research-article

Experience-based Causality Learning for Intelligent Agents

Authors:

Chengqing ZongAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 18, Issue 4

Article No.: 45, Pages 1 - 22

https://doi.org/10.1145/3314943

Published: 21 May 2019 Publication History

Abstract

Understanding causality in text is crucial for intelligent agents. In this article, inspired by human causality learning, we propose an experience-based causality learning framework. Comparing to traditional approaches, which attempt to handle the causality problem relying on textual clues and linguistic resources, we are the first to use experience information for causality learning. Specifically, we first construct various scenarios for intelligent agents, thus, the agents can gain experience from interaction in these scenarios. Then, human participants build a number of training instances for agents of causality learning based on these scenarios. Each instance contains two sentences and a label. Each sentence describes an event that an agent experienced in a scenario, and the label indicates whether the sentence (event) pair has a causal relation. Accordingly, we propose a model that can infer the causality in text using experience by accessing the corresponding event information based on the input sentence pair. Experiment results show that our method can achieve impressive performance on the grounded causality corpus and significantly outperform the conventional approaches. Our work suggests that experience is very important for intelligent agents to understand causality.

References

[1]

Sam Adams, Itmar Arel, Joscha Bach, Robert Coop, Rod Furlan, Ben Goertzel, J. Storrs Hall, Alexei Samsonovich, Matthias Scheutz, Matthew Schlesinger, et al. 2012. Mapping the landscape of human-level artificial general intelligence. AI Mag. 33, 1 (2012), 25--42.

Digital Library

[2]

Yoshua Bengio, Hugo Larochelle, Pascal Lamblin, Dan Popovici, Aaron Courville, Clarence Simard, Jerome Louradour, and Dumitru Erhan. 2007. Deep architectures for baby AI. http://www.cs.toronto.edu/~amnih/cifar/talks/bengio_tutorial.pdf.

[3]

Paul van den Broek and Anne Helder. 2017. Cognitive processes in discourse comprehension: Passive processes, reader-initiated processes, and evolving mental representations. Discourse Process. 54, 5--6 (2017), 360--372.

[4]

Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, and Ruslan Salakhutdinov. 2018. Gated-attention architectures for task-oriented language grounding. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.

[5]

Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, and Yoshua Bengio. 2018. BabyAI: First steps towards grounded language learning with a human in the loop. arXiv preprint arXiv:1810.08272.

[6]

Elizabeth Couper-Kuhlen and Bernd Kortmann. 2009. Cause-Condition-Concession-Contrast: Cognitive and Discourse Perspectives, vol. 33. Walter de Gruyter.

[7]

Martin Curd and Stathis Psillos. 2013. The Routledge Companion to Philosophy of Science. Routledge.

[8]

Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. 2018. Embodied question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2054--2063.

[9]

Quang Xuan Do, Yee Seng Chan, and Dan Roth. 2011. Minimally supervised event causality identification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 294--303.

Digital Library

[10]

John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, Jul (2011), 2121--2159.

Digital Library

[11]

Qiaozi Gao, Malcolm Doering, Shaohua Yang, and Joyce Chai. 2016. Physical causality of action verbs in grounded language understanding. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1. 1814--1824.

[12]

Raymond W. Gibbs Jr. 2003. Embodied experience and linguistic meaning. Brain Lang. 84, 1 (2003), 1--15.

[13]

Roxana Girju. 2003. Automatic detection of causal relations for question answering. In Proceedings of the ACL Workshop on Multilingual Summarization and Question Answering. Association for Computational Linguistics, 76--83.

Digital Library

[14]

Alison Gopnik, Clark Glymour, David M. Sobel, Laura E. Schulz, Tamar Kushnir, and David Danks. 2004. A theory of causal learning in children: Causal maps and Bayes nets. Psychol. Rev. 111, 1 (2004), 3.

[15]

Alison Gopnik, David M. Sobel, Laura E. Schulz, and Clark Glymour. 2001. Causal learning mechanisms in very young children: Two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation. Dev. Psychol. 37, 5 (2001), 620.

[16]

Stevan Harnad. 1990. The symbol grounding problem. Physica D: Nonlin. Phenom. 42, 1–3 (1990), 335--346.

Digital Library

[17]

Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojtek Czarnecki, Max Jaderberg, Denis Teplyashin, et al. 2017. Grounded language learning in a simulated 3D world. arXiv preprint arXiv:1706.06551.

