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Patrick M. Pilarski | Richard S. Sutton | Katya Kudashkina | R. Sutton | P. Pilarski | Katya Kudashkina
[1] H. Woodrow. The ability to learn. , 1946, Psychological review.
[2] D. C. Englebart,et al. Augmenting human intellect: a conceptual framework , 1962 .
[3] Nilo Lindgren. Purposive systems: The edge of Knowledge , 1968, IEEE Spectrum.
[4] Jaime R Carbonell,et al. Mixed-initiative man-computer instructional dialogues , 1970 .
[5] Terry Winograd,et al. Procedures As A Representation For Data In A Computer Program For Understanding Natural Language , 1971 .
[6] J. Forrester. Counterintuitive behavior of social systems , 1971 .
[7] Robert F. Simmons,et al. Generating English discourse from semantic networks , 1972, CACM.
[8] Terry Winograd,et al. Breaking the complexity barrier again , 1973, SIGPLAN '73.
[9] Richard Power,et al. A computer model of conversation , 1974 .
[10] Mario C. Grignetti,et al. An "intelligent" on-line assistant and tutor: NLS-scholar , 1975, AFIPS '75.
[11] Bertram C. Bruce. Belief systems and language understanding , 1975 .
[12] Philip R. Cohen. On knowing what to say: planning speech acts. , 1978 .
[13] James F. Allen. A plan-based approach to speech act recognition , 1979 .
[14] Allen Newell,et al. Computer text-editing: An information-processing analysis of a routine cognitive skill , 1980, Cognitive Psychology.
[15] Glenn Langford. The Nature of Purpose , 1981 .
[16] Michael H. Long. INPUT, INTERACTION, AND SECOND‐LANGUAGE ACQUISITION , 1981 .
[17] Julia Hirschberg,et al. User Participation in the Reasoning Processes of Expert Systems , 1982, AAAI.
[18] Barbara J. Grosz,et al. TEAM: A Transportable Natural-Language Interface System , 1983, ANLP.
[19] William A Woods,et al. Natural Language Communication with Machines: An Ongoing Goal. , 1983 .
[20] Stephen F. Smith,et al. ISIS—a knowledge‐based system for factory scheduling , 1984 .
[21] Richard Young,et al. Making Input Comprehensible: Do Interactional Modifications Help? , 1986 .
[22] Richard C. Waters. KBEmacs: Where's the AI? , 1986, AI Mag..
[23] Geoffrey E. Hinton,et al. Schemata and Sequential Thought Processes in PDP Models , 1986 .
[24] Timothy W. Finin,et al. Natural language interactions with artificial experts , 1986, Proceedings of the IEEE.
[25] T. Pica. Second-language Acquisition, Social Interaction, and the Classroom. , 1987 .
[26] Gail E. Kaiser,et al. Intelligent assistance for software development and maintenance , 1988, IEEE Software.
[27] Johanna D. Moore,et al. Planning Text for Advisory Dialogues , 1989, ACL.
[28] Sandra Carberry,et al. Plan Recognition and Its Use in Understanding Dialog , 1989 .
[29] Yaman Arkun,et al. Neural Network Modeling and an Extended DMC Algorithm to Control Nonlinear Systems , 1990, 1990 American Control Conference.
[30] Richard S. Sutton,et al. Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming , 1990, ML.
[31] Leslie Pack Kaelbling,et al. Input Generalization in Delayed Reinforcement Learning: An Algorithm and Performance Comparisons , 1991, IJCAI.
[32] Satinder P. Singh,et al. Reinforcement Learning with a Hierarchy of Abstract Models , 1992, AAAI.
[33] Pattie Maes,et al. A learning interface agent for scheduling meetings , 1993, IUI '93.
[34] Oliver G. Selfridge,et al. The Gardens of Learning: A Vision for AI , 1993, AI Mag..
[35] D. Richard Hipp,et al. Spoken Natural Language Dialog Systems: A Practical Approach , 1994 .
[36] Susan M. Gass,et al. Input, Interaction, and Second Language Production , 1994, Studies in Second Language Acquisition.
[37] C Kamm,et al. User interfaces for voice applications. , 1994, Proceedings of the National Academy of Sciences of the United States of America.
[38] Eugenio Guglielmelli,et al. Robot assistants: Applications and evolution , 1996, Robotics Auton. Syst..
[39] Tom Routen,et al. Intelligent Tutoring Systems , 1996, Lecture Notes in Computer Science.
[40] Christopher G. Atkeson,et al. A comparison of direct and model-based reinforcement learning , 1997, Proceedings of International Conference on Robotics and Automation.
