- Important Event Information
- Motivation and Background
- Themes of the Symposium
- Organization and Schedule
- Abstract Submissions
- Symposium Organizers
Important Event Information
|Abstracts Due||December 22nd, 2023|
|Selected Presenters Notified||January 5th, 2024|
|AAAI Symposium Registration Opens||January 10th, 2024|
|Finalized Abstracts Due||January 19th, 2024|
|Deadline for Registration||Febuary 29th, 2024|
|Finalized Schedule Released||March 2nd, 2024|
|Symposium Takes Place at Stanford University||March 25-27, 2024|
When preparing abstracts use the AAAI-24 Author Kit. There is no explicit requirement on length and no anonymization is required. Abstracts can be brief, but should provide sufficient detail for the organizing committee to determine the focus and substance of your proposed talk for planning purposes.
Please submit abstract via easychair to the “Symposium on Human-Like Learning” track.
The symposium registration details will be posted here when they become available from AAAI.
Motivation and Background
Recent machine-learning research has made incredible progress across a wide range of tasks. While many systems can achieve human-like performance, one area that is currently under explored is how to realize human-like learning capabilities within these systems. For example, machine learning typically employs batch training and requires more data and computation than people to achieve similar capabilities. The resulting models are effective, but difficult to update in the face of new data without costly retraining. In contrast, humans excel at rapidly assimilating new information on the fly from a limited number of examples. More research is needed to investigate human-like capabilities, such as efficient, incremental learning, and to explore the design of artificial systems that can also exhibit them.
Historically, the fields of Artificial Intelligence and Cognitive Science have maintained close theoretical ties. As a result, early machine-learning research placed a strong emphasis on human-like learning. For example, early research with systems like ACT (Anderson, 1982; 1983) and EPAM (Feigenbaum, 1959; Feigenbaum & Simon, 1984) aimed to both improve our understanding of human learning and to reproduce human-like learning capabilities within artificial systems. Other early research with Soar (Laird, Newell, & Rosenbloom, 1987) composed many human-like capabilities to propose a unified account of human cognition (both problem-solving and learning). Research within the Parallel Distributed Processing group (Rumelhart, McClelland, and the PDP Group, 1986) explored how parallel processing and distributed representations—often within neural networks—could account for human cognition and learning. Despite the success of these early efforts (many of which led directly to today’s dominant paradigms), the connection between Artificial Intelligence and Cognitive Science has weakened over the past decades; there has been an increasing emphasis on functional applications and the development of artificial systems that process data in ways and at scales that exceed human processing capacities. A symposium on human-like learning is timely because it can re-strengthen this theoretical connection and enable researchers to revisit this topic in light of recent computational advancements. A renewed focus on human learning should prove valuable, both in enhancing our comprehension of human capabilities and in enabling new advancements in applications, such as more efficient machine-learning systems.
Themes of the Symposium
At a high-level, the symposium aims to encourage discussion on the following themes:
- Identification of key characteristics of human-like learning to target in AI/ML research, along with what makes these characteristics challenging for current approaches;
- Ongoing and proposed research into how to create artificial systems that exhibit human-like learning;
- Approaches for evaluating human-like learning systems; and
- Exploration of the broader context for and impacts of human-like learning systems, such as how they might complement/benefit current machine learning systems and humanity.
Characteristics of Human Learning
There are several characteristics of human learning that could serve as targets for a general research program. Langley (2022) highlights several that could serve as anchors to motivate the symposium discussions:
- Expertise is acquired in a piecemeal manner, with one element being added at a time.
- Learning is an incremental activity that processes one experience at a time.
- Learning is guided by prior experience, which aids in the interpretation of new experiences.
- Expertise is acquired and refined rapidly, from few training cases.
This list is not exhaustive and only serves to provide initial motivation. We will encourage participants to propose, highlight, and discuss features of human learning that are central to their work and could inspire future computational work. A major output of the symposium will be a list of human-like capabilities that could be the focus for a sustained research program. We also hope that by encouraging researchers to emphasize and discuss human-like capabilities, rather than emphasizing their particular methodological approach, we can foster more effective communication between people that come from different disciplines (e.g., Psychology or AI) and methodological perspectives (e.g., Cognitive Architectures and Deep Learning) backgrounds.