[18]

Christopher Hidey and Kathy McKeown. 2016. Identifying causal relations using parallel Wikipedia articles. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1. 1424--1433.

[19]

Felix Hill, Karl Moritz Hermann, Phil Blunsom, and Stephen Clark. 2017. Understanding grounded language learning agents {J}. arXiv preprint arXiv:1710.09867.

[20]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735--1780.

Digital Library

[21]

Jet Hoek, Sandrine Zufferey, Jacqueline Evers-Vermeul, and Ted J. M. Sanders. 2017. Cognitive complexity and the linguistic marking of coherence relations: A parallel corpus study. J. Pragmat. 121 (2017), 113--131.

[22]

Eduard H. Hovy. 1990. Parsimonious and profligate approaches to the question of discourse structure relations. In Proceedings of the 5th International Workshop on Natural Language Generation.

[23]

David Hume. 2016. An enquiry concerning human understanding. In Seven Masterpieces of Philosophy. Routledge, 191--284.

[24]

Yangfeng Ji and Jacob Eisenstein. 2015. One vector is not enough: Entity-augmented distributed semantics for discourse relations. Trans. Assoc. Comput.Linguist. 3 (2015), 329--344.

[25]

Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell. 2016. The Malmo platform for artificial intelligence experimentation. In Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI’16). 4246--4247.

Digital Library

[26]

Xiaomian Kang, Haoran Li, Long Zhou, Jiajun Zhang, and Chengqing Zong. 2016. An end-to-end Chinese discourse parser with adaptation to explicit and non-explicit relation recognition. In Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL’16 Shared Task).

[27]

Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski. 2016. Vizdoom: A doom-based ai research platform for visual reinforcement learning. In Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG’16). IEEE, 1--8.

[28]

Douwe Kiela, Luana Bulat, Anita L. Vero, and Stephen Clark. 2016. Virtual embodiment: A scalable long-term strategy for artificial intelligence research. arXiv preprint arXiv:1610.07432.

[29]

Walter Kintsch. 1988. The role of knowledge in discourse comprehension: A construction-integration model. Psychol. Rev. 95, 2 (1988), 163.

[30]

Simon Kirby, Tom Griffiths, and Kenny Smith. 2014. Iterated learning and the evolution of language. Curr. Opin. Neurobiol. 28 (2014), 108--114.

[31]

Alistair Knott and Ted Sanders. 1998. The classification of coherence relations and their linguistic markers: An exploration of two languages. J. Pragmat. 30, 2 (1998), 135--175.

[32]

Satwik Kottur, José Moura, Stefan Lee, and Dhruv Batra. 2017. Natural language does not emerge “naturally” in multi-agent dialog. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’17).

[33]

Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman. 2017. Building machines that learn and think like people. Behav. Brain Sci. 40 (2017).

[34]

Alex Lascarides and Nicholas Asher. 1993. Temporal interpretation, discourse relations and commonsense entailment. Linguist. Philos. 16, 5 (1993), 437--493.

[35]

Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni. 2016. Multi-agent cooperation and the emergence of (natural) language. arXiv preprint arXiv:1612.07182.

[36]

Haoran Li, Jiajun Zhang, and Chengqing Zong. 2017. Implicit discourse relation recognition for English and Chinese with multiview modeling and effective representation learning. ACM Trans. Asian Low-Resour. Lang. Info. Process. 16, 3 (2017), 19.

Digital Library

[37]

Ziheng Lin, Min-Yen Kan, and Hwee Tou Ng. 2009. Recognizing implicit discourse relations in the penn discourse treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 343--351.

Digital Library

[38]

Zhiyi Luo, Yuchen Sha, Kenny Q. Zhu, Seung Won Hwang, and Zhongyuan Wang. 2016. Commonsense causal reasoning between short texts. In Proceedings of the 15th International Conference on Principles of Knowledge Representation and Reasoning. 421--430.

Digital Library

[39]

Minh-Thang Luong, Eugene Brevdo, and Rui Zhao. 2017. Neural machine translation (seq2seq) tutorial. Retrieved from https://github.com/tensorflow/nmt.

[40]

William C. Mann and Sandra A. Thompson. 1986. Relational propositions in discourse. Discourse Process. 9, 1 (1986), 57--90.

