Reframe QUT's evaluation framework : a case study of planning, policy and positioning leading to educational best practice

Every university in Australia has a set of policies that guide the institution in its educational practices, however, the policies are often developed in isolation to each other. Now imagine a space where policies are evidence-based, refined annually, cohesively interrelated, and meet stakeholders’ needs. Is this happenstance or the result of good planning? Culturally, Queensland University of Technology (QUT) is a risk-averse institution that takes pride in its financial solvency and is always keen to know “how are we going?” With a twenty-year history of annual reporting that assures the quality of course performance through multiple lines of evidence, QUT’s Learning and Teaching Unit went one step further and strategically aligned a suite of policies that take into consideration the needs of their stakeholders, collaborate with other areas across the institution and use multiple lines of evidence to inform curriculum decision-making. In QUT’s experience, strategic planning can lead to policy that is designed to meet stakeholders’ needs, not manage them; where decision-making is supported by evidence, not rhetoric; where all feedback is incorporated, not ignored; and where policies are cohesively interrelated, not isolated. While many may call this ‘policy nirvana’, QUT has positioned itself to demonstrate good educational practice through Reframe, its evaluation framework. In this case, best practice was achieved through the application of a theory of change and a design-led logic model that allows for transition to other institutions with different cultural specificity. The evaluation approach follows Seldin’s (2003) notion to offer depth and breadth to the evaluation framework along with Berk’s (2005) concept of multiple lines of evidence. In summary, this paper offers university executives, academics, planning and quality staff an opportunity to understand the critical steps that lead to strategic planning and design of evidence-based educational policy that positions a university for best practice in learning and teaching.

Convergence-guaranteed time-varying RRT path planning for profiling floats in 4-Dimensional flow

This paper presents an extension to the Rapidly-exploring Random Tree (RRT) algorithm applied to autonomous, drifting underwater vehicles. The proposed algorithm is able to plan paths that guarantee convergence in the presence of time-varying ocean dynamics. The method utilizes 4-Dimensional, ocean model prediction data as an evolving basis for expanding the tree from the start location to the goal. The performance of the proposed method is validated through Monte-Carlo simulations. Results illustrate the importance of the temporal variance in path execution, and demonstrate the convergence guarantee of the proposed methods.

Practical path planning and obstacle avoidance for autonomous mowing

There is a need for systems which can autonomously perform coverage tasks on large outdoor areas. Unfortunately, the state-of-the-art is to use GPS based localization, which is not suitable for precise operations near trees and other obstructions. In this paper we present a robotic platform for autonomous coverage tasks. The system architecture integrates laser based localization and mapping using the Atlas Framework with Rapidly-Exploring Random Trees path planning and Virtual Force Field obstacle avoidance. We demonstrate the performance of the system in simulation as well as with real world experiments.

Method for planning and executing obstacle-free paths for rotating excavation machinery

This invention concerns the control of rotating excavation machinery, for instance to avoid collisions with obstacles. In a first aspect the invention is a control system for autonomous path planning in excavation machinery, comprising: A map generation subsystem to receive data from an array of disparate and complementary sensors to generate a 3-Dimensional digital terrain and obstacle map referenced to a coordinate frame related to the machine's geometry, during normal operation of the machine. An obstacle detection subsystem to find and identify obstacles in the digital terrain and obstacle map, and then to refine the map by identifying exclusion zones that are within reach of the machine during operation. A collision detection subsystem that uses knowledge of the machine's position and movements, as well as the digital terrain and obstacle map, to identify and predict possible collisions with itself or other obstacles, and then uses a forward motion planner to predict collisions in a planned path. And, a path planning subsystem that uses information from the other subsystems to vary planned paths to avoid obstacles and collisions. In other aspects the invention is excavation machinery including the control system; a method for control of excavation machinery; and firmware and software versions of the control system.

Relocating a flood-affected community : good planning or good politics?

Problem, research strategy and findings: On January 10, 2011, the town of Grantham, Queensland (Australia), was inundated with a flash flood in which 12 of the town's 370 residents drowned. The overall damage bill in Queensland was AUD∃2.38 billion (USD∃2.4 billion) with 35 deaths, and more than three-quarters of the state was declared a flood disaster zone. In this study, we focus on the unusual and even rare decision to relocate Grantham in March 2011. The Lockyer Valley Regional Council (LVRC) acquired a 377-hectare (932-acre) site to enable a voluntary swap of equivalent-sized lots. In addition, planning regulations were set aside to streamline the relocation of a portion of the town. We review the natural hazard literature as it relates to community relocation, state and local government documents related to Grantham, and reports and newspaper articles related to the flood. We also analyze data from interviews with key stakeholders. We document the process of community relocation, assess the relocation process in Grantham against best practice, examine whether the process of community relocation can be upscaled and if the Grantham relocation is an example of good planning or good politics. Takeaway for practice: Our study reveals two key messages for practice. Community relocation (albeit a small one) is possible, and the process can be done quickly; some Grantham residents moved into their new, relocated homes in December 2012, just 11 months after the flood. Moreover, the role of existing planning regulations can be a hindrance to quick action; political leadership, particularly at the local level, is key to implementing the relocation.