Creating Human-Like Learning Systems
Given these characteristics, the major focus of the symposium will be to explore ongoing and proposed research into how to create artificial systems that exhibit human-like learning capabilities. Many different research efforts—spanning multiple methodological paradigms—have emphasized some aspects of human-like learning:
- Cognitive architectures: Researchers in this area have identified and operationalized several human-like learning assumptions, such as the assumption that “learning occurs online and incrementally” (Laird, Lebiere, & Rosenbloom, 2017).
- Interactive task learning: This paradigm emphasizes the incremental, multimodal acquisition of new task knowledge from a limited amount of interactive instruction provided by human teachers (Laird et al., 2017); early examples include systems like AnimNL (Webber, Badler, Di Eugenio, Geib, Levison, & Moore, 1995) and more recent examples include AILEEN (Mohan, Klenk, Shreve, Evans, Ang, & Maxwell, 2020) and ONYX (Ruoff, Myers, & Maedche, 2023).
- Extended/continual learning: A class of approaches that emphasizes learning of different types of knowledge or functions over extended durations (e.g., years); a notable example is NELL (Mitchell et al., 2018), which continually crawls the web and grows and refines its relational ontology based on what it learns.
- Probabilistic programming: This paradigm emphasizes the use of Bayesian priors (over both internal structures and parameters) to guide and constrain learning, which enables the acquisition of new concepts with few examples (Lake, Salakhutdinov, & Tenenbaum, 2015).
- Concept formation: Systems like Cobweb (Fisher & Langley, 1990; Fisher & Yoo, 1993; MacLellan, Matsakis, & Langley, 2022) emphasize the incremental, piecemeal acquisition of concepts and rapid, few-example learning.
- Analogical and case-based learning: The SAGE (McLure, Friedman, & Forbus, 2015) system leverages analogical processing to support efficient, few example learning over structured representations.
- Logic-based learning: Recent work on this topic emphasizes the induction of modular knowledge structures that can be composed at performance and guide subsequent learning (Cropper, Dumančić, & Muggleton, 2020).
- Simulated students: Efforts to simulate human novices has highlighted the importance of prior knowledge and self-explanation in guiding subsequent learning (VanLehn, Jones, & Chi, 1991) and emphasized comparison of human and machine behavior—both correct and incorrect—as a tool to better understand and simulate human learning (MacLellan, Harpstead, Patel, Koedinger, 2016; Weitekamp, Ye, Rachatasumrit, Harpstead, & Koedinger, 2020).
- Human-like neural network approaches: Although most current neural network approaches do not emphasize human-like learning, there are some efforts that do. One recent example investigates how to model humans’ ability to learn increasingly complex skills across a lifespan without major instances of catastrophic interference or forgetting (Lyndgaard, Tidler, Provine, & Varma, 2022).
These are just a few examples of research lines that emphasize human-like learning. The symposium aims to bring together individual researchers working across different paradigms who may feel isolated in their commitment to human-like learning, and to create a community that can foster collaboration and inspire innovation on this topic. Many of these efforts share similar goals, but there has only been limited discussion about common insights for creating human-like learning systems that span across paradigms. The symposium will encourage discussion on this topic by having speakers representing different paradigms present in the same session and facilitating a broader group discussion about cross-paradigm commonalities.
Evaluating Human-Like Learning Systems
Research on this topic will need some way to measure progress. Thus, another major aim of the symposium will be to discuss how to evaluate systems with human-like capabilities. Here are a few potential evaluations participants might discuss:
- assessing systems based on their learning efficiency, rather than final performance;
- measuring fits to human behavioral data, both quantitative and qualitative;
- establishing a set of target capabilities, with qualitative measures of attainment; and
- evaluating human-like learning systems in terms of ease of use and acceptance by human users who are interacting with them.
These are just a selection of several potential evaluation ideas and a key deliverable of the symposium will be to solicit participants ideas about evaluations and to encourage formation of a community consensus around appropriate evaluations.
