Cyber Physical Systems (CPS) require a multitude of components interacting among themselves and with the users to perform automatic actions, usually under unpredictable or uncertain conditions. Multi-Agent Systems (MAS) have emerged over the years as one of the major technological paradigms regulating interactions and negotiations among autonomous entities running under heterogeneous conditions. As such, MAS have the potential to support CPS in implementing a highly reconfigurable distributed thinking. However, some gaps are still present between MAS’ features and the strict requirements of CPS. The most relevant is the lack of reliability, which is mainly due to specific features characterizing negotiation protocols. This paper presents a systematic literature review of MAS negotiation protocols aiming at providing a comprehensive overview of their strengths and limitations, examining both the assumptions and requirements set during their development. While this work confirms the potential of MAS in regulating the interactions among CPS components, the findings also highlight the absence of real-time compliance in current negotiation protocols. Strongly characterizing CPS, the capability to face strict time constraints could bridge the gap between MAS and CPS.

Multi-Agent Systems' Negotiation Protocols for Cyber-Physical Systems: Results from a Systematic Literature Review.

One of most primitive problems by unmanned underwater vehicle intelligent swarm (UIS) is coordination control, which has a great significance for realization of target hunting with great performance of efficiency and robustness. Existing studies concentrate on behavior-based centralized or distributed control approaches with the prior knowledge and mostly do not elaborately consider behavior conflicts and constraint differences. Therefore, a novel behavior-driven coordination control framework including topology architecture and swarm control which is inspired by immune mechanism, is investigated for target hunting of heterogeneous UIS under unknown and uncertain environment in this paper. For topology architecture, a hybrid non-central distributed topology is developed as a novel immune-inspired architecture to regulate agents with self-organizational and fault-tolerance features. For swarm control, a dual-layer switching control scheme composed by global control and local control, is proposed to drive behaviors via behavioral-intensity, the trigger of switching is when the target is detected. The global control approach is employed to search target, in which two constraints of energy consumption and healthy-state are considered to achieve good operational reliability. While the local control approach is developed to form the dynamic alliance of tracking and capturing, in which behavioral-intensity control strategy for behavior aggregation and decision-making control strategy for behavior selection are respectively designed to avoid behavior conflicts. Simulation results demonstrate that proposed framework can accomplish hunting under various situations such as hunter agent is random or fixed distribution, and the number of targets asynchronously appears. It is confirmed that our framework is capable of achieving the target hunting under unknown and uncertain environment with greater efficiency and robustness.

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A Behavior-Driven Coordination Control Framework for Target Hunting by UUV Intelligent Swarm

This study examines the pursuit-evasion problem for coordinating multiple robotic pursuers to locate and track a nonadversarial mobile evader in a dynamic environment. Two kinds of pursuit strategies are proposed, one for agents that cooperate with each other and the other for agents that operate independently. This work further employs the probabilistic theory to analyze the uncertain state information about the pursuers and the evaders and uses case-based reasoning to equip agents with memories and learning abilities. According to the concepts of assimilation and accommodation, both positive-angle and bevel-angle strategies are developed to assist agents in adapting to their environment effectively. The case study analysis uses the Recursive Porous Agent Simulation Toolkit (REPAST) to implement a multiagent system and demonstrates superior performance of the proposed approaches to the pursuit-evasion game.

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Multiagent Cooperative Learning Strategies for Pursuit-Evasion Games

Fuzzy reasoning is the very important process in the intelligent systems. Very few papers address the research for interpolative reasoning under multidimensional sparse rules. Moreover, these methods sometimes can not guarantee the convexity of result. Nowadays, multidimensional sparse rules base focus on the premises composed of many fuzzy sets, but do not consider the consequences composed of multidimensional fuzzy sets. Thus the fuzzy production rule can not express the complicate problems in the real world. It needs to be extended. This paper proposes a similarity relation between fuzzy sets. Based on the similarity relation, then we propose an improved fuzzy interpolative reasoning method. Moreover, we extend the method to the case of complex multidimensional consequences.

Multidimensional Fuzzy Interpolative Reasoning Method Based on \lambda-Width Similarity

This paper mainly discusses the pursuit-evasion games, in which a team of autonomous mobile robots act as pursuers to pursue multiple moving targets cooperatively. In order to reduce the communication load during the task negotiation process, the auction mechanism in economics was introduced, and an improved task negotiation process based on multi-unit combinatorial auction was proposed. It allocated task dynamically through capability classification and independent competition. A kind of multi-robot cooperative pursuit algorithm that allows dynamic alliance is proposed. Simulation results show the feasibility and validity of the given algorithm.

Multi-robot cooperative pursuit based on Combinatorial Auction mechanism under dynamic environment

This paper mainly discusses multi-agent pursuit-evasion game: one team of agents pursues another team of agents. Contract net protocol is extended, including improvement of alliance decision and permissively dynamic alliance. The multi-agent cooperative pursuit algorithm is proposed based on the extended contract net protocol. Simulation results have shown the feasibility and validity of the given algorithm.

Yue-Hai Wang

Papers

Multi-agent cooperative pursuit based on extended contract net protocol