We are looking at how shared communicative systems can emerge and develop in populations of independently adapting Reinforcement Learning agents. We start with simulations of a "minimal assumptions" multi-agent model (w.r.t. pre-built constraints/architecture/supervision) and then add potentially helpful components one-by-one to probe the necessary and sufficient conditions for the emergence of communicative patterns. To isolate the properties of communicative systems affected by our interventions, we have developed a set of metrics for multi-agent communicative analysis.
Dubova, M., Moskvichev, A., & Goldstone, R. (2020). Reinforcement Communication Learning in Different Social Network Structures. ICML 2020 1st Language and Reinforcement Learning Workshop. (see this 5-min video presentation from ICML LAREL)
Dubova, M., & Moskvichev, A. (2020). Effects of supervision, population size, and self-play on multi-agent reinforcement learning to communicate. Artificial Life Conference Proceedings (pp. 678-686).
We are studying how unsupervised and task-dependent perceptual learning mechanisms are supporting adaptive concept learning in humans and artificial neural networks. We formalize multi-task perceptual learning with Bayesian models, and with a convolutional beta-VAE neural network trained to both reconstruct and categorize perceptual inputs. We conduct behavioral experiments to compare model predictions and human learning.
We conducted several experimental studies to test the factors that determine the onset of assimilative or contrastive visual adaptation aftereffect. Getting insights from these data, we developed a probabilistic model of the potential common mechanism underlying adaptation aftereffects in the opposite directions. We formalized the alterations of perception which occur after short-term adaptation as a result of Bayesian inference based on learning the perceptual structure of stimuli.
Collaborator: Arseny Moskvichev
Dubova, M., & Moskvichev, A. (2019). Adaptation Aftereffects as a Result of Bayesian Categorization. Proceedings of the 41st Annual Meeting of the Cognitive Science Society (pp. 1669-1675).
Semantic similarity detection
We tried to capture between-sentence similarity with a combination of different metrics, starting from simple word overlapping and ending with distance between sentence embedding representations. The work went beyond the initial scope when we obtained unrealistically high performance scores and realized that the evaluation metric used for the algorithms’ selection for many years was biased. Eventually, we did not only develop a new semantic similarity detection method, but also proposed a new evaluation framework for the task.
Collaborator: Anton Belyy
Belyy, A., Dubova, M., & Nekrasov, D. (2018). Improved evaluation framework for complex plagiarism detection. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 157-162).
Belyy, A. V., & Dubova, M. A. (2018). Framework for Russian plagiarism detection using sentence embedding similarity and negative sampling. Dialogue, 1.