[41] Roberto Pieraccini,et al. Learning dialogue strategies within the Markov decision process framework , 1997, 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings.
[42] R. Sutton. Between MDPs and Semi-MDPs : Learning , Planning , and Representing Knowledge at Multiple Temporal Scales , 1998 .
[43] Doina Precup,et al. Between MOPs and Semi-MOP: Learning, Planning & Representing Knowledge at Multiple Temporal Scales , 1998 .
[44] J. Cassell,et al. More Than Just Another Pretty Face: Embodied Conversational Interface Agents , 1999 .
[45] Marilyn A. Walker,et al. Reinforcement Learning for Spoken Dialogue Systems , 1999, NIPS.
[46] Marilyn A. Walker,et al. An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email , 2000, J. Artif. Intell. Res..
[47] Marilyn A. Walker,et al. NJFun- A Reinforcement Learning Spoken Dialogue System , 2000 .
[48] Pedro M. Domingos,et al. Learning Repetitive Text-Editing Procedures with SMARTedit , 2001, Your Wish is My Command.
[49] Richard S. Sutton,et al. Predictive Representations of State , 2001, NIPS.
[50] Danqi Chen,et al. of the Association for Computational Linguistics: , 2001 .
[51] Jeff G. Schneider,et al. Autonomous helicopter control using reinforcement learning policy search methods , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).
[52] Jürgen Schmidhuber,et al. Model-based reinforcement learning for evolving soccer strategies , 2001 .
[53] Nikolaos G. Bourbakis,et al. An intelligent assistant for navigation of visually impaired people , 2001, Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001).
[54] Joelle Pineau,et al. Pearl: A Mobile Robotic Assistant for the Elderly , 2002 .
[55] Pierre-Yves Oudeyer,et al. Robotic clicker training , 2002, Robotics Auton. Syst..
[56] S. Singh,et al. Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System , 2011, J. Artif. Intell. Res..
[57] S. Shankar Sastry,et al. Autonomous Helicopter Flight via Reinforcement Learning , 2003, NIPS.
[58] Milind Tambe,et al. Adjustable Autonomy Challenges in Personal Assistant Agents: A Position Paper , 2003, Agents and Computational Autonomy.
[59] Joelle Pineau,et al. Towards robotic assistants in nursing homes: Challenges and results , 2003, Robotics Auton. Syst..
[60] Marilyn A. Walker,et al. Trainable Sentence Planning for Complex Information Presentations in Spoken Dialog Systems , 2004, ACL.
[61] Ben Tse,et al. Autonomous Inverted Helicopter Flight via Reinforcement Learning , 2004, ISER.
[62] J. Kocijan,et al. Gaussian process model based predictive control , 2004, Proceedings of the 2004 American Control Conference.
[63] Gökhan Tür,et al. Combining active and semi-supervised learning for spoken language understanding , 2005, Speech Commun..
[64] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[65] H. Cuayahuitl,et al. Human-computer dialogue simulation using hidden Markov models , 2005, IEEE Workshop on Automatic Speech Recognition and Understanding, 2005..
[66] Cynthia Breazeal,et al. Real-Time Interactive Reinforcement Learning for Robots , 2005 .
[67] Pieter Abbeel,et al. An Application of Reinforcement Learning to Aerobatic Helicopter Flight , 2006, NIPS.
[68] Andrea Lockerd Thomaz,et al. Reinforcement Learning with Human Teachers: Understanding How People Want to Teach Robots , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.
[69] Daniel C. Fain,et al. Sponsored search: A brief history , 2006 .
[70] Yuzhu Lu,et al. Augmented Reality E-Commerce Assistant System: Trying While Shopping , 2007, HCI.
[71] Hui Ye,et al. Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System , 2007, NAACL.
[72] Marilyn A. Walker,et al. Individual and Domain Adaptation in Sentence Planning for Dialogue , 2007, J. Artif. Intell. Res..
[73] Lihong Li,et al. An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning , 2008, ICML '08.
[74] Alborz Geramifard,et al. Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping , 2008, UAI.
[75] Suresh Manandhar,et al. Designing an interactive open-domain question answering system , 2009, Natural Language Engineering.
[76] W. Bruce Croft,et al. Search Engines - Information Retrieval in Practice , 2009 .
[77] Alan K. Mackworth,et al. Artificial Intelligence - Foundations of Computational Agents , 2010 .
[78] Panagiotis G. Ipeirotis,et al. Running Experiments on Amazon Mechanical Turk , 2010, Judgment and Decision Making.
[79] Gökhan Tür,et al. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 1 The CALO Meeting Assistant System , 2022 .
