McLeod Institute of Simulation Sciences
Academic Certificate

PhD degree given by the

Budapest University of Technology and Economics

To:

Gábor Szucs

Supervisor:

Andras Javor

No:

1

Thesis Title

Optimizing and Modeling Methods of Highly Sophisticated Systems by Artificial Intelligence Controlled Simulation, with Special Emphasis on the Investigation of Traffic Systems

Summary
The number of problems solved by simulation is growing persistently. Simulation is a fast and efficient tool for analyzing sophisticated and large systems, besides it can help to determine optimal ones. The efficiency of the traditional simulation cycle can be improved by automating the long iteration process of model building, running simulation, data evaluation and model modification. It can be solved by intelligent demons (agents) monitoring the operational trajectory of the system during simulation, and modifying the model based on their reasoning algorithms. In the dissertation the methods and algorithms of artificial intelligence controlled simulation providing tools for problem solving in case of highly sophisticated systems have been discussed in order to find optimal solutions more effectively. In the development of algorithms searching solutions for complex problems instead of using conventional procedural algorithms, a structure constructed of modular objects based on the demon principle has been elaborated as a mechanism for solving problems in a wide range of applications. Monitoring, decision making, rule based and frame based knowledge bases with priority modules were integrated in this architecture in order to obtain a tool with easy adaptability for different tasks. In the procedure of searching models representing the optimal system beside structural and parametrical modifications the possibility of modifying the influence mechanisms between model objects is reviewed. The architecture presented in the dissertation can be applied in many fields, particularly in highly sophisticated systems, thus in the thesis transport systems are dealt with, where these methodologies can be used successfully. New methods are reviewed, which promote the development of multifacetted simulation models in road, urban and railway transport systems, as well as traffic caused air pollution models. In case of modeling traffic caused air pollution the traffic-emission line sources supply the air-pollutional emission values, and a user oriented place element has been implemented to be used in Knowledge Attributed Petri Nets. The dissertation describes new algorithms for adaptive simulation runlength control. At a given confidence level and interval an adaptive solution taking into account the weight of importance of the various investigated parameters considering a large number of factors is presented.
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