Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

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Automated Planning: Theory and Practice.

China is the world's largest user of industrial robots. In 2016, sales of industrial robots in China reached 87,000 units, accounting for around 30 percent of the global market. To put this number in perspective, robot sales in all of Europe and the Americas in 2016 reached 97,300 units (according to data from the International Federation of Robotics). Between 2005 and 2016, the operational stock of industrial robots in China increased at an annual average rate of 38 percent. In this paper, we describe the adoption of robots by China's manufacturers using both aggregate industry-level and firm-level data, and we provide possible explanations from both the supply and demand sides for why robot use has risen so quickly in China. A key contribution of this paper is that we have collected some of the world's first data on firms' robot adoption behaviors with our China Employer-Employee Survey (CEES), which contains the first firm-level data that is representative of the entire Chinese manufacturing sector.

https://pubs.aeaweb.org/doi/pdf/10.1257/jep.33.2.71

The Rise of Robots in China

This chapter contains sections titled: 2.1 Introduction, 2.2 Agent Communications, 2.3 Agent Interaction Protocols, 2.4 Societies of Agents, 2.5 Conclusions, 2.6 Exercises, 2.7 References

Multiagent Systems and Societies of Agents

Personalized orders bring challenges to the production paradigm, and there is an urgent need for the dynamic responsiveness and self-adjustment ability of the workshop. Traditional dispatching rules and heuristic algorithms solve the production planning and control problems by making schedules. However, the previous methods cannot work well in a changeable workshop environment when encountering a large number of stochastic disturbances of orders and resources. Recently, the potential of artificial intelligence (AI) algorithms in solving the dynamic scheduling problem has attracted researchers' attention. Therefore, this paper presents a multi-agent manufacturing system based on deep reinforcement learning (DRL), which integrates the self-organization mechanism and self-learning strategy. Firstly, the manufacturing equipment in the workshop is constructed as an equipment agent with the support of edge computing node, and an improved contract network protocol (CNP) is applied to guide the cooperation and competition among multiple agents, so as to complete personalized orders efficiently. Secondly, a multi-layer perceptron is employed to establish the decision-making module called AI scheduler inside the equipment agent. According to the perceived workshop state information, AI scheduler intelligently generates an optimal production strategy to perform task allocation. Then, based on the collected sample trajectories of scheduling process, AI scheduler is periodically trained and updated through the proximal policy optimization (PPO) algorithm to improve its decision-making performance. Finally, in the multi-agent manufacturing system testbed, dynamic events such as stochastic job insertions and unpredictable machine failures are considered in the verification experiments. The experimental results show that the proposed method is capable of obtaining the scheduling solutions that meet various performance metrics, as well as dealing with resource or task disturbances efficiently and autonomously. 

Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems

Holonic coordination obtained by joining the contract net protocol with constraint satisfaction

Holonic manufacturing execution systems still raise problems regarding modelling, design, testing and implementation of holons. This paper aims at obtaining an improved holonic scheme based on some underlying points: the types of holons used in PROSA holonic reference architecture, the contract net protocol for holons’ coordination and the BDI agents as software components. The addressed issues concern a solution for the proper interaction between several holons and the obtaining of certain patterns capable of guiding the design and implementation of holons. To be able to analyze holons’ behaviour and holonic system performance, a modelling approach using specific points of the planning theory and Petri nets was developed. The proposed formalization better clarifies how a holon’s components have to be devised, and clearly separates the holons’ construction and operation. Some drawbacks that can appear in a holonic system are revealed both by the holons’ models and by the conducted simulation experiments. The solution to the discovered weak points is based on the coordination provided by a centralized component and a specific way of conducting the bidding process. With these improvements a holonic adaptive plan-based architecture is obtained, this paper presenting the aspects regarding holons design and testing, while the problems on holonic agents’ reasoning procedure and details of their implementation are only briefly tackled.

On a holonic adaptive plan-based architecture: planning scheme and holons’ life periods

On Rescheduling in Holonic Manufacturing Systems

This paper presents a research on multi-robot system planning based on a combination between a multiagent system and a constraint satisfaction problem approach. By using coloured Petri net models, specifically developed for a distributed constraint satisfaction algorithm, it was possible to make some simulation tests with a multi-robot system comprised of four robots involved in solving assembly goals. The analysis determined a mechanism to make the distributed planning phase more efficient by adapting the agents' priorities.

A Constraint Satisfaction Approach for Planning of Multi-Robot Systems

This contribution reveals how the planning and coordination processes in Holonic Manufacturing Execution Systems entail the need of a centralized component, under the form of a staff holon. By using appropriate models (both monochrome and coloured Petri nets) and certain aspects of planning techniques in Artificial Intelligence, a PROSA based implementation is considered. In the obtained holonic scheme, named HAPBA, the solution of the manufacturing goals is attained by holons’ cooperation, according to an enhanced form of the Contract Net Protocol, so that certain drawbacks that are possible to appear in a holonic approach are avoided. Theoretical and experimental arguments are supporting the proposed solution.

HAPBA – A Holonic Adaptive Plan-Based Architecture

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