Models of symbol emergence in communication: a conceptual review and a
guide for avoiding local minima
- URL: http://arxiv.org/abs/2303.04544v1
- Date: Wed, 8 Mar 2023 12:53:03 GMT
- Title: Models of symbol emergence in communication: a conceptual review and a
guide for avoiding local minima
- Authors: Julian Zubek, Tomasz Korbak, Joanna R\k{a}czaszek-Leonardi
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Computational simulations are a popular method for testing hypotheses about
the emergence of communication. This kind of research is performed in a variety
of traditions including language evolution, developmental psychology, cognitive
science, machine learning, robotics, etc. The motivations for the models are
different, but the operationalizations and methods used are often similar. We
identify the assumptions and explanatory targets of several most representative
models and summarise the known results. We claim that some of the assumptions
-- such as portraying meaning in terms of mapping, focusing on the descriptive
function of communication, modelling signals with amodal tokens -- may hinder
the success of modelling. Relaxing these assumptions and foregrounding the
interactions of embodied and situated agents allows one to systematise the
multiplicity of pressures under which symbolic systems evolve. In line with
this perspective, we sketch the road towards modelling the emergence of
meaningful symbolic communication, where symbols are simultaneously grounded in
action and perception and form an abstract system.
Related papers
- Automatic Discovery of Visual Circuits [66.99553804855931]
We explore scalable methods for extracting the subgraph of a vision model's computational graph that underlies recognition of a specific visual concept.
We find that our approach extracts circuits that causally affect model output, and that editing these circuits can defend large pretrained models from adversarial attacks.
arXiv Detail & Related papers (2024-04-22T17:00:57Z) - Language, Environment, and Robotic Navigation [0.0]
We propose a unified framework where language functions as an abstract communicative system and as a grounded representation of perceptual experiences.
Our review of cognitive models of distributional semantics and their application to autonomous agents underscores the transformative potential of language-integrated systems.
arXiv Detail & Related papers (2024-04-03T20:30:38Z) - Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world.
The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time.
The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
arXiv Detail & Related papers (2023-07-25T17:59:18Z) - A Recursive Bateson-Inspired Model for the Generation of Semantic Formal
Concepts from Spatial Sensory Data [77.34726150561087]
This paper presents a new symbolic-only method for the generation of hierarchical concept structures from complex sensory data.
The approach is based on Bateson's notion of difference as the key to the genesis of an idea or a concept.
The model is able to produce fairly rich yet human-readable conceptual representations without training.
arXiv Detail & Related papers (2023-07-16T15:59:13Z) - Symbol emergence as interpersonal cross-situational learning: the
emergence of lexical knowledge with combinatoriality [5.350057408744861]
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.
arXiv Detail & Related papers (2023-06-27T23:55:33Z) - Foundation Models for Decision Making: Problems, Methods, and
Opportunities [124.79381732197649]
Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks.
New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning.
Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
arXiv Detail & Related papers (2023-03-07T18:44:07Z) - Geometric and Topological Inference for Deep Representations of Complex
Networks [13.173307471333619]
We present a class of statistics that emphasize the topology as well as the geometry of representations.
We evaluate these statistics in terms of the sensitivity and specificity that they afford when used for model selection.
These new methods enable brain and computer scientists to visualize the dynamic representational transformations learned by brains and models.
arXiv Detail & Related papers (2022-03-10T17:14:14Z) - Emergent Graphical Conventions in a Visual Communication Game [80.79297387339614]
Humans communicate with graphical sketches apart from symbolic languages.
We take the very first step to model and simulate such an evolution process via two neural agents playing a visual communication game.
We devise a novel reinforcement learning method such that agents are evolved jointly towards successful communication and abstract graphical conventions.
arXiv Detail & Related papers (2021-11-28T18:59:57Z) - 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)
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.