[80] Christopher C. Yang. Search Engines Information Retrieval in Practice , 2010, J. Assoc. Inf. Sci. Technol..
[81] L Poole David,et al. Artificial Intelligence: Foundations of Computational Agents , 2010 .
[82] Stephen Ades,et al. Voice Annotation and Editing in a Workstation Environment , 2010 .
[83] Farbod Fahimi,et al. Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.
[84] Carl E. Rasmussen,et al. PILCO: A Model-Based and Data-Efficient Approach to Policy Search , 2011, ICML.
[85] Mohammad Amin Bassiri. Interactional Feedback and the Impact of Attitude and Motivation on Noticing L2 Form , 2011 .
[86] Milica Gasic,et al. On-line policy optimisation of spoken dialogue systems via live interaction with human subjects , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.
[87] N. Daw,et al. The ubiquity of model-based reinforcement learning , 2012, Current Opinion in Neurobiology.
[88] Peter Stone,et al. Reinforcement learning from simultaneous human and MDP reward , 2012, AAMAS.
[89] Patrick M. Pilarski,et al. Between Instruction and Reward: Human-Prompted Switching , 2012, AAAI Fall Symposium: Robots Learning Interactively from Human Teachers.
[90] Yuval Tassa,et al. MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[91] Kiyoshi Yasuda,et al. Towards Assessing the Communication Responsiveness of People with Dementia , 2012, IVA.
[92] Dongho Kim,et al. POMDP-based dialogue manager adaptation to extended domains , 2013, SIGDIAL Conference.
[93] Dongho Kim,et al. On-line policy optimisation of Bayesian spoken dialogue systems via human interaction , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[94] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[95] Pierre Lison. Model-based Bayesian reinforcement learning for dialogue management , 2013, INTERSPEECH.
[96] Milica Gasic,et al. POMDP-Based Statistical Spoken Dialog Systems: A Review , 2013, Proceedings of the IEEE.
[97] Andrea Lockerd Thomaz,et al. Policy Shaping: Integrating Human Feedback with Reinforcement Learning , 2013, NIPS.
[98] David L. Roberts,et al. A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback , 2014, AAAI.
[99] Gökhan Tür,et al. Understanding Spoken Language , 2014, Computing Handbook, 3rd ed..
[100] Ryuichiro Higashinaka,et al. Towards an open-domain conversational system fully based on natural language processing , 2014, COLING.
[101] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[102] Sergey Levine,et al. Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics , 2014, NIPS.
[103] Alaa Hassan Mahmoud,et al. Speech To Text Conversion , 2014 .
[104] P. König,et al. Primary Visual Cortex Represents the Difference Between Past and Present , 2013, Cerebral cortex.
[105] Thomas B. Schön,et al. From Pixels to Torques: Policy Learning with Deep Dynamical Models , 2015, ICML 2015.
[106] Imed Zitouni,et al. Automatic Online Evaluation of Intelligent Assistants , 2015, WWW.
[107] Fabrice Lefèvre,et al. Reinforcement-learning based dialogue system for human-robot interactions with socially-inspired rewards , 2015, Comput. Speech Lang..
[108] Carl E. Rasmussen,et al. Gaussian Processes for Data-Efficient Learning in Robotics and Control , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[109] P. Pilarski. Prosthetic Devices as Goal-Seeking Agents , 2015 .
[110] Martin A. Riedmiller,et al. Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images , 2015, NIPS.
[111] Shimon Whiteson,et al. Learning to Communicate with Deep Multi-Agent Reinforcement Learning , 2016, NIPS.
[112] Dilek Z. Hakkani-Tür,et al. Interactive reinforcement learning for task-oriented dialogue management , 2016 .
[113] Jianfeng Gao,et al. Deep Reinforcement Learning for Dialogue Generation , 2016, EMNLP.
[114] Gabriel Skantze,et al. Real-Time Coordination in Human-Robot Interaction Using Face and Voice , 2017, AI Mag..
[115] Marc'Aurelio Ranzato,et al. Sequence Level Training with Recurrent Neural Networks , 2015, ICLR.
[116] Tom Schaul,et al. Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.
[117] Rob Fergus,et al. Learning Multiagent Communication with Backpropagation , 2016, NIPS.
[118] Patrick M. Pilarski,et al. Face valuing: Training user interfaces with facial expressions and reinforcement learning , 2016, ArXiv.
[119] Jianfeng Gao,et al. Deep Reinforcement Learning with a Natural Language Action Space , 2015, ACL.