[41]

William C. Mann and Sandra A. Thompson. 1988. Rhetorical structure theory: Toward a functional theory of text organization. Text-Interdisc. J. Study Discourse 8, 3 (1988), 243--281.

[42]

Eugenio Martínez-Cámara, Vered Shwartz, Iryna Gurevych, and Ido Dagan. 2017. Neural disambiguation of causal lexical markers based on context. In Proceedings of the 12th International Conference on Computational Semantics (IWCS’17).

[43]

Todor Mihaylov and Anette Frank. 2016. Discourse relation sense classification using cross-argument semantic similarity based on word embeddings. In Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL’16 Shared Task). 100--107.

[44]

Tomas Mikolov, Armand Joulin, and Marco Baroni. 2016. A roadmap towards machine intelligence. In Proceedings of the International Conference on Intelligent Text Processing and Computational Linguistics. Springer, 29--61.

[45]

Eleni Miltsakaki, Rashmi Prasad, Aravind K. Joshi, and Bonnie L. Webber. 2004. The Penn Discourse Treebank. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’04).

[46]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, et al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529--533.

[47]

Gerben Mulder. 2008. Understanding Causal Coherence Relations. Vol. 172. LOT.

[48]

Steve Nebel, Sascha Schneider, and Gunter Daniel Rey. 2016. Mining learning and crafting scientific experiments: A literature review on the use of Minecraft in education and research. J. Edu. Technol. Soc. 19, 2 (2016), 355--366.

[49]

Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, and Honglak Lee. 2016. Control of memory, active perception, and action in Minecraft. arXiv preprint arXiv:1605.09128.

Digital Library

[50]

Junhyuk Oh, Satinder Singh, Honglak Lee, and Pushmeet Kohli. 2017. Zero-shot task generalization with multi-task deep reinforcement learning. In Proceedings of the 34th International Conference on Machine Learning. JMLR.org, 2661--2670.

Digital Library

[51]

K. Papineni, S. Roukos, T. Ward, and W. J. Zhu. 2002. IBM research report bleu: A method for automatic evaluation of machine translation. In Proceedings of Annual Meeting of the Association for Computational Linguistics. 311--318.

Digital Library

[52]

Jean Piaget. 1970. Piaget’s theory. Piaget and His School. Springer, Berlin, 11--23.

[53]

Emily Pitler, Mridhula Raghupathy, Hena Mehta, Ani Nenkova, Alan Lee, and Aravind K. Joshi. 2008. Easily identifiable discourse relations. In Proceedings of the International Conference on Computational Linguistics (COLING’08). 87--90.

[54]

Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Miltsakaki, Livio Robaldo, Aravind K. Joshi, and Bonnie L. Webber. 2008. The Penn Discourse Treebank 2.0. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’08). 2961--2968.

[55]

Mehwish Riaz and Roxana Girju. 2010. Another look at causality: Discovering scenario-specific contingency relationships with no supervision. In Proceedings of the IEEE 4th International Conference on Semantic Computing (ICSC’10). IEEE, 361--368.

Digital Library

[56]

Mehwish Riaz and Roxana Girju. 2013. Toward a better understanding of causality between verbal events: Extraction and analysis of the causal power of verb-verb associations. In Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL’13). 21--30.

[57]

Mehwish Riaz and Roxana Girju. 2014. In-depth exploitation of noun and verb semantics to identify causation in verb-noun pairs. In Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL’14). 161--170.

[58]

Hannah Rohde, Alexander Johnson, Nathan Schneider, and Bonnie Webber. 2018. Discourse coherence: Concurrent explicit and implicit relations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1. 2257--2267.

[59]

Attapol Rutherford, Vera Demberg, and Nianwen Xue. 2017. A systematic study of neural discourse models for implicit discourse relation. In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL’17), vol. 1. 281--291.

[60]

Attapol Rutherford and Nianwen Xue. 2016. Robust non-explicit neural discourse parser in English and Chinese. In Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL’16 Shared Task). 55--59.

[61]

Ted Sanders. 2005. Coherence, causality and cognitive complexity in discourse. In Proceedings of the 1st International Symposium on the Exploration and Modelling of Meaning (ACTS/SEM’05). University of Toulouse-le-Mirail Toulouse, 105--114.

[62]

Ted J. M. Sanders and Leo G. M. Noordman. 2000. The role of coherence relations and their linguistic markers in text processing. Discourse Process. 29, 1 (2000), 37--60.

