an intricate mixture of strict and defeasible information. The importance of representing defeasible information in an inheritance system has been widely recognized, but it is not enough for a system to represent only defeasible information: without the ability to represent strict information as well, the system cannot represent definitional relations among concepts. As a response to this difficulty, we present a well-defined and intuitively attractive theory of inheritance for IS-A hierarchies containing strict and defeasible links types mixed together. Copyright © 1988, AAAI (***). All rights reserved.

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Which side are you on? Identifying perspectives at the document and sentence levels 10

Which side are you on? Identifying perspectives at the docum...

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10th Conference on Computational Natural Language Learning, CoNLL-X

作者： Lin, Wei-Hao Wilson, Theresa Wiebe, Janyce Hauptmann, Alexander Language Technologies Institute Carnegie Mellon University Pittsburgh PA 15213 United States Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 United States

In this paper we investigate a new problem of identifying the perspective from which a document is written. By perspective we mean a point of view, for example, from the perspective of Democrats or Republicans. Can computers learn to identify the perspective of a document? Not every sentence is written strongly from a perspective. Can computers learn to identify which sentences strongly convey a particular perspective? We develop statistical models to capture how perspectives are expressed at the document and sentence levels, and evaluate the proposed models on articles about the Israeli-Palestinian conflict. The results show that the proposed models successfully learn how perspectives are reflected in word usage and can identify the perspective of a document with high accuracy. ©2006 Association for Computational Linguistics.

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The Third International Conference on human-robot interaction

The Third International Conference on human-robot interactio...

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作者： Fong, Terry Dautenhahn, Kerstin Scheutz, Matthias Demiris, Yiannis Intelligent Robotics Group NASA Ames Research Center Department of Artificial Intelligence School of Computer Science University of Herfordshire Herfordshire United Kingdom Adaptive Systems Research Group University of Herfordshire Herfordshire United Kingdom Department of Cognitive Science Computer Science and Informatics Cognitive Science Program Indiana University Bloomington IN United States Intelligent Robotics Team Department of Electrical Engineering Imperial College London London United Kingdom

The third international conference on Human-Robot Interaction (HRI-2008) was held in Amsterdam, The Netherlands, March 12-15, 2008. The theme of HRI-2008, "living with robots," highlights the importance of the technical and social issues underlying human-robot interaction with companion and assistive robots for long-term use in everyday life and work activities. More than 250 researchers, practitioners, and exhibitors attended the conference, and many more contributed to the conference as authors or reviewers. HRI-2009 will be held in San Diego, California, from March 11-13, 2009. Copyright © 2008, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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A robotic and virtual testing platform highlighting the promise of soft wearable actuators for wrist tremor suppression

Device

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Device 2025年

作者： Alona Shagan Shomron Christina Chase-Markopoulou Johannes R. Walter Johanna Sellhorn-Timm Yitian Shao Tobias Nadler Audrey Benson Isabell Wochner Ellen H. Rumley Isabel Wurster Philipp Klocke Daniel Weiss Syn Schmitt Christoph Keplinger Daniel F.B. Haeufle Robotic Materials Department Max Planck Institute for Intelligent Systems Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Stuttgart Germany Stuttgart Center for Simulation Science (SimTech) University of Stuttgart Stuttgart Germany Hertie Institute for Clinical Brain Research University of Tübingen Tübingen Germany Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany Institute for Computer Engineering (ZITI) University of Heidelberg Heidelberg Germany Paul M. Rady Department of Mechanical Engineering University of Colorado Boulder CO USA Center for Neurology Department of Neurodegenerative Diseases University of Tübingen Tübingen Germany Center for Bionic Intelligence (BITS) Tübingen and Stuttgart Germany Material Science and Engineering Program University of Colorado Boulder CO USA Werner Reichardt Center for Integrative Neuroscience University of Tübingen Tübingen Germany

