title: Puarenga Stream alum dosing – summary of effects on lake biota 2013/2014

authors: Ling, Nicholas

abstract: Continuous alum dosing of the Puarenga Stream commenced in early 2010 to reduce inflows of dissolved reactive phosphorus to Lake Rotorua. Analyses of bioaccumulation in the tissues of macrobiota were undertaken in February 2013 and February 2014 for comparison to comparable samples obtained prior to and shortly after the commencement of alum dosing. No significant effects of alum dosing could be distinguished. Bioavailable aluminium in the vicinity of Sulphur Bay appears to be primarily influenced by the major geothermal activity of this region. No significant differences in aluminium bioaccumulation were observed in any species compared to samples obtained prior to the commencement of alum dosing. Continuous alum dosing of the Puarenga Stream since 2010 apparently has not resulted in adverse effects to Lake Rotorua shoreline biota nor caused any apparent increase in aluminium bioaccumulation in lake biota in the vicinity of Sulphur Bay.

text: Puarenga Stream alum dosing – summary of effects on lake biota 2013/2014 Continuous alum dosing of the Puarenga Stream commenced in early 2010 to reduce inflows of dissolved reactive phosphorus to Lake Rotorua. Analyses of bioaccumulation in the tissues of macrobiota were undertaken in February 2013 and February 2014 for comparison to comparable samples obtained prior to and shortly after the commencement of alum dosing. No significant effects of alum dosing could be distinguished. Bioavailable aluminium in the vicinity of Sulphur Bay appears to be primarily influenced by the major geothermal activity of this region. No significant differences in aluminium bioaccumulation were observed in any species compared to samples obtained prior to the commencement of alum dosing. Continuous alum dosing of the Puarenga Stream since 2010 apparently has not resulted in adverse effects to Lake Rotorua shoreline biota nor caused any apparent increase in aluminium bioaccumulation in lake biota in the vicinity of Sulphur Bay.

year: 2014-11

title: Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method

authors: Sarjant, Samuel

subjects: relational reinforcement learning

abstract: Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by performing actions that interact with the environment. The relational representation allows more dynamic environment states than an attribute-based representation of reinforcement learning, but this flexibility also creates new problems such as a potentially infinite number of states.

This thesis describes an RRL algorithm named Cerrla that creates policies directly from a set of learned relational “condition-action” rules using the Cross-Entropy Method (CEM) to control policy creation. The CEM assigns each rule a sampling probability and gradually modifies these probabilities such that the randomly sampled policies consist of ‘better’ rules, resulting in larger rewards received. Rule creation is guided by an inferred partial model of the environment that defines: the minimal conditions needed to take an action, the possible specialisation conditions per rule, and a set of simplification rules to remove redundant and illegal rule conditions, resulting in compact, efficient, and comprehensible policies.

Cerrla is evaluated on four separate environments, where each environment has several different goals. Results show that compared to existing RRL algorithms, Cerrla is able to learn equal or better behaviour in less time on the standard RRL environment. On other larger, more complex environments, it can learn behaviour that is competitive to specialised approaches. The simplified rules and CEM’s bias towards compact policies result in comprehensive and effective relational policies created in a relatively short amount of time.

text: Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by performing actions that interact with the environment. The relational representation allows more dynamic environment states than an attribute-based representation of reinforcement learning, but this flexibility also creates new problems such as a potentially infinite number of states.

This thesis describes an RRL algorithm named Cerrla that creates policies directly from a set of learned relational “condition-action” rules using the Cross-Entropy Method (CEM) to control policy creation. The CEM assigns each rule a sampling probability and gradually modifies these probabilities such that the randomly sampled policies consist of ‘better’ rules, resulting in larger rewards received. Rule creation is guided by an inferred partial model of the environment that defines: the minimal conditions needed to take an action, the possible specialisation conditions per rule, and a set of simplification rules to remove redundant and illegal rule conditions, resulting in compact, efficient, and comprehensible policies.

Cerrla is evaluated on four separate environments, where each environment has several different goals. Results show that compared to existing RRL algorithms, Cerrla is able to learn equal or better behaviour in less time on the standard RRL environment. On other larger, more complex environments, it can learn behaviour that is competitive to specialised approaches. The simplified rules and CEM’s bias towards compact policies result in comprehensive and effective relational policies created in a relatively short amount of time.

year: 2013

title: Researcher-teacher collaboration in Māori-medium education: Aspects of learning for a teacher and researchers in Aotearoa New Zealand when teaching mathematics

authors: Hawera, Ngārewa

subjects: teacher

abstract: This paper describes aspects of learning for a teacher in a Māori-medium Year 7–8 classroom and two mathematics education researchers from a university when they collaborated on a project supporting children to develop their ideas about transformation geometry. Key principles of kaupapa Māori (Māori ideology) methodology such as ako (reciprocal learning), manākitanga (showing care, respect and kindness) and whanaungatanga (family-like relationships) were integral to the initiation and facilitation of the research. Data presented is qualitative and derived largely from wānanga ā-kanohi (face-to-face discussion) between the participants. Results revealed that the collaborative partnership was conducive to developing insights into the complexities of learning mathematics in an indigenous setting. Issues of collegiality, learning mathematics in a second language, teacher mathematical knowledge and appropriate contexts for learning mathematics are analysed and discussed.

text: Researcher-teacher collaboration in Māori-medium education: Aspects of learning for a teacher and researchers in Aotearoa New Zealand when teaching mathematics This paper describes aspects of learning for a teacher in a Māori-medium Year 7–8 classroom and two mathematics education researchers from a university when they collaborated on a project supporting children to develop their ideas about transformation geometry. Key principles of kaupapa Māori (Māori ideology) methodology such as ako (reciprocal learning), manākitanga (showing care, respect and kindness) and whanaungatanga (family-like relationships) were integral to the initiation and facilitation of the research. Data presented is qualitative and derived largely from wānanga ā-kanohi (face-to-face discussion) between the participants. Results revealed that the collaborative partnership was conducive to developing insights into the complexities of learning mathematics in an indigenous setting. Issues of collegiality, learning mathematics in a second language, teacher mathematical knowledge and appropriate contexts for learning mathematics are analysed and discussed.

year: 2014