HEnRY: A Multi-Agent System Framework for Multi-Domain Contexts
- URL: http://arxiv.org/abs/2410.12720v1
- Date: Wed, 16 Oct 2024 16:28:49 GMT
- Title: HEnRY: A Multi-Agent System Framework for Multi-Domain Contexts
- Authors: Emmanuele Lacavalla, Shuyi Yang, Riccardo Crupi, Joseph E. Gonzalez,
- Abstract summary: HEnRY aims to introduce a Multi-Agent System (MAS) into Intesa Sanpaolo.
The name HEnRY summarizes the project's core principles.
- Score: 24.129185956252886
- License:
- Abstract: This project, named HEnRY, aims to introduce a Multi-Agent System (MAS) into Intesa Sanpaolo. The name HEnRY summarizes the project's core principles: the Hierarchical organization of agents in a layered structure for efficient resource management; Efficient optimization of resources and operations to enhance overall performance; Reactive ability of agents to quickly respond to environmental stimuli; and Yielding adaptability and flexibility of agents to handle unexpected situations. The discussion covers two distinct research paths: the first focuses on the system architecture, and the second on the collaboration between agents. This work is not limited to the specific structure of the Intesa Sanpaolo context; instead, it leverages existing research in MAS to introduce a new solution. Since Intesa Sanpaolo is organized according to a model that aligns with international corporate governance best practices, this approach could also be relevant to similar scenarios.
Related papers
- Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
We propose a novel framework for agent-oriented planning in multi-agent systems, leveraging a fast task decomposition and allocation process.
We integrate a feedback loop into the proposed framework to further enhance the effectiveness and robustness of such a problem-solving process.
arXiv Detail & Related papers (2024-10-03T04:07:51Z) - LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Integration of Multi Active/Passive Core-Agents [0.0]
We propose a novel LLM-based Agent Unified Modeling Framework (LLM-Agent-UMF)
Our framework distinguishes between the different components of an LLM-based agent, setting LLMs and tools apart from a new element, the core-agent.
We evaluate our framework by applying it to thirteen state-of-the-art agents, thereby demonstrating its alignment with their functionalities.
arXiv Detail & Related papers (2024-09-17T17:54:17Z) - AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction [10.65417796726349]
relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence.
We propose an agent-based RE framework, namely AgentRE, which fully leverages the potential of large language models to achieve RE in complex scenarios.
arXiv Detail & Related papers (2024-09-03T12:53:05Z) - CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration [87.51781348070914]
Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks.
We propose the CoAct framework, which transfers the hierarchical planning and collaboration patterns in human society to LLM systems.
arXiv Detail & Related papers (2024-06-19T09:23:53Z) - Large Multimodal Agents: A Survey [78.81459893884737]
Large language models (LLMs) have achieved superior performance in powering text-based AI agents.
There is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain.
This review aims to provide valuable insights and guidelines for future research in this rapidly evolving field.
arXiv Detail & Related papers (2024-02-23T06:04:23Z) - Agents meet OKR: An Object and Key Results Driven Agent System with
Hierarchical Self-Collaboration and Self-Evaluation [25.308341461293857]
OKR-Agent is designed to enhance the capabilities of Large Language Models (LLMs) in task-solving.
Our framework includes two novel modules: hierarchical Objects and Key Results generation and multi-level evaluation.
arXiv Detail & Related papers (2023-11-28T06:16:30Z) - Learning Reward Machines in Cooperative Multi-Agent Tasks [75.79805204646428]
This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL)
It combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks.
The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments.
arXiv Detail & Related papers (2023-03-24T15:12:28Z) - Multi-agent Deep Covering Skill Discovery [50.812414209206054]
We propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space.
Also, we propose a novel framework to adopt the multi-agent options in the MARL process.
We show that the proposed algorithm can effectively capture the agent interactions with the attention mechanism, successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options.
arXiv Detail & Related papers (2022-10-07T00:40:59Z) - HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with
Dual Coordination Mechanism [17.993973801986677]
Multi-agent reinforcement learning often suffers from the exponentially larger action space caused by a large number of agents.
We propose a novel value decomposition framework HAVEN based on hierarchical reinforcement learning for the fully cooperative multi-agent problems.
arXiv Detail & Related papers (2021-10-14T10:43:47Z) - RODE: Learning Roles to Decompose Multi-Agent Tasks [69.56458960841165]
Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles.
We propose to first decompose joint action spaces into restricted role action spaces by clustering actions according to their effects on the environment and other agents.
By virtue of these advances, our method outperforms the current state-of-the-art MARL algorithms on 10 of the 14 scenarios that comprise the challenging StarCraft II micromanagement benchmark.
arXiv Detail & Related papers (2020-10-04T09:20:59Z) - Relational-Grid-World: A Novel Relational Reasoning Environment and An
Agent Model for Relational Information Extraction [0.0]
Reinforcement learning (RL) agents are often designed specifically for a particular problem and they generally have uninterpretable working processes.
Statistical methods-based RL algorithms can be improved in terms of generalizability and interpretability using symbolic Artificial Intelligence (AI) tools such as logic programming.
We present a model-free RL architecture that is supported with explicit relational representations of the environmental objects.
arXiv Detail & Related papers (2020-07-12T11:30:48Z)
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.