Embracing AWKWARD! Real-time Adjustment of Reactive Planning Using Social Norms

• URL: http://arxiv.org/abs/2204.10740v1
• Date: Fri, 22 Apr 2022 15:02:08 GMT
• Title: Embracing AWKWARD! Real-time Adjustment of Reactive Planning Using Social Norms
• Authors: Leila Methnani, Andreas Antoniades and Andreas Theodorou
• Abstract summary: AWKWARD agents can have their plans re-configured in real time to align with social role requirements. OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles.
• Score: 2.610470075814367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the AWKWARD agent architecture for the development of agents in Multi-Agent Systems. AWKWARD agents can have their plans re-configured in real time to align with social role requirements under changing environmental and social circumstances. The proposed hybrid architecture makes use of Behaviour Oriented Design (BOD) to develop agents with reactive planning and of the well-established OperA framework to provide organisational, social, and interaction definitions in order to validate and adjust agents' behaviours. Together, OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles, while providing the additional benefit of transparency into the interactions that drive this behavioural change in individual agents. We present this architecture to motivate the bridging between traditional symbolic- and behaviour-based AI communities, where such combined solutions can help MAS researchers in their pursuit of building stronger, more robust intelligent agent teams. We use DOTA2 -- a game where success is heavily dependent on social interactions -- as a medium to demonstrate a sample implementation of our proposed hybrid architecture

Related papers

• AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making [45.179910497107606]
  We introduce AdaSociety, a customizable multi-agent environment featuring expanding state and action spaces. As agents progress, the environment adaptively generates new tasks with social structures for agents to undertake. AdaSociety serves as a valuable research platform for exploring intelligence in diverse physical and social settings.
  arXiv  Detail & Related papers  (2024-11-06T12:19:01Z)
• Can Agents Spontaneously Form a Society? Introducing a Novel Architecture for Generative Multi-Agents to Elicit Social Emergence [0.11249583407496219]
  We introduce a generative agent architecture called ITCMA-S, which includes a basic framework for individual agents and a framework that supports social interactions among multi-agents. This architecture enables agents to identify and filter out behaviors that are detrimental to social interactions, guiding them to choose more favorable actions.
  arXiv  Detail & Related papers  (2024-09-10T13:39:29Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning [58.84311336011451]
  We propose a novel gradient-based state representation for multi-agent reinforcement learning. We employ denoising score matching to learn the social gradient fields (SocialGFs) from offline samples. In practice, we integrate SocialGFs into the widely used multi-agent reinforcement learning algorithms, e.g., MAPPO.
  arXiv  Detail & Related papers  (2024-05-03T04:12:19Z)
• S-Agents: Self-organizing Agents in Open-ended Environments [15.700383873385892]
  We introduce a self-organizing agent system (S-Agents) with a "tree of agents" structure for dynamic workflow. This structure can autonomously coordinate a group of agents, efficiently addressing the challenges of open and dynamic environments. Our experiments demonstrate that S-Agents proficiently execute collaborative building tasks and resource collection in the Minecraft environment.
  arXiv  Detail & Related papers  (2024-02-07T04:36:31Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors [93.38830440346783]
  We propose a multi-agent framework framework that can collaboratively adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that framework framework can effectively deploy multi-agent groups that outperform a single agent. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups.
  arXiv  Detail & Related papers  (2023-08-21T16:47:11Z)
• Towards socially-competent and culturally-adaptive artificial agents Expressive order, interactional disruptions and recovery strategies [0.0]
  The overarching aim of this work is to set a framework to make the artificial agent socially-competent beyond dyadic interaction-interaction. The paper highlights how this level of competence is achieved by focusing on just three dimensions: (i) social capability, (ii) relational role, and (iii) proximity.
  arXiv  Detail & Related papers  (2023-08-06T15:47:56Z)
• REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework [0.6335848702857039]
  A general framework is proposed to support the development of software systems that are able to adapt their behaviour according to the operating environment changes. The proposed approach, named REPTILE, works in a complete proactive manner and relies on Deep Reinforcement Learning-based agents to react to events. In our framework, two types of novelties are taken into account: those related to the context/environment and those related to the physical architecture itself. The framework, predicting those novelties before their occurrence, extracts time-changing models of the environment and uses a suitable Markov Decision Process to deal with the real-time setting.
  arXiv  Detail & Related papers  (2022-03-28T12:38:08Z)
• A ROS Architecture for Personalised HRI with a Bartender Social Robot [61.843727637976045]
  BRILLO project has the overall goal of creating an autonomous robotic bartender that can interact with customers while accomplishing its bartending tasks. We present the developed three-layers ROS architecture integrating a perception layer managing the processing of different social signals, a decision-making layer for handling multi-party interactions, and an execution layer controlling the behaviour of a complex robot composed of arms and a face.
  arXiv  Detail & Related papers  (2022-03-13T11:33:06Z)

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.