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Abstract
Computational models of motivation have been explored as a means for artificial agents to identify, prioritize and select the goals they will pursue autonomously. This thesis aims to extend current research on models of motivation by investigating the role of motivation in solving optimization problems, with application to task allocation as an example. To achieve this aim, a novel approach that incorporates computational models of motivation into Particle Swarm Optimization (PSO) is proposed. Each particle acts as a self-motivated agent. Three models of motivation, namely achievement, affiliation, and power motivation, are explored. The use of models of motivation enables PSO agents to select the optima that they will pursue autonomously, which has led us to introduce a new class of PSO algorithms, the Motivated Particle Swarm Optimization (MPSO) algorithms. The proposed MPSO algorithms are tested on a range of task allocation problems with different initialization points, varying numbers of tasks, and diverse parameter settings. To evaluate the effectiveness of the algorithms, new behavioral and performance metrics are introduced. Furthermore, this study provides analysis of parameter selection for the new algorithms and evaluates the effectiveness of swarms with different compositions of motivated agents.
Simulation results confirm that agents with different motive profiles exhibit different behavioral characteristics, and that these characteristics have a positive impact on performance. The proposed approach is also shown to improve the performance of existing PSO approaches without motivation, particularly when there is only a small number of agents and when the agents are initialized from a single point, which is the case in many realistic situations.
This thesis makes contributions in three research areas. The first contribution is within the field of motivated learning as it extends the use of computational models of motivation to the optimization domain. The second contribution is in the area of PSO where a new approach that incorporates motivation into PSO settings is introduced to enhance the performance of existing PSO approaches. This thesis also contributes in the domain of task allocation by introducing a new decision making mechanism that permits agents to select a task according to their own motivations.