Symbol emergence as interpersonal cross-situational learning: the
emergence of lexical knowledge with combinatoriality
- URL: http://arxiv.org/abs/2306.15837v1
- Date: Tue, 27 Jun 2023 23:55:33 GMT
- Title: Symbol emergence as interpersonal cross-situational learning: the
emergence of lexical knowledge with combinatoriality
- Authors: Yoshinobu Hagiwara, Kazuma Furukawa, Takafumi Horie, Akira Taniguchi,
and Tadahiro Taniguchi
- Abstract summary: We present a computational model for a symbol emergence system in cognitive and developmental robotics.
Our proposed model facilitates the emergence of lexical knowledge with Metropolisity by performing category formation.
Our results indicate that the lexical knowledge developed using our proposed model exhibits performance for novel situations.
- Score: 5.350057408744861
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a computational model for a symbol emergence system that enables
the emergence of lexical knowledge with combinatoriality among agents through a
Metropolis-Hastings naming game and cross-situational learning. Many
computational models have been proposed to investigate combinatoriality in
emergent communication and symbol emergence in cognitive and developmental
robotics. However, existing models do not sufficiently address category
formation based on sensory-motor information and semiotic communication through
the exchange of word sequences within a single integrated model. Our proposed
model facilitates the emergence of lexical knowledge with combinatoriality by
performing category formation using multimodal sensory-motor information and
enabling semiotic communication through the exchange of word sequences among
agents in a unified model. Furthermore, the model enables an agent to predict
sensory-motor information for unobserved situations by combining words
associated with categories in each modality. We conducted two experiments with
two humanoid robots in a simulated environment to evaluate our proposed model.
The results demonstrated that the agents can acquire lexical knowledge with
combinatoriality through interpersonal cross-situational learning based on the
Metropolis-Hastings naming game and cross-situational learning. Furthermore,
our results indicate that the lexical knowledge developed using our proposed
model exhibits generalization performance for novel situations through
interpersonal cross-modal inference.
Related papers
- MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities [72.68829963458408]
We present MergeNet, which learns to bridge the gap of parameter spaces of heterogeneous models.
The core mechanism of MergeNet lies in the parameter adapter, which operates by querying the source model's low-rank parameters.
MergeNet is learned alongside both models, allowing our framework to dynamically transfer and adapt knowledge relevant to the current stage.
arXiv Detail & Related papers (2024-04-20T08:34:39Z) - Models of symbol emergence in communication: a conceptual review and a
guide for avoiding local minima [0.0]
Computational simulations are a popular method for testing hypotheses about the emergence of communication.
We identify the assumptions and explanatory targets of several most representative models and summarise the known results.
In line with this perspective, we sketch the road towards modelling the emergence of meaningful symbolic communication.
arXiv Detail & Related papers (2023-03-08T12:53:03Z) - A Probabilistic Model Of Interaction Dynamics for Dyadic Face-to-Face
Settings [1.9544213396776275]
We develop a probabilistic model to capture the interaction dynamics between pairs of participants in a face-to-face setting.
This interaction encoding is then used to influence the generation when predicting one agent's future dynamics.
We show that our model successfully delineates between the modes, based on their interacting dynamics.
arXiv Detail & Related papers (2022-07-10T23:31:27Z) - DIME: Fine-grained Interpretations of Multimodal Models via Disentangled
Local Explanations [119.1953397679783]
We focus on advancing the state-of-the-art in interpreting multimodal models.
Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
arXiv Detail & Related papers (2022-03-03T20:52:47Z) - Meta-brain Models: biologically-inspired cognitive agents [0.0]
We propose a computational approach we call meta-brain models.
We will propose combinations of layers composed using specialized types of models.
We will conclude by proposing next steps in the development of this flexible and open-source approach.
arXiv Detail & Related papers (2021-08-31T05:20:53Z) - Multi-Agent Imitation Learning with Copulas [102.27052968901894]
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions.
In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems.
Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
arXiv Detail & Related papers (2021-07-10T03:49:41Z) - Controlling Synthetic Characters in Simulations: A Case for Cognitive
Architectures and Sigma [0.0]
Simulations require computational models of intelligence that generate realistic and credible behavior for the participating synthetic characters.
Sigma is a cognitive architecture and system that strives to combine what has been learned from four decades of independent work on symbolic cognitive architectures, probabilistic graphical models, and more recently neural models, under its graphical architecture hypothesis.
In this paper, we will introduce Sigma along with its diverse capabilities and then use three distinct proof-of-concept Sigma models to highlight combinations of these capabilities.
arXiv Detail & Related papers (2021-01-06T19:07:36Z) - Neuro-Symbolic Representations for Video Captioning: A Case for
Leveraging Inductive Biases for Vision and Language [148.0843278195794]
We propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning.
Our approach uses a dictionary learning-based method of learning relations between videos and their paired text descriptions.
arXiv Detail & Related papers (2020-11-18T20:21:19Z) - Behavior Priors for Efficient Reinforcement Learning [97.81587970962232]
We consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors.
We discuss how such latent variable formulations connect to related work on hierarchical reinforcement learning (HRL) and mutual information and curiosity based objectives.
We demonstrate the effectiveness of our framework by applying it to a range of simulated continuous control domains.
arXiv Detail & Related papers (2020-10-27T13:17:18Z) - Compositional Generalization by Learning Analytical Expressions [87.15737632096378]
A memory-augmented neural model is connected with analytical expressions to achieve compositional generalization.
Experiments on the well-known benchmark SCAN demonstrate that our model seizes a great ability of compositional generalization.
arXiv Detail & Related papers (2020-06-18T15:50:57Z) - Structural Inductive Biases in Emergent Communication [36.26083882473554]
We investigate the impact of representation learning in artificial agents by developing graph referential games.
We show that agents parametrized by graph neural networks develop a more compositional language compared to bag-of-words and sequence models.
arXiv Detail & Related papers (2020-02-04T14:59:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.