586 research outputs found
Background, Systematic Review, Challenges and Outlook
Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.publishersversionpublishe
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
Spatio-Temporal Context in Agent-Based Meeting Scheduling
Meeting scheduling is a common task for organizations of all sizes. It involves searching for a time and place when and where all the participants can meet. However, scheduling a meeting is generally difficult in that it attempts to satisfy the preferences of all participants. Negotiation tends to be an iterative and time consuming task. Proxy agents can handle the negotiation on behalf of the individuals without sacrificing their privacy or overlooking their preferences. This thesis examines the implications of formalizing meeting scheduling as a spatiotemporal negotiation problem. The “Children in the Rectangular Forest” (CRF) canonical model is applied to meeting scheduling. By formalizing meeting scheduling within the CRF model, a generalized problem emerges that establishes a clear relationship with other spatiotemporal distributed scheduling problems. The thesis also examines the implications of the proposed formalization to meeting scheduling negotiations. A protocol for meeting location selection is presented and evaluated using simulations
Aprendizagem de coordenação em sistemas multi-agente
The ability for an agent to coordinate with others within a system is a
valuable property in multi-agent systems. Agents either cooperate as a team
to accomplish a common goal, or adapt to opponents to complete different
goals without being exploited. Research has shown that learning multi-agent
coordination is significantly more complex than learning policies in singleagent
environments, and requires a variety of techniques to deal with the
properties of a system where agents learn concurrently. This thesis aims to
determine how can machine learning be used to achieve coordination within
a multi-agent system. It asks what techniques can be used to tackle the
increased complexity of such systems and their credit assignment challenges,
how to achieve coordination, and how to use communication to improve the
behavior of a team.
Many algorithms for competitive environments are tabular-based, preventing
their use with high-dimension or continuous state-spaces, and may be
biased against specific equilibrium strategies. This thesis proposes multiple
deep learning extensions for competitive environments, allowing algorithms
to reach equilibrium strategies in complex and partially-observable environments,
relying only on local information. A tabular algorithm is also extended
with a new update rule that eliminates its bias against deterministic strategies.
Current state-of-the-art approaches for cooperative environments rely
on deep learning to handle the environment’s complexity and benefit from a
centralized learning phase. Solutions that incorporate communication between
agents often prevent agents from being executed in a distributed
manner. This thesis proposes a multi-agent algorithm where agents learn
communication protocols to compensate for local partial-observability, and
remain independently executed. A centralized learning phase can incorporate
additional environment information to increase the robustness and speed with
which a team converges to successful policies. The algorithm outperforms
current state-of-the-art approaches in a wide variety of multi-agent environments.
A permutation invariant network architecture is also proposed
to increase the scalability of the algorithm to large team sizes. Further research
is needed to identify how can the techniques proposed in this thesis,
for cooperative and competitive environments, be used in unison for mixed
environments, and whether they are adequate for general artificial intelligence.A capacidade de um agente se coordenar com outros num sistema é uma
propriedade valiosa em sistemas multi-agente. Agentes cooperam como
uma equipa para cumprir um objetivo comum, ou adaptam-se aos oponentes
de forma a completar objetivos egoístas sem serem explorados. Investigação
demonstra que aprender coordenação multi-agente é significativamente
mais complexo que aprender estratégias em ambientes com um
único agente, e requer uma variedade de técnicas para lidar com um ambiente
onde agentes aprendem simultaneamente. Esta tese procura determinar
como aprendizagem automática pode ser usada para encontrar coordenação
em sistemas multi-agente. O documento questiona que técnicas podem ser
usadas para enfrentar a superior complexidade destes sistemas e o seu desafio
de atribuição de crédito, como aprender coordenação, e como usar
comunicação para melhorar o comportamento duma equipa.
Múltiplos algoritmos para ambientes competitivos são tabulares, o que impede
o seu uso com espaços de estado de alta-dimensão ou contínuos, e
podem ter tendências contra estratégias de equilíbrio específicas. Esta tese
propõe múltiplas extensões de aprendizagem profunda para ambientes competitivos,
permitindo a algoritmos atingir estratégias de equilíbrio em ambientes
complexos e parcialmente-observáveis, com base em apenas informação
local. Um algoritmo tabular é também extendido com um novo critério de
atualização que elimina a sua tendência contra estratégias determinísticas.
