{"type":"ScholarlyArticle","title":"Abstraction for Deep Reinforcement Learning","authors":[{"name":"Murray Shanahan","slug":"murray-shanahan","given":"Murray","family":"Shanahan","affiliation":"Imperial College London"},{"name":"Melanie Mitchell","slug":"melanie-mitchell","given":"Melanie","family":"Mitchell","affiliation":"Santa Fe Institute"}],"abstract":"We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might be brought to bear on this issue, but they present difficulties because of the need for end-to-end differentiability. We review developments in AI and machine learning that could facilitate their adoption.","doi":"10.24963/ijcai.2022/780","url":"https://ijcai.org/proceedings/2022/780","pdf":"https://ijcai.org/proceedings/2022/0780.pdf","markdown":"https://ijcai.org/proceedings/2022/780.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/780","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Survey Track","track":"Survey Track","number":780,"pages":{"first":5588,"last":5596},"keywords":["Survey Track: Machine Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987284","length_sec":67,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985427","length_sec":553,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Andreas et al., 2018] Jacob Andreas, Dan Klein, and Sergey Levine. Learning with latent language. In Pro- ceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2166-2179, 2018.","title":"Learning with latent language","authors":"Jacob Andreas, Dan Klein, Sergey Levine","year":"2018","venue":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Baars, 2013] Bernard J. Baars. A global workspace theory of conscious experience. In Consciousness in philosophy and cognitive neuroscience, pages 161-184. Psychology Press, 2013.","title":"A global workspace theory of conscious experience","authors":"J Bernard, Baars","year":"2013","venue":"Consciousness in philosophy and cognitive neuroscience","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Banino et al., 2022] Andrea Banino, Adria Puigdomenech Badia, Jacob C Walker, Tim Scholtes, Jovana Mitrovic, and Charles Blundell. CoBERL: Contrastive BERT for reinforcement learning. In International Conference on Learning Representations, 2022.","title":"CoBERL: Contrastive BERT for reinforcement learning","authors":"Andrea Banino, Adria Puigdomenech Badia, Jacob C Walker, Tim Scholtes, Jovana Mitrovic, Charles Blundell","year":"2022","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Barrett et al., 2018] D. G. T. Barrett, F. Hill, A. Santoro, A. S. Morcos, and T. Lillicrap. Measuring abstract rea- soning in neural networks. In Proceedings of the Inter- national Conference on Machine Learning, pages 4477- 4486, 2018.","title":"Measuring abstract reasoning in neural networks","authors":"D G T Barrett, F Hill, A Santoro, A S Morcos, T Lillicrap","year":"2018","venue":"Proceedings of the International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Brown et al., 2020] Tom Brown, Benjamin Mann, Nick Ry- der, Melanie Subbiah, Jared D Kaplan, et al. Language models are few-shot learners. In Advances in Neural Infor- mation Processing Systems, volume 33, pages 1877-1901, 2020.","title":"Language models are few-shot learners","authors":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Burgess et al., 2019] Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner. MONet: Un- supervised scene decomposition and representation. arXiv:1901.11390, 2019.","title":"MONet: Unsupervised scene decomposition and representation","authors":"Loic Christopher P Burgess, Nicholas Matthey, Rishabh Watters, Irina Kabra, Matt Higgins, Alexander Botvinick, Lerchner","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2020] Ting Chen, Simon Kornblith, Moham- mad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In Inter- national Conference on Machine Learning (ICML), pages 1597-1607, 2020.","title":"A simple framework for contrastive learning of visual representations","authors":"Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton","year":"2020","venue":"International Conference on Machine Learning (ICML)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Creswell et al., 2021] Antonia Creswell, Rishabh Kabra, Christopher P. Burgess, and Murray Shanahan. Unsuper- vised object-based transition models for 3D partially ob- servable environments. In Advances in Neural Information Processing Systems, 2021.","title":"Unsupervised object-based transition models for 3D partially observable environments","authors":"Antonia Creswell, Rishabh Kabra, Christopher P Burgess, Murray Shanahan","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Crosby et al., 2020] Matthew Crosby, Benjamin Beyret, Murray Shanahan, José