User Models: The Problem of Disparity

A signific~mt component of a user raodel in au infornration-seeking dialogue is tim task-related plazl motivating the information-seeker's queries. A number of researchers have Irmdeled the plan inference process and used these models to design more robust natural language interfaces. However in each case, it has been assumed that the system's context model and the plan under construction by the information-seeker are never at variance. This paper addre~es the problem of disparate plans. It presents a four phase approach and argues that hmldling disparate plans requires an enriched context model. This model nmst permit tile addition of companents suggested by the information,'~eeker but not fully supported by the system's domain knowledge, and must differentiate mnong its components according to the kind of support accorded each component as a correct part of the information-seeker's overall plan. It is shown how a component's support should affect the system's hypothesis about the source of error once plan disparity is suggested. I . I N T R O D U C T I O N Corranunication as we know it involves more thml ~inlply answering isolated queries. When two individuals participate in an iuformation-seeking dialogue, tile information-provider uses the context within which each query occurs to interpret the query, determine tile desired information, and formulate an appropriate response. This context consists of more than mere knowledge of the previous questions and answers. A cooperative participant uses the information exchanged during the dialogue and knowledge of the domain to hypothesize a model of the speaker; this model is adjusted and expanded as the dialogue progresses and is called a user model. Perhaps the most significant component of a user model is the listener's belief about the underlying task motivating the information-seeker's queries and his partially developed plan for accomplishing this task. A number of researchers have modeled the plan inference process [Allen 1980], [Cacberry 1983], [Grosz 1977], [Litman 1984], [Perrault 19801, [Robinson 1981], [Sidner 1983], and these models have been used to understand indirect speech acts [Perranlt 1980], provide helpful responses [Allen 1980], interpret pragmatically ill-formed queries [Carberry 1986], understand intersentential ellipsis [Allen 1980, Carberry 1985], and identify the kind of response intended by a speaker [Sidner 1983]. However in each case, four critical assmnptions have been magic: [1] Tile inforroation-seeker's knowledge about the task domain may be lacking but is not erroneous. [2] The infornmtion-seeker's queries never address aspects of the task outside tile system's knowledge. Such systems maintain the closed world assumption [Reiter 1978]. [3] The information provided by the information-seeker is correct a~ld not misleading. [4] The underlying plan inferred by the system prior to analysis of a new utterance is a partially instantiated version of the plan under consideration by the information-seeker. These assumptions eliminate the possibility that tile informationseeker might ask queries irrelevant to the task at hand, that the information..seeker might ask about details outside tile system's limited knowledge, that the information-seeker might accidentally provide misleading information, and that the system might have made erroneous inferences from previous queries. The end result is that tbe system believes that the underlying task-related plan inferred by the system and the task-related plan under construction by the information-seeker are never at variance with one another. If we want systems capable of understanding and appropriately responding to naturally occurring dialogue, natural language interfaces must be able to deal with situations where those assumptions are not true. Our analysis of transcripts of naturally occurring information-seeking dialogues indicates that human participants at tempt to detect inconsistencies in the models and repair them whenever possible. We claim that natural language systenm must do likewise; othe~'ise they will be unable to respond appropriately and cooperatively to dialogue that humans regard as natural. This paper presents a taxonomy of disparate plan models, according to how the model inferrod by the information-provider reflects the information-seeker's model of his task. We claim that plan inference must be extended to include a four phase approach to handling disparate plans ~md that this approach requires a richer model than maintained by current systems. We show how the support that an information-provider accords a component as a correct past of the model affects her hypothesis about the source of error once plan disparity is suggested. 2. T Y P E S OF M O D E L S An information-seeking dialogue contains two participauts, one seeking hfformation and the other attempting to provide that information. Underlying such a dialogue is a task which the information-seeker wants to perform, generally at some time in the future. The information-seeker poses queries in order to obtain the information necessary to construct a plan for accomplishing this task. Examples of such tasks include pursuing a program of study in a university domain, treating a patient in a medical domain, and taking a vacation in a travel domain. A cooperative natural hmguage system must at tempt to infer the underlying task-related plan motivating the information-seeker's queries mad use this plan to provide cooperative, helpful responses [Carberry 1983, 1985]. We call the system's model of this plan a context model. A context model is one component of a user model.