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.

ACE research vignette 041: Business Planning and the emergence of new ventures

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. In this vignette, Christophe Garonne and Per Davidsson examine the value of business planning for business start-ups.

Making robust decisions for conservation with restricted money and knowledge

1. In conservation decision-making, we operate within the confines of limited funding. Furthermore, we often assume particular relationships between management impact and our investment in management. The structure of these relationships, however, is rarely known with certainty - there is model uncertainty. We investigate how these two fundamentally limiting factors in conservation management, money and knowledge, impact optimal decision-making. 2. We use information-gap decision theory to find strategies for maximizing the number of extant subpopulations of a threatened species that are most immune to failure due to model uncertainty. We thus find a robust framework for exploring optimal decision-making. 3. The performance of every strategy decreases as model uncertainty increases. 4. The strategy most robust to model uncertainty depends not only on what performance is perceived to be acceptable but also on available funding and the time horizon over which extinction is considered. 5. Synthesis and applications. We investigate the impact of model uncertainty on robust decision-making in conservation and how this is affected by available conservation funding. We show that subpopulation triage can be a natural consequence of robust decision-making. We highlight the need for managers to consider triage not as merely giving up, but as a tool for ensuring species persistence in light of the urgency of most conservation requirements, uncertainty and the poor state of conservation funding. We illustrate this theory by a specific application to allocation of funding to reduce poaching impact on the Sumatran tiger Panthera tigris sumatrae in Kerinci Seblat National Park. © 2008 The Authors.

Optimal allocation of conservation effort among subpopulations of a threatened species: How important is patch quality?

Money is often a limiting factor in conservation, and attempting to conserve endangered species can be costly. Consequently, a framework for optimizing fiscally constrained conservation decisions for a single species is needed. In this paper we find the optimal budget allocation among isolated subpopulations of a threatened species to minimize local extinction probability. We solve the problem using stochastic dynamic programming, derive a useful and simple alternative guideline for allocating funds, and test its performance using forward simulation. The model considers subpopulations that persist in habitat patches of differing quality, which in our model is reflected in different relationships between money invested and extinction risk. We discover that, in most cases, subpopulations that are less efficient to manage should receive more money than those that are more efficient to manage, due to higher investment needed to reduce extinction risk. Our simple investment guideline performs almost as well as the exact optimal strategy. We illustrate our approach with a case study of the management of the Sumatran tiger, Panthera tigris sumatrae, in Kerinci Seblat National Park (KSNP), Indonesia. We find that different budgets should be allocated to the separate tiger subpopulations in KSNP. The subpopulation that is not at risk of extinction does not require any management investment. Based on the combination of risks of extinction and habitat quality, the optimal allocation for these particular tiger subpopulations is an unusual case: subpopulations that occur in higher-quality habitat (more efficient to manage) should receive more funds than the remaining subpopulation that is in lower-quality habitat. Because the yearly budget allocated to the KSNP for tiger conservation is small, to guarantee the persistence of all the subpopulations that are currently under threat we need to prioritize those that are easier to save. When allocating resources among subpopulations of a threatened species, the combined effects of differences in habitat quality, cost of action, and current subpopulation probability of extinction need to be integrated. We provide a useful guideline for allocating resources among isolated subpopulations of any threatened species. © 2010 by the Ecological Society of America.