[120] Alexander I. Rudnicky,et al. An Intelligent Assistant for High-Level Task Understanding , 2016, IUI.
[121] Jianfeng Gao,et al. A User Simulator for Task-Completion Dialogues , 2016, ArXiv.
[122] Sergey Levine,et al. End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..
[123] Yelong Shen,et al. ReasoNet: Learning to Stop Reading in Machine Comprehension , 2016, CoCo@NIPS.
[124] Patrick M. Pilarski,et al. Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning , 2016, ArXiv.
[125] Angeliki Lazaridou,et al. Towards Multi-Agent Communication-Based Language Learning , 2016, ArXiv.
[126] Maxine Eskénazi,et al. Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning , 2016, SIGDIAL Conference.
[127] David Vandyke,et al. A Network-based End-to-End Trainable Task-oriented Dialogue System , 2016, EACL.
[128] David Vandyke,et al. Dialogue manager domain adaptation using Gaussian process reinforcement learning , 2016, Comput. Speech Lang..
[129] Jason Weston,et al. Learning through Dialogue Interactions by Asking Questions , 2016, ICLR.
[130] Lantao Yu,et al. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.
[131] Li Zhou,et al. End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient , 2017, ArXiv.
[132] Gökhan Tür,et al. Towards Zero-Shot Frame Semantic Parsing for Domain Scaling , 2017, INTERSPEECH.
[133] Tom Schaul,et al. The Predictron: End-To-End Learning and Planning , 2016, ICML.
[134] Jason Weston,et al. Dialogue Learning With Human-In-The-Loop , 2016, ICLR.
[135] Learning Robust Dialog Policies in Noisy Environments , 2017, ArXiv.
[136] Chris Sauer,et al. Beating Atari with Natural Language Guided Reinforcement Learning , 2017, ArXiv.
[137] Stefan Ultes,et al. Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management , 2017, SIGDIAL Conference.
[138] Joelle Pineau,et al. A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues , 2016, AAAI.
[139] Joelle Pineau,et al. A Deep Reinforcement Learning Chatbot , 2017, ArXiv.
[140] Jianfeng Gao,et al. End-to-End Task-Completion Neural Dialogue Systems , 2017, IJCNLP.
[141] Yann Dauphin,et al. Deal or No Deal? End-to-End Learning of Negotiation Dialogues , 2017, EMNLP.
[142] Stefan Ultes,et al. Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning , 2017, SIGDIAL Conference.
[143] Andreas Krause,et al. Safe Model-based Reinforcement Learning with Stability Guarantees , 2017, NIPS.
[144] Bing Liu,et al. Iterative policy learning in end-to-end trainable task-oriented neural dialog models , 2017, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).
[145] Pieter Abbeel,et al. Autonomous Helicopter Flight Using Reinforcement Learning , 2010, Encyclopedia of Machine Learning.
[146] Geoffrey Zweig,et al. Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning , 2017, ACL.
[147] Dilek Z. Hakkani-Tür,et al. Scalable multi-domain dialogue state tracking , 2017, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).
[148] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[149] Kam-Fai Wong,et al. Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning , 2017, EMNLP.
[150] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[151] Seunghak Yu,et al. Scaling up deep reinforcement learning for multi-domain dialogue systems , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[152] Bing Liu,et al. End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning , 2017, ArXiv.
[153] Patrick M. Pilarski,et al. Communicative Capital for Prosthetic Agents , 2017, ArXiv.
[154] Bing Liu,et al. Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding , 2017, ArXiv.
[155] Jason Weston,et al. Learning End-to-End Goal-Oriented Dialog , 2016, ICLR.
[156] Jianfeng Gao,et al. Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access , 2016, ACL.
[157] Yu Wu,et al. Towards Explainable and Controllable Open Domain Dialogue Generation with Dialogue Acts , 2018, ArXiv.
[158] Marc Peter Deisenroth,et al. Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control , 2017, AISTATS.
[159] Jianfeng Gao,et al. Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning , 2018, EMNLP.
[160] Kallirroi Georgila,et al. Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task , 2018, SIGDIAL Conference.
[161] Zhou Yu,et al. Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog , 2018, SIGDIAL Conference.
[162] Sergey Levine,et al. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models , 2018, NeurIPS.
[163] Pieter Abbeel,et al. Emergence of Grounded Compositional Language in Multi-Agent Populations , 2017, AAAI.
[164] Zhi Chen,et al. Policy Adaptation for Deep Reinforcement Learning-Based Dialogue Management , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[165] Chong Wang,et al. Subgoal Discovery for Hierarchical Dialogue Policy Learning , 2018, EMNLP.
