title: Machine Learning for Adaptive Computer Game Opponents

authors: Miles, Jonathan David

subjects: Machine Learning

abstract: This thesis investigates the use of machine learning techniques in 
computer games to create a computer player that adapts to its opponent's 
game-play. This includes first confirming that machine learning 
algorithms can be integrated into a modern computer game without have a 
detrimental effect on game performance, then experimenting with 
different machine learning techniques to maximize the computer player's 
performance. Experiments use three machine learning techniques; static 
prediction models, continuous learning, and reinforcement learning. 
Static models show the highest initial performance but are not able to 
beat a simple opponent. Continuous learning is able to improve the 
performance achieved with static models but the rate of improvement 
drops over time and the computer player is still unable to beat the 
opponent. Reinforcement learning methods have the highest rate of 
improvement but the lowest initial performance. This limits the 
effectiveness of reinforcement learning because a large number of 
episodes are required before performance becomes sufficient to match the 
opponent.

text: Machine Learning for Adaptive Computer Game Opponents This thesis investigates the use of machine learning techniques in 
computer games to create a computer player that adapts to its opponent's 
game-play. This includes first confirming that machine learning 
algorithms can be integrated into a modern computer game without have a 
detrimental effect on game performance, then experimenting with 
different machine learning techniques to maximize the computer player's 
performance. Experiments use three machine learning techniques; static 
prediction models, continuous learning, and reinforcement learning. 
Static models show the highest initial performance but are not able to 
beat a simple opponent. Continuous learning is able to improve the 
performance achieved with static models but the rate of improvement 
drops over time and the computer player is still unable to beat the 
opponent. Reinforcement learning methods have the highest rate of 
improvement but the lowest initial performance. This limits the 
effectiveness of reinforcement learning because a large number of 
episodes are required before performance becomes sufficient to match the 
opponent.

year: 2009

title: Coupling constant metamorphosis and Nth-order symmetries in classical and quantum mechanics

authors: Kalnins, Ernie G.

subjects: mathematics

abstract: We review the fundamentals of coupling constant metamorphosis (CCM) and the Stäckel transform, and apply them to map integrable and superintegrable systems of all orders into other such systems on different manifolds. In general, CCM does not preserve the order of constants of the motion or even take polynomials in the momenta to polynomials in the momenta. We study specializations of these actions which preserve polynomials and also the structure of the symmetry algebras in both the classical and quantum cases. We give several examples of non-constant curvature third- and fourth-order superintegrable systems in two space dimensions obtained via CCM, with some details on the structure of the symmetry algebras preserved by the transform action.

text: Coupling constant metamorphosis and Nth-order symmetries in classical and quantum mechanics We review the fundamentals of coupling constant metamorphosis (CCM) and the Stäckel transform, and apply them to map integrable and superintegrable systems of all orders into other such systems on different manifolds. In general, CCM does not preserve the order of constants of the motion or even take polynomials in the momenta to polynomials in the momenta. We study specializations of these actions which preserve polynomials and also the structure of the symmetry algebras in both the classical and quantum cases. We give several examples of non-constant curvature third- and fourth-order superintegrable systems in two space dimensions obtained via CCM, with some details on the structure of the symmetry algebras preserved by the transform action.

year: 2010

title: Everyday experiences of racism: Suppressing Māori cultural expressions in mental health

authors: Southey, Kim Marie

abstract: Tēnā kautau i runga i ngā tini āhuatanga o te wā.

This publication has been produced as part of the He Kokonga Ngākau Fellowships, an initiative of Te Atawhai o Te Ao that seeks to support and contribute to our research projects, and grow Māori research capability that promotes Kaupapa Māori research. These fellowships have been established to support Māori postgraduate students, practitioners, community researchers, and writers in their writing on kaupapa that have relevance to our organisation and wider community.

Dr Kim Southey (Ngāti Porou, Ngāti Kuia) draws on her doctoral research on re-presenting Māori and Indigenous understandings of being, and deconstructing the notion of mental illness. This
publication focuses on how Māori and Indigenous expressions within the mental (and wider) health system are suppressed and restricted by a dominant (western) worldview. Dr Southey posits that the western insistence on representing things in the world through rational, logical explanations impacts on Māori cultural understandings, limiting how we can express ourselves. This is viewed by Dr Southey as a form of institutional racism.

text: Everyday experiences of racism: Suppressing Māori cultural expressions in mental health Tēnā kautau i runga i ngā tini āhuatanga o te wā.

This publication has been produced as part of the He Kokonga Ngākau Fellowships, an initiative of Te Atawhai o Te Ao that seeks to support and contribute to our research projects, and grow Māori research capability that promotes Kaupapa Māori research. These fellowships have been established to support Māori postgraduate students, practitioners, community researchers, and writers in their writing on kaupapa that have relevance to our organisation and wider community.

Dr Kim Southey (Ngāti Porou, Ngāti Kuia) draws on her doctoral research on re-presenting Māori and Indigenous understandings of being, and deconstructing the notion of mental illness. This
publication focuses on how Māori and Indigenous expressions within the mental (and wider) health system are suppressed and restricted by a dominant (western) worldview. Dr Southey posits that the western insistence on representing things in the world through rational, logical explanations impacts on Māori cultural understandings, limiting how we can express ourselves. This is viewed by Dr Southey as a form of institutional racism.

year: 2022