Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems

Intelligent systems are becoming increasingly ubiquitous in daily life. Mobile devices are providing machine-generated support to users, robots are coming out of their cages in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver. However, these interactive intelligent systems\u27 effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner. The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task. People explore their surroundings, make mistakes, and may interrupt an activity to handle more urgent matters. The decisions that an autonomous intelligent system makes should account for such noise and variance regardless of the form of interaction, which includes adapting action choices and possibly its own goals.While most people take these aspects of interaction for granted, they are complex and involve many specific tasks that have primarily been studied independently within artificial intelligence. This results in open-loop interactive experiences where the user must perform a fixed input command or the intelligent system performs a hard-coded output response---one of the components of the interaction cannot adapt with respect to the other for longer-term back-and-forth interactions. This dissertation explores how developments in plan recognition, activity recognition, intent recognition, and autonomous planning can work together to develop more adaptive interactive experiences between autonomous intelligent systems and the people around them. In particular, we consider a unifying perspective of recognition algorithms that provides sufficient information to dynamically produce short-term automated planning problems, and we present ways to run these algorithms faster for the real-time needs of interaction. This exploration leads to the introduction of the Planning and Recognition Together Close the Interaction Loop (PReTCIL) framework that serves as a first step towards identifying how we can address the problem of closing the interaction loop, in addition to new questions that need to be considered

Annual Report 2017-2018

LETTER FROM THE DEAN I am pleased to share with you the College of Computing and Digital Media’s (CDM) 2017-18 annual report, highlighting the many achievements across our community. It was a big year. We began offering five new programs (two bachelor’s, two master’s, and one PhD) across our three schools, in addition to several new certificate programs through our Institute for Professional Development. We built new, cutting-edge spaces to support these and other programs— most notably a 4,500 square-foot makerspace, a robotics and medical engineering lab, an augmented and virtual reality lab, and plans for a cyber-physical systems project lab. Our faculty continued to pursue their research and creative agendas, offering collaborative opportunities with students and partners. CDM students and alumni were celebrated for their many achievements— everything from leading the winning teams at the U.S. Cyber Challenge and Campus 1871 to showcasing their games at juried festivals and winning national screenwriting competitions. We encouraged greater research and teaching collaboration, both between our own schools and with units outside CDM. Design and Computing faculty are working together on an NSA grant for smart home devices that considers both software and interface/design, as well as a new grant-funded game lab. One Project Bluelight film team collaborated with The Theatre School and the School of Music while CDM and College of Science and Health faculty joined forces to research the links between traumatic brain injury, domestic violence, and deep games. It has been exciting and inspiring to witness the accomplishments of our innovative and dedicated community. We are proud to provide the space and resources for them to do their exceptional work. David MillerDean, College of Computing and Digital Mediahttps://via.library.depaul.edu/cdmannual/1001/thumbnail.jp

Narrative Information Extraction with Non-Linear Natural Language Processing Pipelines

Computational narrative focuses on methods to algorithmically analyze, model, and generate narratives. Most current work in story generation, drama management or even literature analysis relies on manually authoring domain knowledge in some specific formal representation language, which is expensive to generate. In this dissertation we explore how to automatically extract narrative information from unannotated natural language text, how to evaluate the extraction process, how to improve the extraction process, and how to use the extracted information in story generation applications. As our application domain, we use Vladimir Propp's narrative theory and the corresponding Russian and Slavic folktales as our corpus. Our hypothesis is that incorporating narrative-level domain knowledge (i.e., Proppian theory) to core natural language processing (NLP) and information extraction can improve the performance of tasks (such as coreference resolution), and the extracted narrative information. We devised a non-linear information extraction pipeline framework which we implemented in Voz, our narrative information extraction system. Finally, we studied how to map the output of Voz to an intermediate computational narrative model and use it as input for an existing story generation system, thus further connecting existing work in NLP and computational narrative. As far as we know, it is the first end-to-end computational narrative system that can automatically process a corpus of unannotated natural language stories, extract explicit domain knowledge from them, and use it to generate new stories. Our user study results show that specific error introduced during the information extraction process can be mitigated downstream and have virtually no effect on the perceived quality of the generated stories compared to generating stories using handcrafted domain knowledge.Ph.D., Computer Science -- Drexel University, 201

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

Autonomous behaviour in tangible user interfaces as a design factor

PhD ThesisThis thesis critically explores the design space of autonomous and actuated artefacts, considering how autonomous behaviours in interactive technologies might shape and influence users’ interactions and behaviours. Since the invention of gearing and clockwork, mechanical devices were built that both fascinate and intrigue people through their mechanical actuation. There seems to be something magical about moving devices, which draws our attention and piques our interest. Progress in the development of computational hardware is allowing increasingly complex commercial products to be available to broad consumer-markets. New technologies emerge very fast, ranging from personal devices with strong computational power to diverse user interfaces, like multi-touch surfaces or gestural input devices. Electronic systems are becoming smaller and smarter, as they comprise sensing, controlling and actuation. From this, new opportunities arise in integrating more sensors and technology in physical objects. These trends raise some specific questions around the impacts smarter systems might have on people and interaction: how do people perceive smart systems that are tangible and what implications does this perception have for user interface design? Which design opportunities are opened up through smart systems? There is a tendency in humans to attribute life-like qualities onto non-animate objects, which evokes social behaviour towards technology. Maybe it would be possible to build user interfaces that utilise such behaviours to motivate people towards frequent use, or even motivate them to build relationships in which the users care for their devices. Their aim is not to increase the efficiency of user interfaces, but to create interfaces that are more engaging to interact with and excite people to bond with these tangible objects. This thesis sets out to explore autonomous behaviours in physical interfaces. More specifically, I am interested in the factors that make a user interpret an interface as autonomous. Through a review of literature concerned with animated objects, autonomous technology and robots, I have mapped out a design space exploring the factors that are important in developing autonomous interfaces. Building on this and utilising workshops conducted with other researchers, I have vi developed a framework that identifies key elements for the design of Tangible Autonomous Interfaces (TAIs). To validate the dimensions of this framework and to further unpack the impacts on users of interacting with autonomous interfaces I have adopted a ‘research through design’ approach. I have iteratively designed and realised a series of autonomous, interactive prototypes, which demonstrate the potential of such interfaces to establish themselves as social entities. Through two deeper case studies, consisting of an actuated helium balloon and desktop lamp, I provide insights into how autonomy could be implemented into Tangible User Interfaces. My studies revealed that through their autonomous behaviour (guided by the framework) these devices established themselves, in interaction, as social entities. They furthermore turned out to be acceptable, especially if people were able to find a purpose for them in their lives. This thesis closes with a discussion of findings and provides specific implications for design of autonomous behaviour in interfaces