Atuais soluções de estado-da-arte para ambientes cooperativos têm base em
aprendizagem profunda para lidar com a complexidade do ambiente, e beneficiam
duma fase de aprendizagem centralizada. Soluções que incorporam
comunicação entre agentes frequentemente impedem os próprios de ser executados
de forma distribuída. Esta tese propõe um algoritmo multi-agente
onde os agentes aprendem protocolos de comunicação para compensarem
por observabilidade parcial local, e continuam a ser executados de forma
distribuída. Uma fase de aprendizagem centralizada pode incorporar informação
adicional sobre ambiente para aumentar a robustez e velocidade
com que uma equipa converge para estratégias bem-sucedidas. O algoritmo
ultrapassa abordagens estado-da-arte atuais numa grande variedade de ambientes
multi-agente. Uma arquitetura de rede invariante a permutações é
também proposta para aumentar a escalabilidade do algoritmo para grandes
equipas. Mais pesquisa é necessária para identificar como as técnicas propostas
nesta tese, para ambientes cooperativos e competitivos, podem ser
usadas em conjunto para ambientes mistos, e averiguar se são adequadas a
inteligência artificial geral.Apoio financeiro da FCT e do FSE no âmbito do III Quadro Comunitário de ApoioPrograma Doutoral em Informátic
Optimisation sous contraintes de problèmes distribués par auto-organisation coopérative
Quotidiennement, divers problèmes d'optimisation : minimiser un coût de production, optimiser le parcours d'un véhicule, etc sont à résoudre. Ces problèmes se caractérisent par un degré élevé de complexité dû à l'hétérogénéité et la diversité des acteurs en jeu, à la masse importante des données ainsi qu'à la dynamique des environnements dans lesquels ils sont plongés.
Face à la complexité croissante de ces applications, les approches de résolution classiques ont montré leurs limites. Depuis quelques années, la communauté scientifique s'intéresse aux développements de nouvelles solutions basées sur la distribution du calcul et la décentralisation du contrôle plus adaptées à ce genre de problème. La théorie des AMAS (Adaptive Multi-Agents Systems) propose le développement de solutions utilisant des systèmes multi-agents auto-adaptatifs par auto-organisation coopérative. Cette théorie a montré son adéquation pour la résolution de problèmes complexes et dynamiques, mais son application reste à un niveau d'abstraction assez élevé.
L'objectif de ce travail est de spécialiser cette théorie pour la résolution de ce genre de problèmes. Ainsi, son utilisation en sera facilitée. Pour cela, le modèle d'agents AMAS4Opt avec des comportements et des interactions coopératifs et locaux a été défini. La validation s'est effectuée sur deux problèmes clés d'optimisation : le contrôle manufacturier et la conception de produit complexe. De plus, afin de montrer la robustesse et l'adéquation des solutions développées, un ensemble de critères d'évaluation permettant de souligner les points forts et faibles des systèmes adaptatifs et de les comparer à des systèmes existants a été défini.We solve problems and make decisions all day long. Some problems and decisions are very challenging: What is the best itinerary to deliver orders given the weather, the traffic and the hour? How to improve product manufacturing performances? etc. Problems that are characterized by a high level of complexity due to the heterogeneity and diversity of the participating actors, to the increasing volume of manipulated data and to the dynamics of the applications environments.
Classical solving approaches have shown their limits to cope with this growing complexity. For the last several years, the scientific community has been interested in the development of new solutions based on computation distribution and control decentralization. The AMAS (Adaptive Multi-Agent-Systems) theory proposes to build solutions based on self-adaptive multi-agent systems using cooperative self-organization. This theory has shown its adequacy to solve different complex and dynamic problems, but remains at a high abstraction level.
This work proposes a specialization of this theory for complex optimization problem solving under constraints. Thus, the usage of this theory is made accessible to different non-AMAS experts' engineers. Thus, the AMAS4Opt agent model with cooperative, local and generic behaviours and interactions has been defined.This model is validated on two well-known optimization problems: scheduling in manufacturing control and complex product design. Finally, in order to show the robustness and adequacy of the developed solutions, a set of evaluation criteria is proposed to underline the advantages and limits of adaptive systems and to compare them with already existing systems
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