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    Evolving and co-evolving meta-level reasoning strategies for multi-agent collaboration : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand
    (Massey University, 2024) Alshehri, Mona Abdulrahman M
    This research presents a novel hybrid evolutionary algorithm for generating meta-level reasoning strategies through computational graphs to solve multi-agent planning and collaboration problems in dynamic environments using only a sparse training set. We enhanced Genetic Network Programming (GNP) by reducing its reliance on randomness, using conflict extractions and optimal search in computational mechanisms to explore nodes more systematically. We incorporated three algorithms into the GNP core. Firstly, we used private conflict kernels to extract conflict-generating structures from graph solutions, which enhances selection, crossover, and mutation operations. Secondly, we enhanced the GNP algorithm by incorporating optimal search and merged Conflict Directed A* with GNP to reduce the search branching factor. We call our novel algorithm Conflict-Directed A* with Genetic Network Programming (CDA*-GNP), which identifies the most effective combination of processing nodes within the graph solution. Additionally, we investigated the use of a chromosome structure with multiple subprograms of varying sizes that the algorithm automatically adjusts. Thirdly, we applied Conflict-Directed A* to a genetically co-evolving heterogeneous cooperative system. A set of agents with diversified computational node composition is evolved to identify the best collection of team members and to efficiently prevent conflicting members from being in the same team. Also, we incorporated methods to enhance the population diversity in each proposed algorithm. We tested the proposed algorithms using four cooperative multi-agent testbeds, including the prey and predator problem and the original tile world problem. More complex multi-agent and multi-task benchmarking testbeds were also introduced for further evaluation. As compared to existing variants of GNP, experimental results show that our algorithm has smoother and more stable fitness improvements across generations. Using the popular tile world problem as a benchmarking testbed, CDA*-GNP achieved better performance results than the best existing variant of GNP for solving the problem. Our algorithm returned 100% accuracy on the test set compared to only 83% reported in the literature. Moreover, CDA*-GNP is 78% faster in terms of the average number of generations and 74% faster in terms of the average number of fitness evaluations. In general, our findings suggest that a hybrid algorithm that balances the utilization of Genetic Network Programming and Optimal strategies leads to the evolution of high-quality solutions faster.
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    Decision markets implementations for human forecasters and multi-agent learning systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand
    (Massey University, 2023) Wang, Wenlong
    Mechanisms of collective decision-making are an increasingly important topic, given that relevant data and information are often distributed. Collective decision-making processes involve eliciting information from multiple agents, aggregating the information, and mapping the aggregated information to a decision. An obstacle to these processes is that information is often proprietary, held by self-interested agents, and sometimes even too sensitive to share. Decision markets are mechanisms for eliciting and aggregating such information into predictions for decision-making. A design for decision markets put forward by Chen, Kash, Ruberry, et al. uses prediction markets to elicit and aggregate predictions that are conditional to the available actions, and then uses a stochastic decision rule to determine, based on the aggregated forecasts, which action to select. The design is incentive-compatible and uses a decision scoring rule to evaluate and incentivise the self-interested agents for their forecasts. The first part of this thesis (Chapter 2) describes a framework for security-based decision markets that allows agents to make predictions by trading assets. Security-based decision markets are designed to be user-friendly for participants familiar with trading in stock markets. For prediction markets, such a framework is well studied. For decision markets, my results show there are important differences between scoring rule based and securities-based implementation. The second and third parts of this thesis (Chapters 3 and 4) investigate decision markets as mechanisms of collective decision-making for multi-agent learning problems, thus building a bridge between economic mechanisms and artificial intelligence. Chapter 3 provides a decision market based algorithm that allows a principal to train multiple autonomous agents with independent and identically distributed (iid) information to solve a contextual bandit problem. Simulation results demonstrate that the proposed multi-agent systems can achieve performance equivalent to a centralised counterpart without requiring direct access to the agents' iid information, which is necessary for the centralised counterpart. Chapter 4 describes a set of mechanisms that allow avoiding stochastic decision rules to select actions based on aggregated forecasts. This is important because committing to a stochastic (i.e., randomising) decision rule means that sometimes suboptimal decisions have to be taken. The mechanisms outlined in this chapter require agents to collectively predict a proxy instead of conditional outcomes. Simulations show that the performance is as good as a Bayesian model with access to all distributed information.
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    Parallel simulation methods for large-scale agent-based predator-prey systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand
    (Massey University, 2019) Quach, Dara (Minh) Quang
    The Animat is an agent-based artificial-life model that is suitable for gaining insight into the interactions of autonomous individuals in complex predator-prey systems and the emergent phenomena they may exhibit. Certain dynamics of the model may only be present in large systems, and a large number of agents may be required to compare with macroscopic models. Large systems can be infeasible to simulate on single-core machines due to processing time required. The model can be parallelised to improve the performance; however, reproducing the original model behaviour and retaining the performance gain is not straightforward. Parallel update strategies and data structures for multi-core CPU and graphical processing units (GPUs) are developed to simulate a typical predator-prey Animat model with improved perfor- mance while reproducing the behaviour of the original model. An analysis is presented of the model to identify dependencies and conditions the parallel update strategy must satisfy to retain original model behaviour. The parallel update strategy for multi-core CPUs is constructed using a spatial domain decomposition approach and supporting data structure. The GPU implementation is developed with a new update strategy that consists of an iterative conflict resolution method and priority number system to simultaneously update many agents with thousands of GPU cores. This update method is supported by a compressed sparse data structure developed to allow for efficient memory transactions. The performance of the Animat simulation is improved with parallelism and without a change in model behaviour. The simulation usability is considered, and an internal agent definition system using a CUDA device Lambda feature is developed to improve the ease of configuring agents without significant changes to the program and loss of performance.