Broader Impacts of Human-Like Learning Systems
Lastly, we envision that research into human-like learning will have substantial broader impacts across several research communities. For example, it could lead to improvements in the efficiency and sustainability of machine-learning models, and enable them to be more adaptable and flexible. It could also contribute to a deeper understanding of human learning, and lead to the reproduction and modeling of human learning capabilities. Finally, it could lead to more user-friendly AI systems with more relatable, human-like capabilities. A final aim of the symposium will be to reflect on and discuss the broader implications for research into human-like learning.
Organization and Schedule
The event will occur at the AAAI Spring Symposium at Stanford from March 25th to March 27th. To support the above aims, we plan to dedicate time in the schedule for group discussions, so we can work towards a consensus on questions like:
- What are the key characteristics of human learning that should be reproduced?
- How do different frameworks reproduce these features?
- How can we evaluate the adequacy of a given model?
- What are the broader impacts for human-like learning research?
We will have a small number of invited speakers set the context for the symposium. The remaining talks will be selected by the organizing committee based on abstracts submitted by participants. We aim to solicit around 20 technical presentations through this process. The talks will be organized into thematic groups that will be presented together, with the intention of highlighting commonalities across research efforts and paradigms. Each presentation will be allotted 30 minutes, with 20 minutes for the presentation and 10 minutes for discussion. We also plan to schedule time for general reflection and discussion to support the symposium’s aims (e.g., discussion of the evaluation of human-like learning systems). Exactly when we schedule these discussions will depend on the overall scheduling of submissions received, but we aim to have some discussion around the end of each day. Below is a tentative schedule.
Tentative Day 1 Schedule (3/25)
|9:00||9:30||Welcome & Introduction|
|9:30||10:00||Invited Speaker 1|
|10:00||10:30||Invited Speaker 2|
|11:00||11:30||Technical Presentation 1|
|11:30||12:00||Technical Presentation 2|
|12:00||12:30||Technical Presentation 3|
|2:00||2:30||Technical Presentation 4|
|2:30||3:00||Technical Presentation 5|
|3:00||3:30||Technical Presentation 6|
|6:00||7:00||AAAI Symposium Reception|
Tentative Day 2 Schedule (3/26)
|9:00||9:30||Technical Presentation 7|
|9:30||10:00||Technical Presentation 8|
|10:00||10:30||Technical Presentation 9|
|11:00||11:30||Technical Presentation 10|
|11:30||12:00||Technical Presentation 11|
|12:00||12:30||Technical Presentation 12|
|2:00||2:30||Technical Presentation 13|
|2:30||3:00||Technical Presentation 14|
|3:00||3:30||Technical Presentation 15|
|4:00||4:30||Technical Presentation 16|
|4:30||5:00||Technical Presentation 17|
|5:00||5:30||Reflection & Discussion Session|
|6:00||7:00||AAAI Plenary Session|
Tentative Day 3 Schedule (3/27)
|9:00||9:30||Day 3 Introduction|
|9:30||10:00||Technical Presentation 18|
|10:00||10:30||Technical Presentation 19|
|11:00||11:30||Technical Presentation 20|
|11:30||12:30||Reflection & Discussion Session|
We believe that this structure will encourage high-quality interaction among participants and foster cross-paradigm communication and collaboration. By emphasizing discussion of key human learning capabilities rather than centering the discussion on methodologies, we hope to make the symposium broadly accessible to researchers from different perspectives and paradigms. By intentionally grouping talks to highlight similarities across efforts, we hope to promote discussion across perspectives and methodologies. Finally, by discussing evaluation and broader impacts, we hope to support the creation of a community of dedicated researchers who are enthusiastic about investigating human-like learning. This in turn should promote additional research in this important area. If there is sufficient interest, we will organize a publication (e.g., a journal special issue) that reports results from the event.
Authors will submit abstracts, which the organizing committee will use to decide on session topics and presentations. Abstracts will also be shared with attendees before the start of the symposium. Authors of submissions that are not presented as talks will be invited to participate in a poster session during the first day. In choosing presenters, the committee will give preference to submissions that are more closely aligned to the overarching theme, while also trying to give coverage to different aspects of the theme.
Please use the AAAI Author Kit for your abstract submission, to ensure formatting across submissions is consistent.
Please submit abstracts via easychair.
