Sandra Carberry

Techniques for Plan Recognition

Knowing a users plans and goals can significantly improve the effectiveness of an interactive system. However, recognizing such goals and the users intended plan for achieving them is not an easy task. Although much research has dealt with representing the knowledge necessary for plan inference and developing strategies that hypothesize the users evolving plans, a number of serious problems still impede the use of plan recognition in large-scale, real-world applications. This paper describes the various approaches that have been taken to plan inference, along with techniques for dealing with ambiguity, robustness, and representation of requisite domain knowledge, and discusses areas for further research.

A TRIPARTITE PLAN-BASED MODEL OF DIALOGUE

This paper presents a tripartite model of dialogue in which three different kinds of actions are modeled: domain actions, problem-solving actions, and discourse or communicative actions. We contend that our process model provides a more finely differentiated representation of user intentions than previous models; enables the incremental recognition of communicative actions that cannot be recognized from a single utterance alone; and accounts for implicit acceptance of a communicated proposition.

Dialogue Act Tagging with Transformation-Based Learning

For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice. We present strategies for constructing a set of dialogue act cues automatically by minimizing the entropy of the distribution of dialogue acts in a training corpus, filtering out irrelevant dialogue act cues, and clustering semantically-related words. In addition, to address limitations of TBL, we introduce a Monte Carlo strategy for training efficiently and a committee method for computing confidence measures. These ideas are combined in our working implementation, which labels held-out data as accurately as any other reported system for the dialogue act tagging task.

MODELING NEGOTIATION SUBDIALOGUES

This paper presents a plan-based model that handles negotiation subdialogues by inferring both the communicative actions that people pursue when speaking and the beliefs underlying these actions. We contend that recognizing the complex discourse actions pursued in negotiation subdialogues (e.g., expressing doubt) requires both a multistrength belief model and a process model that combines different knowledge sources in a unified framework. We show how our model identifies the structure of negotiation subdialogues, including recognizing expressions of doubt, implicit acceptance of communicated propositions, and negotiation subdialogues embedded within other negotiation subdialogues.

Information graphics: an untapped resource for digital libraries

Information graphics are non-pictorial graphics such as bar charts and line graphs that depict attributes of entities and relations among entities. Most information graphics appearing in popular media have a communicative goal or intended message; consequently, information graphics constitute a form of language. This paper argues that information graphics are a valuable knowledge resource that should be retrievable from a digital library and that such graphics should be taken into account when summarizing a multimodal document for subsequent indexing and retrieval. But to accomplish this, the information graphic must be understood and its message recognized. The paper presents our Bayesian system for recognizing the primary message of one kind of information graphic (simple bar charts) and discusses the potential role of an information graphics message in indexing graphics and summarizing multimodal documents.

Response Generation in Collaborative Negotiation

In collaborative planning activities, since the agents are autonomous and heterogenous, it is inevitable that conflicts arise in their beliefs during the planning process. In cases where such conflicts are relevant to the task at hand, the agents should engage in collaborative negotiation as an attempt to square away the discrepancies in their beliefs. This paper presents a computational strategy for detecting conflicts regarding proposed beliefs and for engaging in collaborative negotiation to resolve the conflicts that warrant resolution. Our model is capable of selecting the most effective aspect to address in its pursuit of conflict resolution in cases where multiple conflicts arise, and of selecting appropriate evidence to justify the need for such modification. Furthermore, by capturing the negotiation process in a recursive Propose-Evaluate-Modify cycle of actions, our model can successfully handle embedded negotiation subdialogues.

Increasing high school girls' self confidence and awareness of CS through a positive summer experience

This paper describes the design, implementation, and impact evaluation of a summer program designed to attract high school girls to entering an information technology field for their college major. Our main contributions include an analysis of immediate and longer term surveys from both the student participants and the female teaching assistants, curriculum and pedagogy highlights of the program, and lessons learned from the planning and implementation experiences.

A HYBRID REASONING MODEL FOR INDIRECT ANSWERS

This paper presents our implemented computational model for interpreting and generating indirect answers to Yes-No questions. Its main features are 1) a discourse-plan-based approach to implicature, 2) a reversible architecture for generation and interpretation, 3) a hybrid reasoning model that employs both plan inference and logical inference, and 4) use of stimulus conditions to model a speakers motivation for providing appropriate, unrequested information. The model handles a wider range of types of indirect answers than previous computational models and has several significant advantages.

Recognizing the intended message of line graphs

Information graphics (line graphs, bar charts, etc.) that appear in popular media, such as newspapers and magazines, generally have a message that they are intended to convey. We contend that this message captures the high-level knowledge conveyed by the graphic and can serve as a brief summary of the graphics content. This paper presents a system for recognizing the intended message of a line graph. Our methodology relies on 1)segmenting the line graph into visually distinguishable trends which are used to suggest possible messages, and 2)extracting communicative signals from the graphic and using them as evidence in a Bayesian Network to identify the best hypothesis about the graphics intended message. Our system has been implemented and its performance has been evaluated on a corpus of line graphs.

Constructing and Utilizing a Model of User Preferences in Collaborative Consultation Dialogues

A natural language collaborative consultation system must take user preferences into account. A model of user preferences allows a system to appropriately evaluate alternatives using criteria of importance to the user. Additionally, decision research suggests both that an accurate model of user preferences could enable the system to improve a users decision‐making by ensuring that all important alternatives are considered, and that such a model of user preferences must be built dynamically by observing the users actions during the decision‐making process. This paper presents two strategies: one for dynamically recognizing user preferences during the course of a collaborative planning dialogue and the other for exploiting the model of user preferences to detect suboptimal solutions and suggest better alternatives. Our recognition strategy utilizes not only the utterances themselves but also characteristics of the dialogue in developing a model of user preferences. Our generation strategy takes into account both the strength of a preference and the closeness of a potential match in evaluating actions in the users plan and suggesting better alternatives. By modeling and utilizing user preferences, our system is able to fulfill its role as a collaborative agent.