1,240 research outputs found
Towards Construction of Creative Collaborative Teams Using Multiagent Systems
Group creativity and innovation are of chief importance for both collaborative learning and collaborative working, as increasing the efficiency and effectiveness of groups of individuals performing together specific activities to achieve common goals, in given contexts, is of crucial importance nowadays. Nevertheless, construction of âthe mostâ creative and innovative groups given a cohort of people and a set of common goals and tasks to perform is challenging. We present here our method for semi-automatic construction of âthe mostâ creative and innovative teams given a group of persons and a particular goal, which is based on unsupervised learning and it is supported by a multiagent system. Individual creativity and motivation are both factors influencing group creativity used in the experiments performed with our Computer Science students. However, the method is general and can be used for building the most creative and innovative groups in any collaborative situation
Synergistic Team Composition
Effective teams are crucial for organisations, especially in environments
that require teams to be constantly created and dismantled, such as software
development, scientific experiments, crowd-sourcing, or the classroom. Key
factors influencing team performance are competences and personality of team
members. Hence, we present a computational model to compose proficient and
congenial teams based on individuals' personalities and their competences to
perform tasks of different nature. With this purpose, we extend Wilde's
post-Jungian method for team composition, which solely employs individuals'
personalities. The aim of this study is to create a model to partition agents
into teams that are balanced in competences, personality and gender. Finally,
we present some preliminary empirical results that we obtained when analysing
student performance. Results show the benefits of a more informed team
composition that exploits individuals' competences besides information about
their personalities
Separating Agent-Functioning and Inter-Agent Coordination by Activated Modules: The DECOMAS Architecture
The embedding of self-organizing inter-agent processes in distributed
software applications enables the decentralized coordination system elements,
solely based on concerted, localized interactions. The separation and
encapsulation of the activities that are conceptually related to the
coordination, is a crucial concern for systematic development practices in
order to prepare the reuse and systematic integration of coordination processes
in software systems. Here, we discuss a programming model that is based on the
externalization of processes prescriptions and their embedding in Multi-Agent
Systems (MAS). One fundamental design concern for a corresponding execution
middleware is the minimal-invasive augmentation of the activities that affect
coordination. This design challenge is approached by the activation of agent
modules. Modules are converted to software elements that reason about and
modify their host agent. We discuss and formalize this extension within the
context of a generic coordination architecture and exemplify the proposed
programming model with the decentralized management of (web) service
infrastructures
Towards Building Creative Collaborative Learning Groups Using Reinforcement Learning
Increasing creative skills in collaborative groups is of huge interest for stakeholders in education, industry, policy making etc. However, construction of âthe mostâ creative groups given a cohort of people and a set of common goals and tasks to perform is challenging. The complexity of this undertaking is amplified by the necessity to first understand and then measure what âthe mostâ creative means in a particular situation. We present here our method of semi-automatic building of âthe mostâ creative learning groups given a cohort of students and a particular learning context based on reinforcement learning (an adapted Q-learning algorithm). Various attributes that influence individual and group creativity may be considered. A case study on using this method with our Computer Science students is also included. However, the method is general and can be used for building collaborative groups in any situation, with the appropriate âthe mostâ creative goal and attributes
Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches
This paper surveys the field of multiagent deep reinforcement learning. The
combination of deep neural networks with reinforcement learning has gained
increased traction in recent years and is slowly shifting the focus from
single-agent to multiagent environments. Dealing with multiple agents is
inherently more complex as (a) the future rewards depend on the joint actions
of multiple players and (b) the computational complexity of functions
increases. We present the most common multiagent problem representations and
their main challenges, and identify five research areas that address one or
more of these challenges: centralised training and decentralised execution,
opponent modelling, communication, efficient coordination, and reward shaping.
We find that many computational studies rely on unrealistic assumptions or are
not generalisable to other settings; they struggle to overcome the curse of
dimensionality or nonstationarity. Approaches from psychology and sociology
capture promising relevant behaviours such as communication and coordination.