- Christopher J. MacLellan, Teachable AI Lab, Georgia Institute of Technology, firstname.lastname@example.org
- Ute Schmid, University of Bamberg, email@example.com
- Douglas Fisher, Vanderbilt University, firstname.lastname@example.org
- Randolph M. Jones, Soar Technology, LLC, email@example.com
- Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological review, 89(4), 369.
- Anderson, J. R. (1983). A spreading activation theory of memory. Journal of verbal learning and verbal behavior, 22(3), 261-295.
- Cropper, A., Dumančić, S., & Muggleton, S. H. (2020). Turning 30: New ideas in inductive logic programming. arXiv preprint arXiv:2002.11002.
- Feigenbaum, E.A. (1959) An Information Processing Theory of Verbal Learning. RAND Report P-1817. Santa Monica, CA: RAND Corporation.
- Feigenbaum, E. A., & Simon, H. A. (1984). EPAM-like models of recognition and learning. Cognitive Science, 8, 305–336.
- Fisher, D., & Langley, P. (1990). “The Structure and Formation of Natural Categories,” in G. Bower (ed.), The Psychology of Learning and Motivation, 26, San Diego, CA: Academic Press, 241–284.
- Fisher, D., & Yoo, J. (1993). “Problem solving, categorization, and concept learning: A unifying view,” in G. Nakamura, R. Taraban, & D. Medin (eds.), The Psychology of Learning and Motivation, 29, San Diego, CA: Academic Press, 219–255.
- Laird, J. E., Gluck, K., Anderson, J., Forbus, K. D., Jenkins, O. C., Lebiere, C., … & Kirk, J. R. (2017). Interactive task learning. IEEE Intelligent Systems, 32(4), 6-21.
- Laird, J. E., Lebiere, C. & Rosenbloom, P. S. (2017). A Standard Model for the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics, AI Magazine 38(4). https://doi.org/10.1609/aimag.v38i4.2744
- Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). Soar: An architecture for general intelligence. Artificial intelligence, 33(1), 1-64.
- Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338.
- Langley, P. (2022). The computational gauntlet of human-like learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 11, pp. 12268-12273).
- Lyndgaard, S., Tidler, Z. R., Provine, L., & Varma, S. (2022). Catastrophic interference in neural network models is mitigated when the training data reflect a power-law environmental structure. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 44, No. 44).
- MacLellan, C.J., Harpstead, E., Patel, R., Koedinger, K.R. (2016). The Apprentice Learner Architecture: Closing the loop between learning theory and educational data. In Proceedings of the 9th International Conference on Educational Data Mining. Raleigh, NC: International Educational Data Mining Society.
- MacLellan, C.J., Matsakis, P., & Langley, P. (2022). Efficient Induction of Language Models via Probabilistic Concept Formation. In Proceedings of the Tenth Annual Conference on Advances in Cognitive Systems.
- McLure, M. D., Friedman, S. E., and Forbus, K. D. (2015). Extending analogical generalization with near-misses. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 565–571. Austin, TX: AAAI Press.
- Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Yang, B., Betteridge, J., … & Welling, J. (2018). Never-ending learning. Communications of the ACM, 61(5), 103-115.
- Mohan, S., Klenk, M., Shreve, M., Evans, K., Ang, A., & Maxwell, J. (2020). Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition. In Proceedings of the Annual Conference on Advances in Cognitive Systems 2020.
- Rumelhart, D.E., J.L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, Cambridge, Massachusetts: MIT Press, ISBN 978-0262680530
- Ruoff, M., Myers, B. A., & Maedche, A. (2023). ONYX: Assisting Users in Teaching Natural Language Interfaces Through Multi-Modal Interactive Task Learning. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-16).
- VanLehn, K., Jones, R. M., & Chi, M. T. H. (1991). Modeling the self-explanation effect with Cascade 3. In Proceedings of the human factors in computing systems conference (pp. 132–137).
- Webber, B., Badler, N., Di Eugenio, B., Geib, C., Levison, L., & Moore, M. (1995). Instructions, intentions and expectations. Artificial Intelligence, 73(1-2), 253-269.
- Weitekamp, D., Ye, Z., Rachatasumrit, N., Harpstead, E., & Koedinger, K. (2020). Investigating differential error types between human and simulated learners. In Proceedings of the 21st International Conference on Artificial Intelligence in Education, 586-597. Ifrane, Morocco: Springer International Publishing.