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Design reuse is an approach in which the creation of new designs is based on the identification of previously employed solutions and the incorporation of those into new contexts. This notion has been extensively studied especially by software engineers. This research seeks to support the reuse of design knowledge in the Human-Computer Interaction (HCI) community in creating new designs as it is generally argued that reuse has the potential to reduce development time and costs. Efforts to reuse design elements in HCI, often in the form of design patterns, are slowly emerging. This work seeks to facilitate the reuse of design knowledge in the form of claims. To achieve this goal, the notion of claim relationships—descriptions of connections between claims that emerge in design—is introduced as a mechanism to facilitate reuse. Claims relationships can be used to connect a collection of reusable claims so that they can be searched, understood, tailored, and integrated into new designs. A method for how to use the relationships is presented to aid in the creation of scenarios. Through a series of studies starting from the use of relationships to locate and reuse claims to the use of cards sets incorporating images and rationale for storyboards, the potential for relationships is demonstrated. These works inform the design and evaluation of a storyboarding tool called PIC-UP. PIC-UP is introduced as an example of how relationships can be utilized in the creation of storyboards made of reusable artifacts in the form of claims. Studies of PIC-UP position the tool as one that enables the reuse through the use of a storyboarding guide and social navigation by collecting and sharing claims. It shows potential in aiding novice and non-designers and can serve as a communication tool. / Ph. D.

Generating computer animation from natural language instructions is a complex task that encompasses several key aspects of artificial intelligence including natural language understanding, computer graphics and knowledge representation. Traditionally, this task has been approached using rule based systems which were highly successful on their respective domains, but were difficult to generalise to other domains. In this thesis, I describe the key theories and principles behind a domain-independent machine learning framework for constructing natural language based animation systems, and show how this framework can be more flexible and more powerful than the prevalent rule based approach. / I begin this thesis with a thorough introduction to the goals of the research. I then review the most relevant literature to put this research into perspective. After the literature review, I provide brief descriptions to the most relevant technologies in both natural language processing and computer graphics. I then report original research in semantic role labelling and verb sense disambiguation, followed by a detailed description and analysis of the machine learning framework for natural language based animation generation. / The key contributions of this thesis are: a novel method for performing semantic role labelling of prepositional phrases, a novel method for performing verb sense disambiguation, and a novel machine learning framework for grounding linguistic information in virtual worlds and converting verb-semantic information to computer graphics commands to create computer animation.