We suggest that, for multiagent reinforcement learning to be successful, future
research addresses these challenges with an interdisciplinary approach to open
up new possibilities for more human-oriented solutions in multiagent
reinforcement learning.Comment: 37 pages, 6 figure
AutoAgents: A Framework for Automatic Agent Generation
Large language models (LLMs) have enabled remarkable advances in automated
task-solving with multi-agent systems. However, most existing LLM-based
multi-agent approaches rely on predefined agents to handle simple tasks,
limiting the adaptability of multi-agent collaboration to different scenarios.
Therefore, we introduce AutoAgents, an innovative framework that adaptively
generates and coordinates multiple specialized agents to build an AI team
according to different tasks. Specifically, AutoAgents couples the relationship
between tasks and roles by dynamically generating multiple required agents
based on task content and planning solutions for the current task based on the
generated expert agents. Multiple specialized agents collaborate with each
other to efficiently accomplish tasks. Concurrently, an observer role is
incorporated into the framework to reflect on the designated plans and agents'
responses and improve upon them. Our experiments on various benchmarks
demonstrate that AutoAgents generates more coherent and accurate solutions than
the existing multi-agent methods. This underscores the significance of
assigning different roles to different tasks and of team cooperation, offering
new perspectives for tackling complex tasks. The repository of this project is
available at https://github.com/Link-AGI/AutoAgents
An Abstract Formal Basis for Digital Crowds
Crowdsourcing, together with its related approaches, has become very popular
in recent years. All crowdsourcing processes involve the participation of a
digital crowd, a large number of people that access a single Internet platform
or shared service. In this paper we explore the possibility of applying formal
methods, typically used for the verification of software and hardware systems,
in analysing the behaviour of a digital crowd. More precisely, we provide a
formal description language for specifying digital crowds. We represent digital
crowds in which the agents do not directly communicate with each other. We
further show how this specification can provide the basis for sophisticated
formal methods, in particular formal verification.Comment: 32 pages, 4 figure
Results of multi-agent system and ontology to manage ideas and represent knowledge in a challenge of creativity
This article is about an intelligent system to support ideas management as a
result of a multi-agent system used in a distributed system with heterogeneous
information as ideas and knowledge, after the results about an ontology to
describe the meaning of these ideas. The intelligent system assists
participants of the creativity workshop to manage their ideas and consequently
proposing an ontology dedicated to ideas. During the creative workshop many
creative activities and collaborative creative methods are used by roles
immersed in this creativity workshop event where they share knowledge. The
collaboration of these roles is physically distant, their interactions might be
synchrony or asynchrony, and the information of the ideas are heterogeneous, so
we can say that the process is distributed. Those ideas are writing in natural
language by participants which have a role and the ideas are heterogeneous
since some of them are described by schema, text or scenario of use. This paper
presents first, our MAS and second our Ontology design
Serial Integration, Real Innovation: Roles of Diverse Knowledge and Communicative Participation in Crowdsourcing
Despite a burgeoning public and scholarly interest on open innovation and crowdsourcing, how to enable members of online temporary crowd to maintain knowledge integration and innovation remains underexplored. This study seeks to understand the ways in which online crowd members collectively generate more innovative and serial integrative solutions to crowdsourced open innovation challenges. Analyzing 3,200 unique posts generated by 486 participants of 21 organization-sponsored online crowdsourcing innovation challenges, this research demonstrates that crowd members contribute more innovative solutions when being exposed to explicitly shared diverse knowledge, and that crowd membersâ communicative participation acts as a catalyst for the production of both innovation and serial knowledge integration. Findings suggest that managers who seek to generate knowledge integration and innovation should endeavor to implement systems that afford high-level communicative participation, as well as encourage crowd members to make their diverse knowledge explicit while minimizing their cognitive load in knowledge sharing
Intelligent Embedded Software: New Perspectives and Challenges
Intelligent embedded systems (IES) represent a novel and promising generation of embedded systems (ES). IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence (AI)-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-repairing. Despite the widespread of such systems, some design challenging issues are arising. Designing a resource-constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded system researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality
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