scholarly journals Disruption Management in Airline Operations Control – An Intelligent Agent-Based Approach

10.5772/8373 ◽  
2010 ◽  
Author(s):  
Antonio J.M. ◽  
Eugenio Oliveir
Keyword(s):  
Intelligent Agent ◽  
Disruption Management ◽  
Airline Operations ◽  
Agent Based ◽  
Operations Control

scholarly journals A new approach for disruption management in airline operations control

AI Matters ◽  
2014 ◽  
Vol 1 (1) ◽  
pp. 15-16
Author(s):  
Antonio J. M. Castro ◽  
Ana Paula Rocha ◽  
Eugénio Oliveira
Keyword(s):  
Disruption Management ◽  
Airline Operations ◽  
New Approach ◽  
Operations Control

scholarly journals Formal modelling and verification of a multi-agent negotiation approach for airline operations control

2021 ◽  
Author(s):  
Soufiane Bouarfa ◽  
Reyhan Aydoğan ◽  
Alexei Sharpanskykh
Keyword(s):  
Disruption Management ◽  
System Modelling ◽  
Multi Agent System ◽  
Airline Operations ◽  
Agent System ◽  
Formal Modelling ◽  
Agent Negotiation ◽  
New Strategy ◽  
Operations Control ◽  
Multi Agent

AbstractThis paper proposes and evaluates a new airline disruption management strategy using multi-agent system modelling, simulation, and verification. This new strategy is based on a multi-agent negotiation protocol and is compared with three airline strategies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. To evaluate the new strategy, a rule-based multi-agent system model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation strategy outperforms the established strategies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.

scholarly journals Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 681
Author(s):  
László Barna Iantovics
Keyword(s):  
Problem Solving ◽  
Scientific Literature ◽  
Real Life ◽  
Intelligent Agent ◽  
Difficult Problem ◽  
Black Box ◽  
Machine Intelligence ◽  
Agent Based ◽  
Np Hard Problem ◽  
Box Method

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

Sign in / Sign up

Export Citation Format

Share Document