HIPRICE-A Hybrid Model for Multi-agent Intelligent Recommendation

Juggler System: Hybrid Model and Implementation

Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications ◽

10.1115/dscc2015-9738 ◽

2015 ◽

Author(s):

Santiago Jiménez-Leudo ◽

Nicanor Quijano ◽

Carlos F. Rodríguez

Keyword(s):

Hybrid Model ◽

Movement Control ◽

Physical Implementation ◽

Oscillating Systems ◽

Specific Objective ◽

Multi Agent ◽

Physical Principles

Several juggling systems have been developed in order to control the dynamics of a bouncing element. This system is useful to show the behavior of elements due to impacts, as well as to have control of the trajectory followed by them based on physical principles. In this article, the description of the physical implementation of a juggler system is presented, including its movement control to achieve a specific objective. A hybrid model to analyze and predict the behavior of both the platform and the ball is studied and implemented. Finally, a multi-agent implementation is proposed to synchronize its movements and show how a group of oscillating systems are able to reach a common work point.

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A hybrid model predictive control scheme for containment and distributed sensing in multi-agent systems

Systems & Control Letters ◽

10.1016/j.sysconle.2013.02.007 ◽

2013 ◽

Vol 62 (5) ◽

pp. 413-419 ◽

Cited By ~ 17

Author(s):

Luca Galbusera ◽

Giancarlo Ferrari-Trecate ◽

Riccardo Scattolini

Keyword(s):

Model Predictive Control ◽

Hybrid Model ◽

Predictive Control ◽

Distributed Sensing ◽

Multi Agent Systems ◽

Agent Systems ◽

Control Scheme ◽

Multi Agent

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A HYBRID MODEL FOR SHARING INFORMATION BETWEEN FUZZY, UNCERTAIN AND DEFAULT REASONING MODELS IN MULTI-AGENT SYSTEMS

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488502001557 ◽

2002 ◽

Vol 10 (04) ◽

pp. 401-450 ◽

Cited By ~ 33

Author(s):

XUDONG LUO ◽

CHENGQI ZHANG ◽

NICHOLAS R. JENNINGS

Keyword(s):

Hybrid Model ◽

Approximate Reasoning ◽

Default Reasoning ◽

Uncertain Reasoning ◽

Uncertain Information ◽

Multi Agent Systems ◽

Unified Framework ◽

Agent Systems ◽

Special Cases ◽

Multi Agent

This paper develops a hybrid model which provides a unified framework for the following four kinds of reasoning: 1) Zadeh's fuzzy approximate reasoning; 2) truth-qualification uncertain reasoning with respect to fuzzy propositions; 3) fuzzy default reasoning (proposed, in this paper, as an extension of Reiter's default reasoning); and 4) truth-qualification uncertain default reasoning associated with fuzzy statements (developed in this paper to enrich fuzzy default reasoning with uncertain information). Our hybrid model has the following characteristics: 1) basic uncertainty is estimated in terms of words or phrases in natural language and basic propositions are fuzzy; 2) uncertainty, linguistically expressed, can be handled in default reasoning; and 3) the four kinds of reasoning models mentioned above and their combination models will be the special cases of our hybrid model. Moreover, our model allows the reasoning to be performed in the case in which the information is fuzzy, uncertain and partial. More importantly, the problems of sharing the information among heterogeneous fuzzy, uncertain and default reasoning models can be solved efficiently by using our model. Given this, our framework can be used as a basis for information sharing and exchange in knowledge-based multi-agent systems for practical applications such as automated group negotiations. Actually, to build such a foundation is the motivation of this paper.

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