[166] Xuanjing Huang,et al. Toward Diverse Text Generation with Inverse Reinforcement Learning , 2018, IJCAI.
[167] Bing Liu,et al. Incorporating the Structure of the Belief State in End-to-End Task-Oriented Dialogue Systems , 2018 .
[168] Erik Talvitie,et al. The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces , 2018, ArXiv.
[169] Kam-Fai Wong,et al. Integrating planning for task-completion dialogue policy learning , 2018, ACL.
[170] Jianfeng Gao,et al. BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems , 2016, AAAI.
[171] Matthew B Hoy. Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants , 2018, Medical reference services quarterly.
[172] Mike Lewis,et al. Hierarchical Text Generation and Planning for Strategic Dialogue , 2017, ICML.
[173] Gökhan Tür,et al. Building a Conversational Agent Overnight with Dialogue Self-Play , 2018, ArXiv.
[174] Pei-Hao Su,et al. Sample Efficient Deep Reinforcement Learning for Dialogue Systems With Large Action Spaces , 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[175] Dilek Z. Hakkani-Tür,et al. Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems , 2018, NAACL.
[176] Fabio Viola,et al. Learning and Querying Fast Generative Models for Reinforcement Learning , 2018, ArXiv.
[177] Demis Hassabis,et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.
[178] Jürgen Schmidhuber,et al. World Models , 2018, ArXiv.
[179] Prasoon Goyal,et al. Using Natural Language for Reward Shaping in Reinforcement Learning , 2019, IJCAI.
[180] Jason Weston,et al. ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons , 2019, ArXiv.
[181] Peng Zhang,et al. CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots , 2019, EMNLP/IJCNLP.
[182] Natasha Jaques,et al. Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog , 2019, ArXiv.
[183] Xiaodong Gu,et al. DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder , 2018, ICLR.
[184] Yiming Yang,et al. Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning , 2018, AAAI.
[185] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[186] Mingyang Zhou,et al. Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation , 2019, EMNLP/IJCNLP.
[187] Pascale Fung,et al. HappyBot: Generating Empathetic Dialogue Responses by Improving User Experience Look-ahead , 2019, ArXiv.
[188] Song Liu,et al. Personalized Dialogue Generation with Diversified Traits , 2019, ArXiv.
[189] Lihong Li,et al. Neural Approaches to Conversational AI , 2019, Found. Trends Inf. Retr..
[190] Luke Metz,et al. Learning to Predict Without Looking Ahead: World Models Without Forward Prediction , 2019, NeurIPS.
[191] L. Lastras,et al. Doc2Dial: a Framework for Dialogue Composition Grounded in Business Documents , 2019 .
[192] Jorge Armando Mendez Mendez,et al. Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings , 2022, ArXiv.
[193] Zhuoxuan Jiang,et al. Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability , 2019, SIGdial.
[194] Ruben Villegas,et al. Learning Latent Dynamics for Planning from Pixels , 2018, ICML.
[195] Pascale Fung,et al. Attention over Parameters for Dialogue Systems , 2020, ArXiv.
[196] Sergey Levine,et al. Model-Based Reinforcement Learning for Atari , 2019, ICLR.
[197] Rosalind W. Picard,et al. Hierarchical Reinforcement Learning for Open-Domain Dialog , 2019, AAAI.
[198] Zheng Zhang,et al. Recent advances and challenges in task-oriented dialog systems , 2020, Science China Technological Sciences.
[199] Demis Hassabis,et al. Mastering Atari, Go, chess and shogi by planning with a learned model , 2019, Nature.
[200] Michael Bowling,et al. Sample-Efficient Model-based Actor-Critic for an Interactive Dialogue Task , 2020, ArXiv.
[201] Kee-Eung Kim,et al. Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues , 2020, AAAI.
[202] Harry Shum,et al. The Design and Implementation of XiaoIce, an Empathetic Social Chatbot , 2018, CL.
[203] Mary Williamson,et al. Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills , 2020, ACL.
[204] Jianfeng Gao,et al. Challenges in Building Intelligent Open-domain Dialog Systems , 2019, ACM Trans. Inf. Syst..
[205] Rui Zhang,et al. Dynamic Reward-Based Dueling Deep Dyna-Q: Robust Policy Learning in Noisy Environments , 2020, AAAI.
[206] Gokhan Tur,et al. Plato Dialogue System: A Flexible Conversational AI Research Platform , 2020, ArXiv.
[207] Guangxu Xun,et al. HSCJN: A Holistic Semantic Constraint Joint Network for Diverse Response Generation , 2019, Comput. Speech Lang..