[63]

Ted J. M. Sanders, Wilbert P. M. Spooren, and Leo G. M. Noordman. 1993. Coherence Relations in a Cognitive Theory of Discourse Representation. De Gruyter, Berlin.

[64]

Maria Moffat Frederick Schifter, Catherine C. Cipollone. 2013. Piaget, inhelder. and “Minecraft.” Int. Assoc. Dev. Info. Soc. (2013).

[65]

David M. Sobel and Natasha Z. Kirkham. 2006. Blickets and babies: The development of causal reasoning in toddlers and infants. Dev. Psychol. 42, 6 (2006), 1103.

[66]

Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith Chintala, and Rob Fergus. 2015. Mazebase: A sandbox for learning from games. arXiv preprint arXiv:1511.07401.

[67]

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems. 3104--3112.

Digital Library

[68]

Francisco J. Varela, Evan Thompson, and Eleanor Rosch. 2017. The Embodied Mind: Cognitive Science and Human Experience. MIT Press.

[69]

Georg Henrik Von Wright. 2004. Explanation and Understanding. Cornell University Press.

[70]

Gregor Weiss and Marko Bajec. 2016. Discourse sense classification from scratch using focused RNNs. In Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL’16 Shared Task). 50--54.

[71]

Peter A. White. 1988. Causal processing: Origins and development. Psychol. Bull. 104, 1 (1988), 36.

[72]

Terry Winograd. 1971. Procedures as a representation for data in a computer program for understanding natural language. No. MAC-TR-84. MASSACHUSETTS INST OF TECH CAMBRIDGE PROJECT MAC, 1971.

[73]

Terry Winograd. 1972. Understanding natural language. Cogn. Psychol. 3, 1 (1972), 1--191.

[74]

Yi Wu, Yuxin Wu, Georgia Gkioxari, and Yuandong Tian. 2018. Building generalizable agents with a realistic and rich 3D environment. arXiv preprint arXiv:1801.02209.

[75]

Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, Rashmi Prasad, Christopher Bryant, and Attapol Rutherford. 2015. The CoNLL 2015 shared task on shallow discourse parsing. In Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL’15). 1--16.

[76]

Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, Attapol Rutherford, Bonnie Webber, Chuan Wang, and Hongmin Wang. 2016. CoNLL 2016 shared task on multilingual shallow discourse parsing. In Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL’16 Shared Task). 1--19.

[77]

Haonan Yu, Haichao Zhang, and Wei Xu. 2018. Interactive grounded language acquisition and generalization in a 2D world. arXiv preprint arXiv:1802.01433.

[78]

Qinghua Zhang and Albert Benveniste. 1992. Wavelet networks. IEEE Trans. Neural Netw. 3, 6 (1992), 889--898.

Digital Library

[79]

Yuping Zhou and Nianwen Xue. 2015. The Chinese discourse treebank: A Chinese corpus annotated with discourse relations. Lang. Resour. Eval. 49, 2 (2015), 397--431.

Digital Library

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Index Terms

Experience-based Causality Learning for Intelligent Agents
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Discourse, dialogue and pragmatics

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 18, Issue 4

December 2019

305 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3327969

Editor:
Nianwen Xue
Brandeis University, Waltham, USA

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Publication History

Published: 21 May 2019

Accepted: 01 February 2019

Revised: 01 November 2018

Received: 01 July 2018

Published in TALLIP Volume 18, Issue 4

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Xu ZOuyang LLiu YZhang L(2024)Modular Method for Embodied Instruction Following with Environmental Context Adaptation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651083(1-8)Online publication date: 30-Jun-2024
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https://doi.org/10.1016/j.ipm.2023.103616
Ranaldi LRanaldi FFallucchi FZanzotto F(2022)Shedding Light on the Dark Web: Authorship Attribution in Radical ForumsInformation10.3390/info1309043513:9(435)Online publication date: 14-Sep-2022
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Arzate Cruz CIgarashi T(2021)Interactive Reinforcement Learning for Autonomous Behavior DesignArtificial Intelligence for Human Computer Interaction: A Modern Approach10.1007/978-3-030-82681-9_11(345-375)Online publication date: 5-Nov-2021
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Du JZhao HYu YHu Q(2020)A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident ReportsComputational Intelligence and Neuroscience10.1155/2020/71320722020Online publication date: 1-Jan-2020
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