- B. Miller, C. H. Hwang, Yugyung Lee, Janet Roberts, Alexander I. Rudnicky. 2011. The I 3 S Project : A Mixed , Behavioral and Semantic Approach to Discourse / Dialogue Systems. Abstract: The Intuitive Interfaces to Information Systems (I3S) project at MCC investigated novel approaches to simplifying the construction of spoken dialogue systems. Our goals included designing a system architecture that allows domain independent strategies, such as appropriate conversational gambits, to be separated from domain dependent strategies, such as the most effective prompt to accomplish the immediate task at hand. The system uses plan-based representations for dialogue and domain, and includes such components as a problem solver and plan recognizer. In addition, a new representation called Meta Problem Solving Actions that provides the rationale for problem solving behavior has been introduced to improve overall system behavior and coherency. Other important contributions include the development of a conceptual layer called Interaction Plans that relate Meta Problem Solving Actions to discourse phenomena. We have used these representations to develop an innovative interpretation strategy for user speech acts, using a combination of behavioral and semantic rules and representations to determine the most reasonable interpretation while maintaining real-time response. Our new representations lead to reduced application development and maintenance time. Prototypes have been implemented in an information service-based domain (City Resources). Overview of Current Technology As the amount and complexity of interaction between humans and computers increase, the role of the computer is becoming that of collaboration with humans. A key aspect of this role is the support of mixed-initiative interaction (Allen 1999). In order to properly support such interaction, as well as capture the rationale behind communicative and domain actions, an extensive and flexible framework is required. Common frameworks for dialogue systems built to date include graph-based, frame-based and plan-based. We will first look at the advantages and disadvantages of these systems to motivate our design decisions. * Authors’ current addresses are: Miller– Cycorp, Inc. 3721 Exec. Cntr. Dr., Austin, TX 78731. miller@cyc.com; Lee– CS/Telecom. U of Missouri Kansas City, Kansas City, MO 64110. yugi@cstp.umkc.edu; Roberts – BMC Software, Inc., 10431 Morado Circle Austin, TX 78759. janet_roberts@bmc.com; Rudnicky– School of CS, CMU, Pittsburgh PA 15213, air@cs.cmu.edu; Hwang–MCC, Austin, TX. hwang@mcc.com. First generation spoken dialogue management systems typically involve the construction of conversational flowcharts linking possible dialogue states. Each state specifies a prompt, and enumerates possible user responses that transition the dialogue to a new state (e.g., see (McTear 1998)). A practical difficulty with this approach is that conversational context must be explicitly encoded in the conversational graph. Thus, graph-based systems are exponentially hard in the input domain––i.e., every possible state must be explicitly encoded. Second generation approaches (for example, (Ward and Issar 1994)) substantially simplify this development work by making an assumption that possible interactions with the application can be expressed as a set of frames, e.g., see (Hayes and Reddy 1983), and that interaction in the domain can be driven by the filling out a frame. Given that the parser also outputs semantic frames, the application frame can be filled in with a few rules (e.g., what to do when the parsed frame specifies an already filled in slot) as well as rules for generating a prompt back to the user. Frame-and-Rule systems have a number of advantages over the graph-based model. For one, it is relatively simple to allow for a kind of mixed-initiative interaction when the domain supports keyword or phrase-based parsing. A keyword in the input stream allows the parser to make a fairly good guess as to the corresponding semantic frame, and a deep parse is not required. Prompting is simply based on unfilled slots in the application semantic frame. This allows the following sort of interaction: (S) Where would you like to go? (U) I need to leave sometime after 4pm today. (S) Leaving today after 4pm; where would you like to go? Here the system prompt may have been generated on the basis of having a slot for arrival-city in the application semantic frame. If it is currently unfilled, the rule system may select it as the next thing to ask the user. The user, on the other hand, ignores the prompt and supplies a departure time. So long as the parser can recognize the keyword “leave” and realize that a time has been supplied, it can guess that a departure time has been specified regardless of the deeper meaning of the sentence. If this fills in another unfilled slot in the application frame, the system may enter
