Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

Unmanned Underwater Vehicle (UUV) formation system has an important role in the utilization of marine resource. In order to provide an efficient method to research modeling and simulation of UUV formation in the marine environment, the novel approach based on Multi-Agent Interaction Chain was proposed for the UUV formation system. Firstly, Multi-Agent Interaction Chain was analyzed, which mainly considered task and role of UUV in the formation, and the overall modeling process of UUV formation system based on Multi-Agent Interaction Chain was established. Then, the static structure of Multi-Agent Interaction Chain was researched focusing on Hybrid UUV-Agent model structure from the UUV-Agent State-Set and UUV-Agent Rule-Base which were the two aspects to strengthen reliability of interaction chain; the dynamic mechanism of Multi-Agent Interaction Chain was designed, which was focused on collaboration model and communication model through the Adaptive Dynamic Contract Net Protocol and KQML/XML/RTI. Finally, three experiments were established to verify the validity and effectiveness of proposed modeling approach for UUV formation system. Simulation results show the proposed model has good performance, which has important theoretical innovation and application prospects.

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Robust Control Barrier Functions with Sector-Bounded Uncertainties

IEEE Control Systems Letters ◽

10.1109/lcsys.2021.3136653 ◽

2021 ◽

pp. 1-1

Author(s):

Jyot Buch ◽

Shih-Chi Liao ◽

Peter Seiler

Keyword(s):

Robust Control ◽

Barrier Functions ◽

Bounded Uncertainties

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Hybrid Emotionally Aware Mediated Multiagency

Machine Learning ◽

10.4018/978-1-60960-818-7.ch508 ◽

2012 ◽

pp. 1314-1329

Author(s):

Giovanni Vincenti ◽

James Braman

Keyword(s):

Artificial Intelligence ◽

Artificial Life ◽

Consumer Products ◽

Agent Societies ◽

Everyday Lives ◽

Agent Interaction ◽

Multi Agent ◽

Thinking Processes

Emotions influence our everyday lives, guiding and misguiding us. They lead us to happiness and love, but also to irrational acts. Artificial intelligence aims at constructing agents that can emulate thinking processes, but artificial life still lacks emotions and all the consequences that come from them. This work introduces an emotionally aware framework geared towards multi-agent societies. Basing our model on the shoulders of solid foundations created by pioneers who first explored the coupling of emotions and agency, we extend their ideas to include inter-agent interaction and virtual genetics as key components of an agent’s emotive state. We also introduce possible future applications of this framework in consumer products as well as research endeavors.

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High gain feedback robust control for flocking of multi-agents system

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219836465 ◽

2019 ◽

Vol 41 (13) ◽

pp. 3769-3776

Author(s):

Qing Zhang ◽

Jie Wang ◽

Zhengquan Yang ◽

Zengqiang Chen

Keyword(s):

Robust Control ◽

Lyapunov Stability ◽

Nonlinear Dynamic System ◽

High Gain ◽

Multi Agent System ◽

Agent System ◽

Velocity Error ◽

Multi Agent ◽

Theory Of Lyapunov Stability ◽

Multi Agents

This paper focuses on the detailed illustration over the feedback robust control of high gain for flocking of the multi-agent system. As for the second-order unknown bounded nonlinear dynamic system, the designed controller has feedback robust control of high gain. Under the action of the leader, the flocking of the multi-agent system established on the basis of high gain feedback robust control can be realized. By employing the theory of Lyapunov stability, it is observed that the velocity error between agents approaches to zero, and no collision occurs between agents. It is further proved by the simulation that the high gain feedback robust control for flocking of multi-agent system can be obtained accordingly. Compared with Qing et al. (2014), high gain feedback robust control for flocking of multi-agent system has better stability.

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Semantic Interoperability of Geospatial Services

Handbook of Research on Geoinformatics ◽

10.4018/978-1-59140-995-3.ch046 ◽

2010 ◽

pp. 400-421

Author(s):

Iftikhar U. Sikder ◽

Santosh K. Misra

Keyword(s):

Spatial Reasoning ◽

Spatial Information ◽

Resource Integration ◽

Semantic Level ◽

Collaborative Modeling ◽

Multiple Data ◽

Agent Interaction ◽

Agent Collaboration ◽

Multi Agent ◽

Geospatial Services

This article proposes a multi-agent based framework that allows multiple data sources and models to be semantically integrated for spatial modeling in business processing. The paper reviews the feasibility of ontology-based spatial resource integration options to combine the core spatial reasoning with domainspecific application models. We propose an ontology-based framework for semantic level communication of spatial objects and application models. We then introduce a multi-agent system, ontology-based spatial information and resource integration services (OSIRIS), to semantically interoperate complex spatial services and integrate them in a meaningful composition. The advantage of using multi-agent collaboration in OSIRIS is that it obviates the need for end-user analysts to be able to decompose a problem domain to subproblems or to map different models according to what they actually mean. We also illustrate a multi-agent interaction scenario for collaborative modeling of spatial applications using the proposed custom feature of OSIRIS.

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Decentralized Control Barrier Functions for Coupled Multi-Agent Systems under Signal Temporal Logic Tasks

2019 18th European Control Conference (ECC) ◽

10.23919/ecc.2019.8796109 ◽

2019 ◽

Cited By ~ 1

Author(s):

Lars Lindemann ◽

Dimos V. Dimarogonas

Keyword(s):

Temporal Logic ◽

Decentralized Control ◽

Multi Agent Systems ◽

Barrier Functions ◽

Agent Systems ◽

Multi Agent

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On distributed artificial intelligence

The Knowledge Engineering Review ◽

10.1017/s0269888900004367 ◽

1988 ◽

Vol 3 (1) ◽

pp. 21-57 ◽

Cited By ~ 17

Author(s):

Luis Eduardo ◽

Castillo Hern

Keyword(s):

Artificial Intelligence ◽

Conceptual Model ◽

Intelligent Agents ◽

Distributed Artificial Intelligence ◽

Agent Interaction ◽

Multi Agent

AbstractDistributed Artificial Intelligence has been loosely defined in terms of computation by distributed, intelligent agents. Although a variety of projects employing widely ranging methodologies have been reported, work in the field has matured enough to reveal some consensus about its main characteristics and principles. A number of prominent projects are described in detail, and two general frameworks, theSystem conceptual modeland theagent conceptual model, are used to compare the different approaches. The paper concludes by reviewing approaches to formalizing some of the more critical capabilities required by multi-agent interaction.

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