Hernández-Orallo, Lucy Cheke, and Marta Halina. The Animal-AI testbed and competi- tion. In Proceedings of the NeurIPS 2019 Competition and Demonstration Track, volume 123, pages 164-176, 2020.","title":"The Animal-AI testbed and competition","authors":"Matthew Crosby, Benjamin Beyret, Murray Shanahan, José Hernández-Orallo, Lucy Cheke, Marta Halina","year":"2020","venue":"Proceedings of the NeurIPS 2019 Competition and Demonstration Track","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Crouse et al., 2021] Maxwell Crouse, Constantine Nakos, Ibrahim Abdelaziz, and Ken Forbus. Neural analogical matching. In Proceedings of the AAAI Conference on Ar- tificial Intelligence, volume 35, pages 809-817, 2021.","title":"Neural analogical matching","authors":"Maxwell Crouse, Constantine Nakos, Ibrahim Abdelaziz, Ken Forbus","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Demircigil et al., 2017] Mete Demircigil, Judith Heusel, Matthias Löwe, Sven Upgang, and Franck Vermet. On a model of associative memory with huge storage capacity. Journal of Statistical Physics, 168(2):288-299, 2017.","title":"On a model of associative memory with huge storage capacity","authors":"Mete Demircigil, Judith Heusel, Matthias Löwe, Sven Upgang, Franck Vermet","year":"2017","venue":"Journal of Statistical Physics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Doumas et al., 2008] Leonidas A.A. Doumas, John E. Hummel, and Catherine M. Sandhofer. A theory of the discovery and predication of relational concepts. Psycho- logical review, 115(1):1, 2008.","title":"A theory of the discovery and predication of relational concepts","authors":"A A Leonidas, John E Doumas, Catherine M Hummel, Sandhofer","year":"2008","venue":"Psychological review","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Dubey et al., 2018] Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, and Alexei Efros. Investigating hu- man priors for playing video games. In Proceedings of the 35th International Conference on Machine Learning, pages 1349-1357, 2018.","title":"Investigating human priors for playing video games","authors":"Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei Efros","year":"2018","venue":"Proceedings of the 35th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ellis et al., 2020] K. Ellis, C. Wong, M. Nye, M. Sablé- Meyer, L. Cary, L. Morales, L. Hewitt, A. Solar-Lezama, and J. B. Tenenbaum. DreamCoder: Growing generaliz- able, interpretable knowledge with wake-sleep Bayesian program learning. arXiv:2006.08381, 2020.","title":"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning","authors":"K Ellis, C Wong, M Nye, M Sablé-Meyer, L Cary, L Morales, L Hewitt, A Solar-Lezama, J B Tenenbaum","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Engelcke et al., 2020] Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner. Genesis: Gen- erative scene inference and sampling with object-centric latent representations. In International Conference on Learning Representations, 2020.","title":"Genesis: Generative scene inference and sampling with object-centric latent representations","authors":"Martin Engelcke, Oiwi Parker Adam R Kosiorek, Ingmar Jones, Posner","year":"2020","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Evans et al., 2021] R. Evans, J. Hernández-Orallo, J. Welbl, P. Kohli, and M. Sergot. Making sense of sensory input. Artificial Intelligence, 293:103438, 2021.","title":"Making sense of sensory input","authors":"R Evans, J Hernández-Orallo, J Welbl, P Kohli, M Sergot","year":"2021","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Falkenhainer et al., 1989] Brian Falkenhainer, Kenneth D Forbus, and Dedre Gentner. The structure-mapping en- gine: Algorithm and examples. Artificial intelligence, 41(1):1-63, 1989.","title":"The structure-mapping engine: Algorithm and examples","authors":"Brian Falkenhainer, Kenneth D Forbus, Dedre Gentner","year":"1989","venue":"Artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Garnelo and Shanahan, 2019] Marta Garnelo and Murray Shanahan. Reconciling deep learning with symbolic artifi- cial intelligence: representing objects and relations. Cur- rent Opinion in Behavioral Sciences, 29:17-23, 2019.","title":"Reconciling deep learning with symbolic artificial intelligence: representing objects and relations","authors":"Shanahan Garnelo, Marta Garnelo, Murray Shanahan","year":"2019","venue":"Current Opinion in Behavioral Sciences","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Garnelo et al., 2016] Marta Garnelo, Kai Arulkumaran, and Murray Shanahan. Towards deep symbolic reinforcement learning. arXiv: 1609.05518, 2016.","title":"Towards deep symbolic reinforcement learning","authors":"Marta Garnelo, Kai Arulkumaran, Murray Shanahan","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Gentner, 1983] Dedre Gentner. Structure-mapping: A theoretical framework for analogy. Cognitive science, 7(2):155-170, 1983.","title":"Structure-mapping: A theoretical framework for analogy","authors":"Dedre Gentner, Gentner","year":"1983","venue":"Cognitive science","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Goodfellow et al., 2014] Ian J. Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, et al. Generative adversarial nets. In Advances in Neural Infor- mation Processing Systems, volume 28, pages 2672-2680, 2014.","title":"Generative adversarial nets","authors":"Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley","year":"2014","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Goyal et al., 2022] Anirudh Goyal, Aniket Rajiv Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, et al. Coordination among neural modules through a shared global workspace. In International Conference on Learn- ing Representations, 2022.","title":"Coordination among neural modules through a shared global workspace","authors":"Anirudh Goyal, Aniket Rajiv Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke","year":"2022","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Greff et al., 2019] Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, et al. Multi-object representation learning with iter- ative variational inference. In Proceedings of the 36th In- ternational Conference on Machine Learning, volume 97, pages 2424-2433, 2019.","title":"Multi-object representation learning with iterative variational inference","authors":"Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran","year":"2019","venue":"Proceedings of the 36th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Greff et al., 2020] Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber. On the binding problem in artificial neural networks. arXiv:2012.05208, 2020.","title":"On the binding problem in artificial neural networks","authors":"Greff","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Gregor et al., 2019] Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, and Theophane Weber. Temporal difference variational auto-encoder. In Interna- tional Conference on Learning Representations, 2019.","title":"Temporal difference variational auto-encoder","authors":"Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber","year":"2019","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Grill et al., 2020] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, et al. Boot- strap your own latent -a new approach to self-supervised learning. In Advances in Neural Information Processing Systems, volume 33, pages 21271-21284, 2020.","title":"Bootstrap your own latent -a new approach to self-supervised learning","authors":"Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Guo et al., 2020] Zhaohan Daniel Guo, Bernardo Avila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, et al. Bootstrap latent-predictive representations for multitask reinforcement learning. In Proceedings of the 37th Inter- national Conference on Machine Learning, volume 119, pages 3875-3886, 2020.","title":"Bootstrap latent-predictive representations for multitask reinforcement learning","authors":"Guo","year":"2020","venue":"Proceedings of the 37th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ha and Schmidhuber, 2018] David Ha and Jürgen Schmid- huber. Recurrent world models facilitate policy evolution. In Advances in Neural Information Processing Systems, volume 31, pages 2455-2467, 2018.","title":"Recurrent world models facilitate policy evolution","authors":"Schmidhuber Ha, David Ha, Jürgen Schmidhuber","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Higgins et al., 2016] Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, et al. beta-VAE: Learning basic visual concepts with a constrained varia- tional framework. In International Conference on Learn- ing Representations, 2016.","title":"beta-VAE: Learning basic visual concepts with a constrained variational framework","authors":"Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot","year":"2016","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Higgins et al., 2017] Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess, et al. DARLA: Im- proving zero-shot transfer in reinforcement learning. In Proceedings of the 34th International Conference on Ma- chine Learning, volume 70, pages 1480-1490, 2017.","title":"DARLA: Improving zero-shot transfer in reinforcement learning","authors":"Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess","year":"2017","venue":"Proceedings of the 34th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Higgins et al., 2018] Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess, et