Nearly 80 million people in the world deal with medical conditions that cause involuntary periodic movements known as tremors. Wearable soft robotic devices offer a potential solution for actively suppressing these tremors. However, existing prototypes face limitations in actuation performance and complex testing procedures. We present a comprehensive approach for the rapid evaluation of emerging wearable tremor-suppression technologies. This method combines reproducing patient-recorded tremor episodes and measuring tremor suppression in a robotic platform, termed a “mechanical patient”, with validation of the achieved suppression performance of soft actuators via biomechanical modeling, thereby avoiding time-consuming clinical testing in the early stages of development. Using this approach, we highlight that an antagonistic pair of slim and lightweight electrohydraulic actuators can effectively suppress clinically relevant tremors between 2 and 8 Hz. The biomechanical model confirms that the pair of actuators generates adequate suppression forces for all tested tremors.

关键词： tremor suppression mechanical patient soft actuators wearable devices biomechanical simulation DTI-4: Validate

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Explanation by progressive exaggeration

arXiv

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arXiv 2019年

作者： Singla, Sumedha Pollack, Brian Chen, Junxiang Batmanghelich, Kayhan Department of Computer Science University of Pittsburgh Department of Biomedical Informatics University of Pittsburgh Department of Biomedical Informatics Department of Computer Science Intelligent Systems Program University of Pittsburgh

As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical. Classical approaches that assess feature importance (e.g., saliency maps) do not explain how and why a particular region of an image is relevant to the prediction. We propose a method that explains the outcome of a classification black-box by gradually exaggerating the semantic effect of a given class. Given a query input to a classifier, our method produces a progressive set of plausible variations of that query, which gradually change the posterior probability from its original class to its negation. These counter-factually generated samples preserve features unrelated to the classification decision, such that a user can employ our method as a "tuning knob" to traverse a data manifold while crossing the decision boundary. Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis 6

Learning Adaptation and Generalization from Human-Inspired M...

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6th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023

作者： Ho, Joshua Wang, Chien-Min King, Chung-Ta You, Yi-Hsin Feng, Chi-Wei Chen, Yen-Min Kuo, Bo-Yi Institute of Information Science Academia Sinica Taipei11529 Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Taiwan University Taipei10631 Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei10022 Taiwan

ISBN: (纸本)9798350331288

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a humaninspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future. © 2023 IEEE.

关键词： Reinforcement learning

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Integrating knowledge tracing and item response theory: A tale of two frameworks 22

Integrating knowledge tracing and item response theory: A ta...

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Workshop of the 22nd Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Co-located with the 22nd Conference on User Modeling, Adaptation, and Personalization, UMAP 2014

作者： Khajah, Mohammad M. Huang, Yun González-Brenes, José P. Mozer, Michael C. Brusilovsky, Peter Department of Computer Science Institute of Cognitive Science University of Colorado BoulderCO80309-0430 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Digital Data Analytics and Adaptive Learning Pearson Research and Innovation Network PhiladelphiaPA19103 United States

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing.

关键词： Students

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Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition

arXiv

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arXiv 2024年

作者： Buettner, Kyle Malakouti, Sina Li, Xiang Lorraine Kovashka, Adriana Intelligent Systems Program University of Pittsburgh PA United States Department of Computer Science University of Pittsburgh PA United States

Existing object recognition models have been shown to lack robustness in diverse geographical scenarios due to domain shifts in design and context. Class representations need to be adapted to more accurately reflect an object concept under these shifts. In the absence of training data from target geographies, we hypothesize that geographically diverse descriptive knowledge of categories can enhance robustness. For this purpose, we explore the feasibility of probing a large language model for geography-based object knowledge, and we examine the effects of integrating knowledge into zero-shot and learnable soft prompting with CLIP. Within this exploration, we propose geography knowledge regularization to ensure that soft prompts trained on a source set of geographies generalize to an unseen target set. Accuracy gains over prompting baselines on DollarStreet while training only on Europe data are up to +2.8/1.2/1.6 on target data from Africa/Asia/Americas, and +4.6 overall on the hardest classes. Competitive performance is shown vs. few-shot target training, and analysis is provided to direct future study of geographical robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Object recognition

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