A modular architecture for creating multimodal agents
- URL: http://arxiv.org/abs/2206.00636v1
- Date: Wed, 1 Jun 2022 17:12:10 GMT
- Title: A modular architecture for creating multimodal agents
- Abstract summary: The paper describes a flexible and modular platform to create multimodal interactive agents.
The platform operates through an event-bus on which signals and interpretations are posted in a sequence in time.
We explain a broad range of components that have been developed so far and integrated into a range of interactive agents.
- Score: 2.166951056466717
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The paper describes a flexible and modular platform to create multimodal
interactive agents. The platform operates through an event-bus on which signals
and interpretations are posted in a sequence in time. Different sensors and
interpretation components can be integrated by defining their input and output
as topics, which results in a logical workflow for further interpretations. We
explain a broad range of components that have been developed so far and
integrated into a range of interactive agents. We also explain how the actual
interaction is recorded as multimodal data as well as in a so-called episodic
Knowledge Graph. By analysing the recorded interaction, we can analyse and
compare different agents and agent components.
Related papers
- Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
arXiv Detail & Related papers (2025-06-22T16:52:48Z) - Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
"Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data.
We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
arXiv Detail & Related papers (2024-01-07T19:11:18Z) - Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph.
We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
arXiv Detail & Related papers (2023-07-19T02:11:19Z) - InterMulti:Multi-view Multimodal Interactions with Text-dominated
Hierarchical High-order Fusion for Emotion Analysis [10.048903012988882]
We propose a multimodal emotion analysis framework, InterMulti, to capture complex multimodal interactions from different views.
Our proposed framework decomposes signals of different modalities into three kinds of multimodal interaction representations.
THHF module reasonably integrates the above three kinds of representations into a comprehensive multimodal interaction representation.
arXiv Detail & Related papers (2022-12-20T07:02:32Z) - Interaction Modeling with Multiplex Attention [17.04973256281265]
We introduce a method for accurately modeling multi-agent systems.
We show that our approach outperforms state-of-the-art models in trajectory forecasting and relation inference.
arXiv Detail & Related papers (2022-08-23T00:29:18Z) - Multimodal Image Synthesis and Editing: The Generative AI Era [131.9569600472503]
multimodal image synthesis and editing has become a hot research topic in recent years.
We comprehensively contextualize the advance of the recent multimodal image synthesis and editing.
We describe benchmark datasets and evaluation metrics as well as corresponding experimental results.
arXiv Detail & Related papers (2021-12-27T10:00:16Z) - 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) - Referring Image Segmentation via Cross-Modal Progressive Comprehension [94.70482302324704]
Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression.
Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities.
We propose a Cross-Modal Progressive (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task.
arXiv Detail & Related papers (2020-10-01T16:02:30Z) - Situated and Interactive Multimodal Conversations [21.391260370502224]
We introduce Situated Interactive MultiModal Conversations (SIMMC) as a new direction aimed at training agents.
We provide two SIMMC datasets totalling 13K human-human dialogs (169K utterances) using a multimodal Wizard-of-Oz (WoZ) setup.
We present several tasks within SIMMC as objective evaluation protocols, such as Structural API Prediction and Response Generation.
arXiv Detail & Related papers (2020-06-02T09:02:23Z) - 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.