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A Bayesian belief network approach for modelling tactical decision-making in a multiple yacht race simulator

A Bayesian belief network approach for modelling tactical decision-making in a multiple yacht race simulator
A Bayesian belief network approach for modelling tactical decision-making in a multiple yacht race simulator
The importance of human factors has to be taken into account when determining a yacht’s performance over a course. The crew’s capabilities of technical skills, athletic performance, and his/her ability of making rational decisions under time pressure and in light of uncertainty of the future wind regime are important aspects that will determine the overall performance of a yacht-crew system. This thesis highlights the performance of such a yacht-crew system with a focus on the decision-making process of sailors. Aspects of human behaviour in sport and the decision-making process are explained considering the level of expertise and possible approaches of how to model them are shown. An artificial intelligence AI -system is developed that is capable of simulating the decision-making process of different sailing behaviours/styles as well as different expertise levels of sailors within a dynamically changing yacht racing environment. The constraints of the multiple fleet racing simulator Robo-Race (Scarponi 2008) were determined using a series of tests with real sailors identified three important constrains: (1) the predictable behaviour of the AI-yachts, (2) the predictable and unrealistic weather model and (3) the simple model describing the effects of yacht interaction. These restrictions and constraints that limited the real and AI-sailors natural sailing behaviour have been successfully removed in the updated version of Robo-Race. The new developed decision-making engine based on Decision Field Theory that uses Bayesian Belief Networks as the perceptual processor showed a clear superiority over the old rule-based decision-making engine. Extensive simulations demonstrate the feasibility of modelling various decision-making processes and therefore different behaviours and expertise levels of sailors. A good comparison was found with that obtained between the Robo-Race results and the Olympic fleet racing events.
Spenkuch, Thomas
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Spenkuch, Thomas
e3503eb2-77ee-4b85-906f-2c89d96ee5f5
Turnock, Stephen
d6442f5c-d9af-4fdb-8406-7c79a92b26ce
Shenoi, Ramanand
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Spenkuch, Thomas (2014) A Bayesian belief network approach for modelling tactical decision-making in a multiple yacht race simulator. University of Southampton, Engineering and the Environment, Doctoral Thesis, 292pp.

Record type: Thesis (Doctoral)

Abstract

The importance of human factors has to be taken into account when determining a yacht’s performance over a course. The crew’s capabilities of technical skills, athletic performance, and his/her ability of making rational decisions under time pressure and in light of uncertainty of the future wind regime are important aspects that will determine the overall performance of a yacht-crew system. This thesis highlights the performance of such a yacht-crew system with a focus on the decision-making process of sailors. Aspects of human behaviour in sport and the decision-making process are explained considering the level of expertise and possible approaches of how to model them are shown. An artificial intelligence AI -system is developed that is capable of simulating the decision-making process of different sailing behaviours/styles as well as different expertise levels of sailors within a dynamically changing yacht racing environment. The constraints of the multiple fleet racing simulator Robo-Race (Scarponi 2008) were determined using a series of tests with real sailors identified three important constrains: (1) the predictable behaviour of the AI-yachts, (2) the predictable and unrealistic weather model and (3) the simple model describing the effects of yacht interaction. These restrictions and constraints that limited the real and AI-sailors natural sailing behaviour have been successfully removed in the updated version of Robo-Race. The new developed decision-making engine based on Decision Field Theory that uses Bayesian Belief Networks as the perceptual processor showed a clear superiority over the old rule-based decision-making engine. Extensive simulations demonstrate the feasibility of modelling various decision-making processes and therefore different behaviours and expertise levels of sailors. A good comparison was found with that obtained between the Robo-Race results and the Olympic fleet racing events.

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Published date: April 2014
Organisations: University of Southampton, Computational Engineering & Design Group

Identifiers

Local EPrints ID: 366587
URI: http://eprints.soton.ac.uk/id/eprint/366587
PURE UUID: 6cbf00d4-8684-405f-8ad4-5716c6fb6ad9
ORCID for Stephen Turnock: ORCID iD orcid.org/0000-0001-6288-0400

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Date deposited: 16 Oct 2014 11:44
Last modified: 01 Apr 2020 00:24

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