Enhancing 3D Models with Urban Information: A Case Study Involving Local Authorities and Property Professionals in New Zealand: Quantifying the Benefit of 3D over Alternative 2D systems

<p>This thesis aimed to reach two principal outcomes: To develop a robust testing methodology that allowed a detailed and fair comparative analysis of the benefit, or otherwise, of 3D methods of information interrogation over alternative 2D methods; and to test the ability for a single model to have multiple user-group functionality. The research used the examples of two user-groups within the urban planning industry and their typical decision making processes. A robust testing methodology was developed to investigate the usefulness of 3D in a detailed and focused manner involving individual end-users as participants in a case study. The development of this efficient process assisted the study in moving past the initial visual impact of the models. The method employed a combination of three research instruments: A focus group formed the base from which an urban planning task, questionnaire and guided discussion investigated evidence for the benefit or otherwise of 3D using both quantitative and subjective measures. Two widely disparate user-groups were selected to further test the functionality of a resource to meet the needs of multiple users: city council urban designers and property developers. The research revealed that 3D methods of information visualisation allow users to develop a greater spatial awareness, increasing their understanding of information, when compared to alternative 2D methods. While evidence for this benefit was established using both quantitative and subjective methods, the research proved that this increased understanding does not necessarily lead to quicker decisions as the 2D group completed the task faster and more accurately than the 3D group. The ability for a single model to have multiple user-group functionality was confirmed as each of two disparate user-groups noted that the availability of the other user-group's information was of positive benefit to their understanding of the proposed development within the urban planning task.</p>

Enhancing 3D Models with Urban Information: A Case Study Involving Local Authorities and Property Professionals in New Zealand: Quantifying the Benefit of 3D over Alternative 2D systems

<p>This thesis aimed to reach two principal outcomes: To develop a robust testing methodology that allowed a detailed and fair comparative analysis of the benefit, or otherwise, of 3D methods of information interrogation over alternative 2D methods; and to test the ability for a single model to have multiple user-group functionality. The research used the examples of two user-groups within the urban planning industry and their typical decision making processes. A robust testing methodology was developed to investigate the usefulness of 3D in a detailed and focused manner involving individual end-users as participants in a case study. The development of this efficient process assisted the study in moving past the initial visual impact of the models. The method employed a combination of three research instruments: A focus group formed the base from which an urban planning task, questionnaire and guided discussion investigated evidence for the benefit or otherwise of 3D using both quantitative and subjective measures. Two widely disparate user-groups were selected to further test the functionality of a resource to meet the needs of multiple users: city council urban designers and property developers. The research revealed that 3D methods of information visualisation allow users to develop a greater spatial awareness, increasing their understanding of information, when compared to alternative 2D methods. While evidence for this benefit was established using both quantitative and subjective methods, the research proved that this increased understanding does not necessarily lead to quicker decisions as the 2D group completed the task faster and more accurately than the 3D group. The ability for a single model to have multiple user-group functionality was confirmed as each of two disparate user-groups noted that the availability of the other user-group's information was of positive benefit to their understanding of the proposed development within the urban planning task.</p>

An Intelligent Manufacturing Cell Based on Human-robot Collaboration of Frequent Task Learning for Flexible Manufacturing

The manufacturing industry was moving towards the trend of short run production and personalized customization. That results in the challenge of the efficiency of task adjustment and the complexity of tasks for robots. Thus, this paper developed the intelligent manufacturing cell based on human-robot collaboration(HRC-IMC), combining the intelligence of cobot with that of human. And the intelligent manufacturing cell was composed with the modules of imitating learning, human-robot safety planning, task planning and visual inferring. Moreover, all modules were designed to provide a set of systematic and e ective method which can improve the efficiency of task planning and new task learning. The experimental results indicated that the the efficiency of task adjustment of HRC-IMC can be increased 42.8 % than that of Moveit. All in all, this study is of great significance for improving the efficiency of new task planning of cobots by digitizing the manipulation experience of human.

Strategic Planning for the Chinese American Library Association

This report explored the Chinese American Librarians Association (CALA)’s strategic planning process for 2020-2025 during COVID-19. The paper introduced CALA’s mission statement, values, and discussed the importance of the strategic plan. A strategic planning task force was formed to create an opportunity for membership input in virtual town hall meetings and surveys. The authors presented a case study on the process of revising a strategic plan and discussed the implementation phases. The authors also shared the challenges and recommendations in organizing strategic planning for a library association.

Efficient Lifted Planning with Regression-Based Heuristics

Modern state-of-the-art planners operate by generating a grounded transition system prior to performing search for a solution to a given planning task. Some tasks involve a significant number of objects or entail managing predicates and action schemas with a significant number of arguments. Hence, this instantiation procedure can exhaust all available memory and therefore prevent a planner from performing search to find a solution. This thesis explores this limitation by presenting a benchmark set of problems based on Organic Chemistry Synthesis that was submitted to the latest International Planning Competition (IPC-2018). This benchmark was constructed to gauge the performance of the competing planners given that instantiation is an issue. Furthermore, a novel algorithm, the Regression-Based Heuristic Planner (RBHP), is developed with the aim of averting this issue. RBHP was inspired by the retro-synthetic approach commonly used to solve organic synthesis problems efficiently. RBHP solves planning tasks by applying domain independent heuristics, computed by regression, and performing best-first search. In contrast to most modern planners, RBHP computes heuristics backwards by applying the goal-directed regression operator. However, the best-first search proceeds forward similar to other planners. The proposed planner is evaluated on a set of planning tasks included in previous International Planning Competitions (IPC) against a subset of the top scoring state-of-the-art planners submitted to the IPC-2018.