al. SCAN: Learning hierarchical compositional visual concepts. In Proceedings International Conference on Learning Rep- resentations, 2018.","title":"SCAN: Learning hierarchical compositional visual concepts","authors":"Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, P Christopher, Burgess","year":"2018","venue":"Proceedings International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Hofstadter and Mitchell, 1994] D. R. Hofstadter and M. Mitchell. The Copycat project: A model of mental fluidity and analogy-making. In K. J. Holyoak and J. A. Barnden, editors, Advances in Connectionist and Neural Computation Theory, volume 2, pages 31-112. Ablex Publishing Corporation, 1994.","title":"The Copycat project: A model of mental fluidity and analogy-making","authors":"D R Hofstadter, M Mitchell","year":"1994","venue":"Advances in Connectionist and Neural Computation Theory","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Hofstadter, 1995] D. R. Hofstadter. Fluid Concepts and Creative Analogies: Computer Models of the Fundamen- tal Mechanisms of Thought. Basic Books, New York, 1995.","title":"Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought","authors":"D R Hofstadter","year":"1995","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Holyoak and Thagard, 1989] K. J. Holyoak and P. Thagard. Analogical mapping by constraint satisfaction. Cognitive Science, 13(3):295-355, 1989.","title":"Analogical mapping by constraint satisfaction","authors":"K J Holyoak, P Thagard","year":"1989","venue":"Cognitive Science","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Hopfield, 1982] John J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sci- ences, 79(8):2554-2558, 1982.","title":"Neural networks and physical systems with emergent collective computational abilities","authors":"John J Hopfield","year":"1982","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Hopfield, 2007] John J. Hopfield. Hopfield network. Schol- arpedia, 2(5):1977, 2007.","title":"Hopfield network","authors":"John J Hopfield","year":"2007","venue":"Scholarpedia","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Hummel and Holyoak, 1996] J. E. Hummel and K. J. Holyoak. LISA: A computational model of analogical in- ference and schema induction. In Proceedings of the Eigh- teenth Annual Meeting of the Cognitive Science Society, pages 352-357, 1996.","title":"LISA: A computational model of analogical inference and schema induction","authors":"J E Hummel, K J Holyoak","year":"1996","venue":"Proceedings of the Eighteenth Annual Meeting of the Cognitive Science Society","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Kingma and Welling, 2014] Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. arXiv:1312.6114, 2014.","title":"Auto-encoding variational bayes","authors":"P Diederik, Max Kingma, Welling","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Kirk et al., 2021] Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel. A survey of generali- sation in deep reinforcement learning. arXiv:2111.09794, 2021.","title":"A survey of generalisation in deep reinforcement learning","authors":"Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Krotov and Hopfield, 2016] Dmitry Krotov and John J. Hopfield. Dense associative memory for pattern recogni- tion. In Advances in Neural Information Processing Sys- tems, volume 29, pages 1172-1180, 2016.","title":"Dense associative memory for pattern recognition","authors":"Dmitry Krotov, John J Hopfield","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Lake et al., 2017] Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman. Build- ing machines that learn and think like people. Behavioral and Brain Sciences, 40, 2017.","title":"Building machines that learn and think like people","authors":"M Brenden, Lake, D Tomer, Joshua B Ullman, Samuel J Tenenbaum, Gershman","year":"2017","venue":"Behavioral and Brain Sciences","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Lakoff and Johnson, 1980] George Lakoff and Mark John- son. Metaphors We Live By. University of Chicago Press, 1980.","title":"Metaphors We Live By","authors":"George Lakoff, Mark Johnson","year":"1980","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Lampinen et al., 2021] Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie C. Y. Chan, Allison C. Tam, et al. Tell me why! -explanations support learning of relational and causal structure. arXiv:2112.03753, 2021.","title":"Tell me why! -explanations support learning of relational and causal structure","authors":"Andrew K Lampinen, Nicholas A Roy, Ishita Dasgupta, C Y Stephanie, Allison C Chan, Tam","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Lázaro-Gredilla et al., 2019] Miguel Lázaro-Gredilla, Di- anhuan Lin, J. Swaroop Guntupalli, and Dileep George. Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs. Science Robotics, 4(26), 2019.","title":"Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs","authors":"Miguel Lázaro-Gredilla, Dianhuan Lin, J Swaroop Guntupalli, Dileep George","year":"2019","venue":"Science Robotics","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Lesser et al., 1975] Victor Lesser, Richard Fennell, Lee Er- man, and D. Reddy. Organization of the HEARSAY II speech understanding system. IEEE Transactions on Acoustics, Speech, and Signal Processing, 23(1):11-24, 1975.","title":"Organization of the HEARSAY II speech understanding system","authors":"Victor Lesser, Richard Fennell, Lee Erman, D Reddy","year":"1975","venue":"IEEE Transactions on Acoustics, Speech, and Signal Processing","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Locatello et al., 2020] Francesco Locatello, Dirk Weis- senborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, et al. Object-centric learning with slot at- tention. In Advances in Neural Information Processing Systems, volume 33, pages 11525-11538, 2020.","title":"Object-centric learning with slot attention","authors":"Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Luketina et al., 2019] Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, et al. A survey of reinforcement learning informed by natural language. In Proceedings of the International Joint Con- ference on Artificial Intelligence, pages 6309-6317, 2019.","title":"A survey of reinforcement learning informed by natural language","authors":"Luketina","year":"2019","venue":"Proceedings of the International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2019/880"},{"position":48,"raw":"[Marcus, 2001] Gary F Marcus. The Algebraic Mind. MIT Press, 2001.","title":"The Algebraic Mind","authors":"Marcus Gary","year":"2001","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Martin and Doumas, 2019] Andrea E Martin and Leonidas A A Doumas. Predicate learning in neural systems: using oscillations to discover latent structure. Current Opinion in Behavioral Sciences, 29:77-83, 2019.","title":"Predicate learning in neural systems: using oscillations to discover latent structure","authors":"Andrea E Martin, Leonidas A A Doumas","year":"2019","venue":"Current Opinion in Behavioral Sciences","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Mitchell, 2020] Melanie Mitchell. Can GPT-3 make analogies? https://medium.com/@melaniemitchell.me/ can-gpt-3-make-analogies-16436605c446, 2020. Ac- cessed: 2022-05-09.","title":"Can GPT-3 make analogies?","authors":"Melanie Mitchell","year":null,"venue":null,"doi":null,"url":"2022-05-09","internal_url":null},{"position":51,"raw":"[Mitchell, 2021] Melanie Mitchell. Abstraction and analogy- making in artificial intelligence. Annals of the New York Academy of Sciences, 1505:79-101, 2021.","title":"Abstraction and analogymaking in artificial intelligence","authors":"Melanie Mitchell","year":"2021","venue":"Annals of the New York Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Mitrovic et al., 2021] Jovana Mitrovic, Brian McWilliams, Jacob C Walker, Lars Holger Buesing, and Charles Blun- dell. Representation learning via invariant causal mech- anisms. In International Conference on Learning Repre- sentations, 2021.","title":"Representation learning via invariant causal mechanisms","authors":"Jovana Mitrovic, Brian Mcwilliams, Jacob C Walker, Lars Holger Buesing, Charles Blundell","year":"2021","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Oord et al., 2018] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predic- tive coding. arXiv:1807.03748, 2018.","title":"Representation learning with contrastive predictive coding","authors":"Aaron Van Den Oord, Yazhe Li, Oriol Vinyals","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Racanière et al., 2017] Sébastien Racanière, Theophane Weber, David Reichert, Lars Buesing, Arthur Guez, et al. Imagination-augmented agents for deep reinforcement learning. In Advances in Neural Information Processing Systems 30, pages 5690-5701, 2017.","title":"Imagination-augmented agents for deep reinforcement learning","authors":"Sébastien Racanière, Theophane Weber, David Reichert, Lars Buesing, Arthur Guez","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Ramsauer et al., 2021] Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, et al. Hopfield networks is all you need. In International Con- ference on Learning Representations, 2021.","title":"Hopfield networks is all you need","authors":"Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich","year":"2021","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Schank and Abelson, 1977] Roger C. Schank and Robert P. Abelson. Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures. Psychology Press, 1977.","title":"Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures","authors":"Roger C Schank, Robert P Abelson","year":"1977","venue":null,"doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Schrittwieser et al., 2020] Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, et al. Mastering Atari, Go, chess and shogi by planning with a learned model. Nature, 588:604-609, 2020.","title":"Mastering Atari, Go, chess and shogi by planning with a learned model","authors":"Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan","year":"2020","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Shanahan et al., 2020] Murray Shanahan, Benjamin Beyret, Matthew Crosby, and Lucy Cheke. Artificial intelligence and the common sense of animals. Trends in Cognitive Sciences, 24(11):862-872, 2020.","title":"Artificial intelligence and the common sense of animals","authors":"Shanahan","year":"2020","venue":"Trends in Cognitive Sciences","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Shanahan, 2006] Murray Shanahan. A cognitive archi- tecture that combines internal simulation with a global workspace. Consciousness and cognition, 15(2):433-449, 2006.","title":"A cognitive architecture that combines internal simulation with a global workspace","authors":"Murray Shanahan","year":"2006","venue":"Consciousness and cognition","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Smith, 2019] Brian Cantwell Smith. The Promise of Arti- ficial Intelligence: Reckoning and Judgment. MIT Press, 2019.","title":"The Promise of Artificial Intelligence: Reckoning and Judgment","authors":"Brian Cantwell, Smith","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Smolensky, 1990] Paul Smolensky. Tensor product vari- able binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46(1- 2):159-216, 1990.","title":"Tensor product variable binding and the representation of symbolic structures in connectionist systems","authors":"Paul Smolensky","year":"1990","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Spelke, 2000] Elizabeth S. Spelke. Core knowledge. Amer- ican Psychologist, November 2000:1233-1243, 2000.","title":"Core knowledge","authors":"Elizabeth S Spelke","year":"2000","venue":"American Psychologist","doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Tsividis et al., 2017] Pedro A Tsividis, Thomas Pouncy, Jacqueline L Xu, Joshua B Tenenbaum, and Samuel J Ger- shman. Human learning in Atari. In AAAI Spring Sympo- sium on Science of Intelligence: Computational Principles of Natural and Artificial Intelligence, 2017.","title":"Human learning in Atari","authors":"Pedro A Tsividis, Thomas Pouncy, Jacqueline L Xu, Joshua B Tenenbaum, Samuel J Gershman","year":"2017","venue":"AAAI Spring Symposium on Science of Intelligence: Computational Principles of Natural and Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Turner, 1998] Mark Turner. The Literary Mind: The Origins of Thought and Language. Oxford University Press, 1998.","title":"The Literary Mind: The Origins of Thought and Language","authors":"Mark Turner","year":"1998","venue":null,"doi":null,"url":null,"internal_url":null},{"position":65,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, et al. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30, pages 6000-6010, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[Voudouris et al., 2022] Konstantinos Voudouris, Matthew Crosby, Benjamin Beyret, José Hernández-Orallo, Mur- ray Shanahan, et al. Direct human-AI comparison in the Animal-AI environment. Frontiers in Psychology, 2022.","title":"Direct human-AI comparison in the Animal-AI environment","authors":"Konstantinos Voudouris, Matthew Crosby, Benjamin Beyret, José Hernández-Orallo, Murray Shanahan","year":"2022","venue":"Frontiers in Psychology","doi":null,"url":null,"internal_url":null},{"position":67,"raw":"[Wu et al., 2018] Yan Wu, Greg Wayne, Alex Graves, and Timothy Lillicrap. The kanerva machine: A genera- tive distributed memory. In International Conference on Learning Representations, 2018.","title":"The kanerva machine: A generative distributed memory","authors":"Yan Wu, Greg Wayne, Alex Graves, Timothy Lillicrap","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":68,"raw":"[Zhang et al., 2019] C. Zhang, F. Gao, B. Jia, Y. Zhu, and S.- C. Zhu. RAVEN: A dataset for relational and analogical visual reasoning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5312- 5322, 2019.","title":"RAVEN: A dataset for relational and analogical visual reasoning","authors":"C Zhang, F Gao, B Jia, Y Zhu, S.-C Zhu","year":"2019","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}