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I developed a framework to support the maintenance of teams of heterogeneous robots operating in complex and dynamic environments such as disaster zones. Given an established team, my work also facilitates the discovery of work to be done during the team's mission and its subsequent assignment to members of the team in a distributed fashion. I evaluated my framework through the development of an example implementation where robots perform exploration in order to locate victims in a simulated disaster environment.

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Traditionally, we've focussed on the question of how to make a system easy to code the first time, or perhaps on how to ease the system's continued evolution. But if we look at life cycle costs, then we must conclude that the important question is how to make a system easy to operate. To do this we need to make it easy for the operators to see what's going on and to then manipulate the system so that it does what it is supposed to. This is a radically different criterion for success.What makes a computer system visible and controllable? This is a difficult question, but it's clear that today's modern operating systems with nearly 50 million source lines of code are neither. Strikingly, the MIT Lisp Machine and its commercial successors provided almost the same functionality as today's mainstream sytsems, but with only 1 Million lines of code. This paper is a retrospective examination of the features of the Lisp Machine hardware and software system. Our key claim is that by building the Object Abstraction into the lowest tiers of the system, great synergy and clarity were obtained.It is our hope that this is a lesson that can impact tomorrow's designs. We also speculate on how the spirit of the Lisp Machine could be extended to include a comprehensive access control model and how new layers of abstraction could further enrich this model.

The next generations of both biological engineering and computer engineering demand that control be exerted at the molecular level. Creating, characterizing and controlling synthetic biological systems may provide us with the ability to build cells that are capable of a plethora of activities, from computation to synthesizing nanostructures. To develop these systems, we must have a set of tools not only for synthesizing systems, but also designing and simulating them. The BioJADE project provides a comprehensive, extensible design and simulation platform for synthetic biology. BioJADE is a graphical design tool built in Java, utilizing a database back end, and supports a range of simulations using an XML communication protocol. BioJADE currently supports a library of over 100 parts with which it can compile designs into actual DNA, and then generate synthesis instructions to build the physical parts. The BioJADE project contributes several tools to Synthetic Biology. BioJADE in itself is a powerful tool for synthetic biology designers. Additionally, we developed and now make use of a centralized BioBricks repository, which enables the sharing of BioBrick components between researchers, and vastly reduces the barriers to entry for aspiring Synthetic Biologists.

We present a constant-factor approximation algorithm for computing anembedding of the shortest path metric of an unweighted graph into atree, that minimizes the multiplicative distortion.

We present a framework for learning abstract relational knowledge with the aimof explaining how people acquire intuitive theories of physical, biological, orsocial systems. Our approach is based on a generative relational model withlatent classes, and simultaneously determines the kinds of entities that existin a domain, the number of these latent classes, and the relations betweenclasses that are possible or likely. This model goes beyond previouspsychological models of category learning, which consider attributesassociated with individual categories but not relationships between categories.We apply this domain-general framework to two specific problems: learning thestructure of kinship systems and learning causal theories.

Examples are a powerful tool for teaching both humans and computers.In order to learn from examples, however, a student must first extractthe examples from its stream of perception. Snapshot learning is ageneral approach to this problem, in which relevant samples ofperception are used as examples. Learning from these examples can inturn improve the judgement of the snapshot mechanism, improving thequality of future examples. One way to implement snapshot learning isthe Top-Cliff heuristic, which identifies relevant samples using ageneralized notion of peaks. I apply snapshot learning with theTop-Cliff heuristic to solve a distributed learning problem and showthat the resulting system learns rapidly and robustly, and canhallucinate useful examples in a perceptual stream from a teacherlesssystem.

This thesis presents a method of object classification using the idea of deformable shape matching. Three types of visual features, geometric blur, C1 and SIFT, are used to generate feature descriptors. These feature descriptors are then used to find point correspondences between pairs of images. Various morphable models are created by small subsets of these correspondences using thin-plate spline. Given these morphs, a simple algorithm, least median of squares (LMEDS), is used to find the best morph. A scoring metric, using both LMEDS and distance transform, is used to classify test images based on a nearest neighbor algorithm. We perform the experiments on the Caltech 101 dataset [5]. To ease computation, for each test image, a shortlist is created containing 10 of the most likely candidates. We were unable to duplicate the performance of [1] in the shortlist stage because we did not use hand-segmentation to extract objects for our training images. However, our gain from the shortlist to correspondence stage is comparable to theirs. In our experiments, we improved from 21% to 28% (gain of 33%), while [1] improved from 41% to 48% (gain of 17%). We find that using a non-shape based approach, C2 [14], the overall classification rate of 33.61% is higher than all of the shaped based methods tested in our experiments.

In this exciting era of cognitive computing, conversational agents have a promising utility and are the subject of this thesis. Conversational agents aim to offer an alternative to traditional methods for humans to engage with technology. This can mean to reduce human effort to complete a task using reasoning capabilities and by exploiting context, or allow voice interaction when traditional methods are not available or inconvenient. This thesis explores technologies that power conversational applications such as virtual assistants, chatbots and conversational agents to gain a deeper understanding of the frameworks used to build them. This thesis introduces Foodie, a conversational kitchen assistant built using IBM Watson technology. The aim of Foodie is to assist families in improving their eating habits through recipe recommendations taking into account personal contexts, such as allergies and dietary goals while helping reduce food waste and managing grocery budgets. This thesis discusses Foodie's architecture and derives a design methodology for building conversational agents. This thesis explores context-aware systems and their representation in conversational applications. Through Foodie, we characterize the contextual data and define its methods of interaction with the application. Foodie reasons using IBM Watson's conversational services to recognize users' intents and understand events related to the users and their context. This thesis discusses our experiences in building conversational agents with Watson, including features that may improve the development experience for creating rich conversations. / Graduate