Efficient Lifted Planning with Regression-Based Heuristics

Modern state-of-the-art planners operate by generating a grounded transition system prior to performing search for a solution to a given planning task. Some tasks involve a significant number of objects or entail managing predicates and action schemas with a significant number of arguments. Hence, this instantiation procedure can exhaust all available memory and therefore prevent a planner from performing search to find a solution. This thesis explores this limitation by presenting a benchmark set of problems based on Organic Chemistry Synthesis that was submitted to the latest International Planning Competition (IPC-2018). This benchmark was constructed to gauge the performance of the competing planners given that instantiation is an issue. Furthermore, a novel algorithm, the Regression-Based Heuristic Planner (RBHP), is developed with the aim of averting this issue. RBHP was inspired by the retro-synthetic approach commonly used to solve organic synthesis problems efficiently. RBHP solves planning tasks by applying domain independent heuristics, computed by regression, and performing best-first search. In contrast to most modern planners, RBHP computes heuristics backwards by applying the goal-directed regression operator. However, the best-first search proceeds forward similar to other planners. The proposed planner is evaluated on a set of planning tasks included in previous International Planning Competitions (IPC) against a subset of the top scoring state-of-the-art planners submitted to the IPC-2018.

Computational Complexity of Computing Symmetries in Finite-Domain Planning

Symmetry-based pruning is a powerful method for reducing the search effort in finitedomain planning. This method is based on exploiting an automorphism group connected to the ground description of the planning task { these automorphisms are known as structural symmetries. In particular, we are interested in the StructSym problem where the generators of this group are to be computed. It has been observed in practice that the StructSym problem is surprisingly easy to solve. We explain this phenomenon by showing that StructSym is GI-complete, i.e., the graph isomorphism problem is polynomial-time equivalent to it and, consequently, solvable in quasi-polynomial time. This implies that it is solvable substantially faster than most computationally hard problems encountered in AI. We accompany this result by identifying natural restrictions of the planning task and its causal graph that ensure that StructSym can be solved in polynomial time. Given that the StructSym problem is GI-complete and thus solvable quite efficiently, it is interesting to analyse if other symmetries (than those that are encompassed by the StructSym problem) can be computed and/or analysed efficiently, too. To this end, we present a highly negative result: checking whether there exists an automorphism of the state transition graph that maps one state s into another state t is a PSPACE-hard problem and, consequently, at least as hard as the planning problem itself.

Supervisor-Worker Problems with an Application in Education

In many e-learning settings, allowing students to choose which skills to practice encourages their motivation and contributes to learning. However, when given choice, students may prefer to practice skills that they already master, rather than practice skills they need to master. On the other hand, requiring students only to practice their required skills may reduce their motivation and lead to dropout. In this paper, we model this tradeoff as a multi-agent planning task, which we call SWOPP (Supervisor- Worker Problem with Partially Overlapping goals), involving two agents—a supervisor (teacher) and a worker (student)—each with different, yet non-conflicting, goals. The supervisor and worker share joint goals (mastering skills). The worker plans to achieve his/her own goals (completing an e-learning session) at a minimal cost (effort required to solve problems). The supervisor guides the worker towards achieving the joint goals by controlling the problems in the choice set for the worker. We provide a formal model for the SWOPP task and two sound and complete algorithms for the supervisor to guide the worker’s plan to achieve their joint goals. We deploy SWOPP for the first time in a real-world study to personalize math questions for K5 students using an e-learning software in schools. We show that SWOPP was able to guide students’ interactions with the software to practice necessary skills without deterring their motivation.

Media Language Planning During a Pandemic – the Influence of Covid-19 on Language Recommendations to Swedish Media in Finland

A global crisis, like the Covid-19 pandemic, can change not only societies but also languages by a great input of new terminology. For speakers of a minority language, media is in a key position to provide them with these new words in their own language. In the case of Finland-Swedish, the Swedish media in Finland is helped by professional language advisers in this language planning task. This study analyses the media language management in Finland-Swedish media, through a content analysis of language recommendations published between February 2020 and April 2021, as well as interviews with media language advisers. The analysis shows that about a quarter of the language recommendations published during these 15 months are coronavirus-related. The topics in the recommendations follow the development of the outbreak in Finland, showing how closely the language advisers work with the news organizations. Contrary to normal situations, the Finland-Swedish media language advisers could not fully rely on the language recommendations from Sweden, due to their different Covid-19 strategies. Instead, the norm authorities were experts in ministries and official institutions, illustrating how language planning is done collectively. The Finland-Swedish journalists rely heavily on the media language recommendations, showing a certain linguistic insecurity, which according to Muhr (2012) is typical for speakers of non-dominant varieties of a pluricentric language.