Conditional preference networks (CP-nets) model user preferences over objects described in terms of values assigned to discrete features, where the preference for one feature may depend on the values of other features. Most existing algorithms for learning CP-nets from the user's choices assume that the user chooses between pairs of objects. However, many real-world applications involve the the user choosing from all combinatorial possibilities or a very large subset. We introduce a CP-net learning algorithm for the latter type of choice, and study its properties formally and empirically.

This thesis is about a design process for creating educationally relevant tools. I submit that the key to creating tools that are educationally relevant is to focus on ensuring a high degree of alignment between the designed tool and the broader educational context into which the tool will be integrated. The thesis presents methods and processes for creating a tool that is both well aligned and relevant. The design domain of the thesis is described by a set of tools I refer to as “Configurable Embodied Interfaces”. Configurable embodied interfaces have a number of key features, they: Can sense their local surroundings through the detection of such environmental and physical parameters as light, sound, imagery, device acceleration, etc. Act on their local environment by outputting sound, light, imagery, motion of the device, etc. Are configurable in such a way as to link these inputs and outputs in a nearly unlimited number of ways. Contain active ways for users to either directly create new programs linking input and output, or to easily re-configure them by running different programs on them. Are user focused; they assume that a human being is manipulating them in some way, through affecting input and observing output of the interface. Spurred by the growth of cheap computation and sensing, a large number of educational programs have been built around use of configurable embodied interfaces in the last three decades. These programs exist in both formal and informal educational settings and are in use from early childhood through adult and community education. Typically, configurable embodied interfaces are used as tools in three major and sometimes overlapping areas: computer Science education, creative and engineering design education, and traditional science and math education. This work details three examples of collaborations between technologists and educators that led to the creation of educationally successful tools; these three examples share a focus on creating a configurable embodied interface to tackle a specific cognitive and affective set of learning goals, but differ completely in the location of the learning environment, the age and interests of the learners, and the nature of the learning goals. Through the exploration of the methods used, an analysis of the general and context-specific features of the design processes of the three accounts, and a comparison of the process used in this thesis to a conventional engineering design process, this work provides case studies and a set of guidelines that can inform technologists interested in designing educationally relevant embodied interfaces

Mobile robots that encounter people on a regular basis must react to them in some way. While traditional robot control algorithms treat all unexpected sensor readings as objects to be avoided, we argue that robots that operate around people should react socially to those people, following the same social conventions that people use around each other. This thesis presents our COMPANION framework: a Constraint-Optimizing Method for Person–Acceptable NavigatION. COMPANION is a generalized framework for representing social conventions as components of a constraint optimization problem, which is used for path planning and navigation. Social conventions, such as personal space and tending to the right, are described as mathematical cost functions that can be used by an optimal path planner. These social conventions are combined with more traditional constraints, such as minimizing distance, in a flexible way, so that additional constraints can be added easily. We present a set of constraints that specify the social task of traveling around people. We explore the implementation of this task first in simulation, where we demonstrate a robot’s behavior in a wide variety of scenarios. We also detail how a robot’s behavior can be changed by using different relative weights between the constraints or by using constraints representing different sociocultural conventions. We then explore the specific case of passing a person in a hallway, using the robot Grace. Through a user study, we show that people interpret the robot’s behavior according to human social norms, and also that people ascribe different personalities to the robot depending on its level of social behavior. In addition, we present an extension of the COMPANION framework that is able to represent joint tasks between the robot and a person. We identify the constraints necessary to represent the task of having a robot escort a person while traveling side-by-side. In simulation, we show the capability of this representation to produce behaviors such as speeding up or slowing down to travel together around corners, as well as complex maneuvers to travel through narrow chokepoints and return to a side-by-side formation. Finally, we present a newly designed robot, Companion, that is intended as a platform for general social human–robot research. Companion is a holonomic robot, able to move sideways without turning first, which we believe is an important social capability. We detail the design and capabilities of this new platform. As a whole, this thesis demonstrates both a need for, and an implementation and evaluation of, robots that navigate around people according to social norms.

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