Evolving a multiagent system for landmark-based robot navigation

Ambastha, Madhur; Busquets, Dídac; Mántaras, Ramon López de; Sierra, Carles

doi:10.1002/int.20079

Cited by 8 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system formulates a model for a complex and large size power system with respect to some desired response time constraints (Khosla and Li 2005). MASs are also used in a landmark navigation system (Ambastha et al 2005). In the navigation system, an MAS-based robot is used to navigate in unknown environments.…”

Section: Introductionmentioning

confidence: 99%

A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

et al. 2010

View full text Add to dashboard Cite

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trustnegotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.

show abstract

Section: Introductionmentioning

confidence: 99%