This thesis seeks to address word reasoning problems from a semantic standpoint, proposing a uniform approach for generating solutions while also providing human-understandable explanations. Current state of the art solvers of semantic problems rely on traditional machine learning methods. Therefore their results are not easily reusable by algorithms or interpretable by humans. We propose leveraging web-scale knowledge graphs to determine a semantic frame of interpretation. Semantic knowledge graphs are graphs in which nodes represent concepts and the edges represent the relations between them. Our approach has the following advantages: (1) it reduces the space in which the problem is to be solved; (2) sparse and noisy data can be used without relying only on the relations deducible from the data itself; (3) the output of the inference algorithm is supported by an interpretable justification. We demonstrate our approach in two domains: (1) Topic Modeling: We form topics using connectivity in semantic graphs. We use the same topic models for two very different recommendation systems, one designed for high noise interactive applications and the other for large amounts of web data. (2) Analogy Solving: For humans, analogies are a fundamental reasoning pattern, which relies on abstraction and comparative analysis. In order for an analogy to be understood, precise relations have to be identified and mapped. We introduce graph algorithms to assess the analogy strength in contexts derived from the analogy words. We demonstrate our approach by solving standardized test analogy question.

Current recommendation engines attempt to answer the same question: given a user with some activity in the system, which is the next entity, be it a restaurant, a book or a movie, that the user should visit or buy next. The presumption is that the user would favorably review the item being recommended. The goal of our project is to predict how a user would rate an item he/she never rated, which is a generalization of the task recommendation engines perform. Previous work successfully employs machine learning techniques, particularly statistical methods. However, there are some outlier situations which are more difficult to predict, such as new users. In this paper we present a rating prediction approach targeted for entities for which little prior information exists in the database.We put forward and test a number of hypotheses, exploring recommendations based on nearest neighbor-like methods. We adapt existing common sense topic modeling methods to compute similarity measures between users and then use a relatively small set of key users to predict how the target user will rate a given business. We implemented and tested our system for recommending businesses using the Yelp Academic Dataset. We report initial results for topic-based rating predictions, which perform consistently across a broad range of parameters.

HARMI (Human and Robotic Musical Improvisation) is a software and hardware system that enables musical robots to improvise with human performers. The goal of the system is not to replicate human musicians, but rather to explore the novel kinds of musical expression that machines can produce. At the same time, the system seeks to create spaces where humans and robots can communicate with each other in a common language. To help achieve the former, ideas from contemporary compositional practice and music theory were used to shape the system’s expressive capabilities. In regard to the latter, research from the field of cognitive psychology was incorporated to enable communication, interaction, and understanding between human and robotic performers. The system was partly developed in conjunction with a residency at High Concept Laboratories in Chicago, IL, where a group of human improvisers performed with the robotic instruments. The system represents an approach to the question of how humans and robots can interact and improvise in musical contexts. This approach purports to highlight the unique expressive spaces of humans, the unique expressive spaces of machines, and the shared spaces between the two.

In student modeling, the concept of "mastery learning" i.e. that a student continues to learn a skill till mastery is attained is important. Usually, mastery is defined in terms of most recent student performance. This is also the case with models such as Knowledge Tracing which estimate knowledge solely based on patterns of questions a student gets correct and the task usually is to predict immediate next action of the student. In retrospect however, it is not clear if this is a good definition of mastery since it is perhaps more useful to focus more on student retention over a longer period of time. This paper improves a recently introduced model by Wang and Beck that predicts long term student performance by clustering the students and generating multiple predictions by using a recently developed ensemble technique. Another contribution is that we introduce a novel clustering algorithm we call "Regularity Clustering" and show that it is superior in the task of predicting student retention over more popular techniques such as k-means and Spectral Clustering.

Most learning from demonstration algorithms are implemented with a certain set of variables that are known to be important for the agent. The agent is hardcoded to use those variables for learning the task (or a set of parameters). In this work we try to understand the causal structure of a demonstrated task in order to find: which variables cause what other variables to change, and which variables are independent from the others. We used a realistic simulator to record a simple pick and place task demonstration data, and recovered different causal models using the data in Tetrad, a computer program that searches for causal and statistical models. Our findings show that it is possible to deduce irrelevant variables to a demonstrated task, using the recovered causal structure.

In this paper, we present a set of crowdsourcing and data processing techniques for annotating, segmenting and analyzing spoken dialog data to track topics of discussion between multiple users. Specifically, our system records the dialog between the parent and child as they interact with a reading game on a tablet, crowdsources the audio data to obtain transcribed text, and models topics of discussion from speech transcription using ConceptNet, a freely available commonsense knowledge base. We present preliminary results evaluating our technique using dialog collected using an interactive reading game for children 3-5 years of age. We successfully demonstrate the ability to form discussion topics by grouping words with similar meaning. The presented approach is entirely domain independent and in future work can be applied to a broad range of interactive entertainment applications, such as mobile devices, tablets and games.

In the area of student knowledge assessment, knowledge tracing is a model that has been used for over a decade to predict student knowledge and performance. Many modifications to this model have been proposed and evaluated, however, the modifications are often based on a combination of intuition and experience in the domain. This method of model improvement can be difficult for researchers without high level of domain experience and furthermore, the best improvements to the model could be unintuitive ones. Therefore, we propose a completely data driven approach to model improvement. This alternative allows for researchers to evaluate which aspects of a model are most likely to result in model performance improvement. Our results suggest a variety of different improvements to knowledge tracing many of which have not been explored.

We present an innovative approach for large-scale data collection in human-robot interaction research through the use of online multi-player games. By casting a robotic task as a collaborative game, we gather thousands of examples of human-human interactions online, and then leverage this corpus of action and dialog data to create contextually relevant, social and task-oriented behaviors for human-robot interaction in the real world. We demonstrate our work in a collaborative search and retrieval task requiring dialog, action synchronization and action sequencing between the human and robot partners. A user study performed at the Boston Museum of Science shows that the autonomous robot exhibits many of the same patterns of behavior that were observed in the online dataset and survey results rate the robot similarly to human partners in several critical measures.

While such systems have been shown to successfully support a broad range of interactions, they rely heavily on precoded data. For example, dialogue responses are typically limited to only one or two dozen phrases, which pales in comparison to the diversity of human speech. We believe that in order for robotic systems to become a truly ubiquitous technology, robots must make sense of natural human behavior and engage with humans in a more humanlike way. Robots must become more like humans instead of forcing humans to be more like robots. Much of human knowledge about the appropriateness of behavior, in terms of both speech and actions, comes from our personal experiences and our observations of others. We compare its performance variations form a knowledge base from which to a teleoperated robot following a scripted task we learn what to say and what actions to perform to protocol and examine both the behavior of the achieve certain goals.

The 19th robotics program at the annual AAAI conference was held in Atlanta, Georgia in July 2010. In this article we give a summary of three components of the exhibition: small scale manipulation challenge: robotic chess; the learning by demonstration challenge, and the education track. We also describe the participating teams, highlight the research questions they tackled and briefly describe the systems they demonstrated.