We demonstrate the conversational Chatbot platform named Chatti which supports developers with a tool to develop their chatbot easily without full understanding technologies inside a conversational chatbot. To develop a chatbot with Chatti, a developer inputs customized domain data and deploys his Chatbot with a tool. Then users can interact with the Chatbot based on natural language conversation via messengers and so on. Chatti includes natural language understanding, dialog management, action planning, natural language generation and chitchat component which run on g models learned from developers' input data as in common in conversational assistants such as Bixby, Siri, Alexa and etc. With Chatti, the developer could make his Chatbot support two types of conversation simultaneously – basic chitchat and task-oriented dialog. In contrast to prior chatbot building tools are mainly focused on the Natural Language Understanding, Chatti is more focused on full dialog system – dialog management, action planning, natural language generation and chitchat. We believe Chatti could accelerate a wide possibility of conversational Chatbot for services as well as IoT devices.

In this paper, we propose an intelligent dialogue agent for the IoT home. The goal of the proposed system is to efficiently control IoT devices with natural spoken dialogue. This system is made up of the following components: Spoken Language Understanding for analyzing textual input and understanding user intention, Dialogue Management with a State Manager that consists of dialogue policies, Context Manager for understanding the environment, Action Planner responsible for generating a sequence of actions to achieve user intention, Things Manager for observing and controlling IoT devices, and Natural Language Generation that generates natural language from computer-based representation. This system is fully implemented in software and is evaluated in a real IoT home environment.

We introduce a new application of online dialogue analysis: supporting pedagogical assessment of online Q&A discussions. Extending the existing speech act framework, we capture common emotional expressions that often appear in student discussions, such as frustration and degree of certainty, and present a viable approach for the classification. We demonstrate how such dialogue information can be used in analyzing student discussions and identifying difficulties. In particular, the difficulty expressions are aligned to discussion patterns and student performance. We found that frustration occurs more frequently in longer discussions. The students who frequently express frustration tend to get lower grades than others. On the other hand, frequency of high certainty expressions is positively correlated with the performance. We expect such online dialogue analyses can become a powerful assessment tool for instructors and education researchers.

Learning sentiment models from short texts such as tweets is a notoriously challenging problem due to very strong noise and data sparsity. This paper presents a novel, collaborative filtering-based approach for sentiment prediction in twitter conversation threads. Given a set of sentiment holders and sentiment targets, we assume we know the true sentiments for a small fraction of holder-target pairs. This information is then used to predict the sentiment of a previously unknown user towards another user or an entity using collaborative filtering algorithms. We validate our model on two Twitter datasets using different collaborative filtering techniques. Our preliminary results demonstrate that the proposed approach can be effectively used in twitter sentiment prediction, thus mitigating the data sparsity problem.

The goal of the Pedagogical Discourse project is to develop instructional tools that will help students and instructors use discussion boards more effectively, with an emphasis on automatically assessing discussion activities and building tools for promoting student discussion participation and learning. In this paper, we present a two related participation and learning scaffolding tools that exploit natural language processing and information retrieval techniques. The PedaBot tool is designed to aid student knowledge acquisition and promote reflection about course topics by connecting related discussions from a knowledge base of past discussions to the current discussion thread. The MentorMatch tool aims at promoting student participation using student mentors, i.e., course peers with a relatively good understanding of a particular topic. The system identifies students who often provide answers on a given topic and encourages classmates to invite mentors to participate in related discussions. Both tools have been integrated into a live discussion board that is used by an undergraduate computer science course. This paper describes our approaches to applying information retrieval and natural language processing techniques in the development of the tools and presents initial results from instrumentation and survey.

The YODA Robot Project at the University of Southern California/Information Sciences Institute consists of a group of young researchers who share a passion for autonomous systems that can bootstrap its knowledge from real environments by exploration, experimentation, learning, and discovery. Our participation in the Fifth Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence, served as the first milestone in advancing us toward this goal. YODA's software architecture is a hierarchy of abstraction layers, ranging from a set of behaviors at the bottom layer to a dynamic, mission-oriented planner at the top. This abstraction architecture has proven robust in dynamic and noisy environments, as shown by YODA's performance at the robot competition.

The YODA Robot Project at the University of Southern California/Information Sciences Institute consists of a group of young researchers who share a passion for autonomous systems that can bootstrap its knowledge from real environments by exploration, experimentation, learning, and discovery. Our goal is to create a mobile agent that can autonomously learn from its environment based on its own actions, percepts, and mis-sions. Our participation in the Fifth Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence, served as the first milestone in advancing us toward this goal. YODA's software architecture is a hierarchy of abstraction layers, ranging from a set of behaviors at the bottom layer to a dynamic, mission-oriented planner at the top. The planner uses a map of the environment to determine a sequence of goals to be accomplished by the robot and delegates the detailed executions to the set of behaviors at the lower layer. This abstraction architecture has proven robust in dynamic and noisy environments, as shown by YODA's performance at the robot competition.