"Effectively Enforcing Minimality During Backtrack Search"
Daniel Geschwender
Advisor: Berthe Choueiry
Committee members: Stephen Scott, Hongfeng Yu
Date: April 3, 2018
Time: 10 a.m.
Room: 112 Schorr
Abstract: Constraint Processing is an expressive and powerful framework for modeling and solving combinatorial decision problems. Enforcing consistency during backtrack search is an effective technique for reducing thrashing in a large search tree. The higher the level of the consistency enforced, the stronger the pruning of barren subtrees. Recently, high-level consistencies (HLC) were shown to be instrumental for solving difficult instances. In particular, minimality, which is guaranteed to prune all inconsistent branches, is advantageous even when enforced locally. In this thesis, we study two algorithms for computing minimality and propose three new mechanisms that significantly improve performance. Then, we integrate the resulting algorithms in a portfolio that operates both locally and dynamically during search. Finally, we empirically evaluate the performance of our approach on benchmark problems.
"Modeling and Visualizing Molecular Information in Metabolic Control: a Multi-layer Perspective"
Aditya Immaneni
Advisor: Massimiliano Pierobon
Committee members: Juan Cui, Tomas Hellikar
Date: April 5, 2018
Time: 11 a.m.
Room: 256C Avery
Abstract: The future pervasive communication and computing devices are envisioned to be tightly integrated with biological systems, i.e., the Internet of Bio-Nano Things. In particular, the study and exploitation of existing processes for the biochemical information exchange and elaboration in biological systems are currently at the forefront of this research direction. Molecular Communication (MC), which studies biochemical information systems with theory and tools from computer communication engineering, has been recently proposed to model and characterize the aforementioned processes. Combined with the rapidly growing field of bio-informatics, which creates a rich profusion of biological data and tools to mine the underlying information, this investigation direction is set to produce interesting results and methodologies not only for systems engineering but also for novel scientific discovery. The multidisciplinary nature of this work presents an interesting challenge in terms of creating a structured approach to combine the aforementioned disciplines for the study of metabolic processes in biological organisms, and their relationship with information for their control, optimization, and exploitation. In this thesis, we study these processes at varying levels of complexity, namely, at the system layer, cellular layer and pathway layer. First, we model the overall functionality of a multicellular metabolic system, the human digestion, in term of energy production from major nutrients in the food. Second, we analyze metabolic processes in a single cell and their adaptability to incoming nutrient availability information form the environment. Third, we model and characterize the processes that enable information to propagate from the external environment and be processed by the cell. Numerical results are presented to provide a first proof-of-concept characterization of all these processes in terms of information theory.
"Impact of Users' Locus of Control on Presence and Performance in Telepresence Robot Operation"
Urja Acharya
Advisor: Brittany Duncan
Committee members: Justin Bradley, Carrick Detweiler
Date: April 5, 2018
Time: 10 a.m.
Room: 211 Schorr
Abstract: This thesis presents research investigating the impact of user qualities on presence and performance with the goal of guiding the development of shared autonomy algorithms to adapt to users based on inferred qualities. Previous works in shared autonomy have focused on adapting to a single user in a single interaction or the ability to infer future states but have neglected sensing and adapting in real-time to personal qualities (e.g. locus of control (LOC)) of users. This study collected user commands, performance, and personal data from 60 participants in a telepresence robot driving task to understand their relationship and generate a strategy for shared autonomy systems which adapt to individual users. This work impacts the human-robot interaction community through its expansion of previous findings in the community, and the communities affiliated with robotics and autonomous systems as a whole in order to better adapt to the novice users of the future. This work also stands to tighten integration between the findings of the HRI community and the design of autonomy in systems. The results found that the time taken by “High External” users was 33.89% more and the distance travelled by these users was 27.62% more than that of the “Average” users in restrictive mode. In relaxed mode, presence perceived by “High Internal” users was 4.79% more than that of the “Average” users. It was also found that the “High Internal” users issued 29.12% more commands, had 69.17% more conflicting commands, 33.2% more percentage of command conflicts, took 41.18% longer duration, and travelled 17.74% farther in restrictive mode in comparison to relaxed mode. These results indicate that users with different LOC performed differently in different modes of shared control and users seeking more control fought against autonomy in restrictive mode but performed better in relaxed mode. These findings suggest that user qualities can be inferred from a brief set of interactions and autonomy can potentially be adapted during runtime to improve user performance.