Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference

• URL: http://arxiv.org/abs/2109.07194v1
• Date: Wed, 15 Sep 2021 10:20:54 GMT
• Title: Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference
• Authors: Yoshinobu Hagiwara, Kazuma Furukawa, Akira Taniguchi, and Tadahiro Taniguchi
• Abstract summary: This paper describes a computational model of multiagent multimodal categorization that realizes emergent communication. Inter-MDM enables agents to form multimodal categories and appropriately share signs between agents. It is shown that emergent communication improves categorization accuracy, even when some sensory modalities are missing.
• Score: 4.964816143841663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes a computational model of multiagent multimodal categorization that realizes emergent communication. We clarify whether the computational model can reproduce the following functions in a symbol emergence system, comprising two agents with different sensory modalities playing a naming game. (1) Function for forming a shared lexical system that comprises perceptual categories and corresponding signs, formed by agents through individual learning and semiotic communication between agents. (2) Function to improve the categorization accuracy in an agent via semiotic communication with another agent, even when some sensory modalities of each agent are missing. (3) Function that an agent infers unobserved sensory information based on a sign sampled from another agent in the same manner as cross-modal inference. We propose an interpersonal multimodal Dirichlet mixture (Inter-MDM), which is derived by dividing an integrative probabilistic generative model, which is obtained by integrating two Dirichlet mixtures (DMs). The Markov chain Monte Carlo algorithm realizes emergent communication. The experimental results demonstrated that Inter-MDM enables agents to form multimodal categories and appropriately share signs between agents. It is shown that emergent communication improves categorization accuracy, even when some sensory modalities are missing. Inter-MDM enables an agent to predict unobserved information based on a shared sign.

Related papers

• Scaling Large-Language-Model-based Multi-Agent Collaboration [75.5241464256688]
  Pioneering advancements in large language model-powered agents have underscored the design pattern of multi-agent collaboration. Inspired by the neural scaling law, this study investigates whether a similar principle applies to increasing agents in multi-agent collaboration.
  arXiv  Detail & Related papers  (2024-06-11T11:02:04Z)
• SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems [53.94772445896213]
  Large Language Model (LLM)-based multi-agent systems have demonstrated promising performance in simulating human society. We propose SpeechAgents, a multi-modal LLM based multi-agent system designed for simulating human communication.
  arXiv  Detail & Related papers  (2024-01-08T15:01:08Z)
• 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)
• MADiff: Offline Multi-agent Learning with Diffusion Models [79.18130544233794]
  Diffusion model (DM) recently achieved huge success in various scenarios including offline reinforcement learning. We propose MADiff, a novel generative multi-agent learning framework to tackle this problem. Our experiments show the superior performance of MADiff compared to baseline algorithms in a wide range of multi-agent learning tasks.
  arXiv  Detail & Related papers  (2023-05-27T02:14:09Z)
• Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction [97.40338982628094]
  We propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN) which predicts trajectories of heterogeneous agents in multiply categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously. A hierarchical graph attention module is proposed to obtain category-tocategory interaction and agent-to-agent interaction.
  arXiv  Detail & Related papers  (2021-07-31T13:36:04Z)
• 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)
• Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information [1.713291434132985]
  This work proposes the representation of multi-agent communication capabilities as a directed labeled heterogeneous agent graph. We also introduce a neural network architecture that specializes communication in fully cooperative heterogeneous multi-agent tasks.
  arXiv  Detail & Related papers  (2020-12-14T15:09:57Z)
• BGC: Multi-Agent Group Belief with Graph Clustering [1.9949730506194252]
  We propose a semi-communication method to enable agents can exchange information without communication. Inspired by the neighborhood cognitive consistency, we propose a group-based module to divide adjacent agents into a small group and minimize in-group agents' beliefs. Results reveal that the proposed method achieves a significant improvement in the SMAC benchmark.
  arXiv  Detail & Related papers  (2020-08-20T07:07:20Z)
• The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning [6.18778092044887]
  Many real-world problems require the coordination of multiple autonomous agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication strategies that enable complex multi-agent coordination. We show how a single self-interested agent is capable of learning highly manipulative communication strategies that allows it to significantly outperform a cooperative team of agents.
  arXiv  Detail & Related papers  (2020-08-06T12:48:08Z)
• Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication [5.5438676149999075]
  We consider a fully cooperative multi-agent system where agents cooperate to maximize a system's utility. We propose that multi-agent systems must have the ability to communicate and understand the inter-plays between agents. We develop an architecture that allows for communication among agents and tailors the system's reward for each individual agent.
  arXiv  Detail & Related papers  (2020-04-01T14:36:13Z)
• Multi-Agent Interactions Modeling with Correlated Policies [53.38338964628494]
  In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework. We develop a Decentralized Adrial Imitation Learning algorithm with Correlated policies (CoDAIL) Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators.
  arXiv  Detail & Related papers  (2020-01-04T17:31:53Z)

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.