Robust conservation decision-making

Decision-making for conservation is conducted within the margins of limited funding. Furthermore, to allocate these scarce resources we make assumptions about the relationship between management impact and expenditure. The structure of these relationships, however, is rarely known with certainty. We present a summary of work investigating the impact of model uncertainty on robust decision-making in conservation and how this is affected by available conservation funding. We show that achieving robustness in conservation decisions can require a triage approach, and emphasize the need for managers to consider triage not as surrendering but as rational decision making to ensure species persistence in light of the urgency of the conservation problems, uncertainty, and the poor state of conservation funding. We illustrate this theory by a specific application to allocation of funding to reduce poaching impact on the Sumatran tiger Panthera tigris sumatrae in Kerinci Seblat National Park, Indonesia. To conserve our environment, conservation managers must make decisions in the face of substantial uncertainty. Further, they must deal with the fact that limitations in budgets and temporal constraints have led to a lack of knowledge on the systems we are trying to preserve and on the benefits of the actions we have available (Balmford & Cowling 2006). Given this paucity of decision-informing data there is a considerable need to assess the impact of uncertainty on the benefit of management options (Regan et al. 2005). Although models of management impact can improve decision making (e.g.Tenhumberg et al. 2004), they typically rely on assumptions around which there is substantial uncertainty. Ignoring this 'model uncertainty', can lead to inferior decision-making (Regan et al. 2005), and potentially, the loss of the species we are trying to protect. Current methods used in ecology allow model uncertainty to be incorporated into the model selection process (Burnham & Anderson 2002; Link & Barker 2006), but do not enable decision-makers to assess how this uncertainty would change a decision. This is the basis of information-gap decision theory (info-gap); finding strategies most robust to model uncertainty (Ben-Haim 2006). Info-gap has permitted conservation biology to make the leap from recognizing uncertainty to explicitly incorporating severe uncertainty into decision-making. In this paper we present a summary of McDonald-Madden et al (2008a) who use an info-gap framework to address the impact of uncertainty in the functional representations of biological systems on conservation decision-making. Furthermore, we highlight the importance of two key elements limiting conservation decision-making - funding and knowledge - and how they interact to influence the best management strategy for a threatened species. Copyright © ASCE 2011.

Should we implement monitoring or research for conservation?

[Letter to editor, brief commentary or brief communication ]

Subpopulation triage: How to allocate conservation effort among populations

Threatened species often exist in a small number of isolated subpopulations. Given limitations on conservation spending, managers must choose from strategies that range from managing just one subpopulation and risking all other subpopulations to managing all subpopulations equally and poorly, thereby risking the loss of all subpopulations. We took an economic approach to this problem in an effort to discover a simple rule of thumb for optimally allocating conservation effort among subpopulations. This rule was derived by maximizing the expected number of extant subpopulations remaining given n subpopulations are actually managed. We also derived a spatiotemporally optimized strategy through stochastic dynamic programming. The rule of thumb suggested that more subpopulations should be managed if the budget increases or if the cost of reducing local extinction probabilities decreases. The rule performed well against the exact optimal strategy that was the result of the stochastic dynamic program and much better than other simple strategies (e.g., always manage one extant subpopulation or half of the remaining subpopulation). We applied our approach to the allocation of funds in 2 contrasting case studies: reduction of poaching of Sumatran tigers (Panthera tigris sumatrae) and habitat acquisition for San Joaquin kit foxes (Vulpes macrotis mutica). For our estimated annual budget for Sumatran tiger management, the mean time to extinction was about 32 years. For our estimated annual management budget for kit foxes in the San Joaquin Valley, the mean time to extinction was approximately 24 years. Our framework allows managers to deal with the important question of how to allocate scarce conservation resources among subpopulations of any threatened species. © 2008 Society for Conservation Biology.

Ecosystem management along ephemeral rivers: Trading off socio-economic water supply and vegetation conservation under flood regime uncertainty

In ecosystems driven by water availability, plant community dynamics depend on complex interactions between vegetation, hydrology, and human water resources use. Along ephemeral rivers—where water availability is erratic—vegetation and people are particularly vulnerable to changes in each other's water use. Sensible management requires that water supply be maintained for people, while preserving ecosystem health. Meeting such requirements is challenging because of the unpredictable water availability. We applied information gap decision theory to an ecohydrological system model of the Kuiseb River environment in Namibia. Our aim was to identify the robustness of ecosystem and water management strategies to uncertainties in future flood regimes along ephemeral rivers. We evaluated the trade-offs between alternative performance criteria and their robustness to uncertainty to account for both (i) human demands for water supply and (ii) reducing the risk of species extinction caused by water mining. Increasing uncertainty of flood regime parameters reduced the performance under both objectives. Remarkably, the ecological objective (species coexistence) was more sensitive to uncertainty than the water supply objective. However, within each objective, the relative performance of different management strategies was insensitive to uncertainty. The ‘best’ management strategy was one that is tuned to the competitive species interactions in the Kuiseb environment. It regulates the biomass of the strongest competitor and, thus, at the same time decreases transpiration, thereby increasing groundwater storage and reducing pressure on less dominant species. This robust mutually acceptable strategy enables species persistence without markedly reducing the water supply for humans. This study emphasises the utility of ecohydrological models for resource management of water-controlled ecosystems. Although trade-offs were identified between alternative performance criteria and their robustness to uncertain future flood regimes, management strategies were identified that help to secure an ecologically sustainable water supply.

Curiosity driven reinforcement learning for motion planning on humanoids

Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.