In the process of generating discourse, speakers generate utterances which directly achieve the communicative goal of conveying an information item to a hearer, and they also generate utterances which prevent the disruption of correct beliefs maintained by a hearer or the inception of incorrect beliefs. In this paper, we propose a representation scheme which supports a discourse planning mechanism that exhibits both behaviors. Our representation is based on a characterization of commonly occurring impairments to the knowledge acquisition process in terms of a model of a hearer's beliefs. As a testbed of these ideas, a discourse planner called WISHFUL is being implemented in the domain of high-school algebra.

Most Natural Language Generation systems developed to date assume that a user will learn only what is explicitly stated in the discourse. This assumption leads to the generation of discourse that slates explicitly all the information to be conveyed, and that does not address further inferences from the discourse. The content planning mechanism described in this paper addresses these problems by taking into consideration the inferences the user is likely to make from the presented information. These inferences are modeled by means of inference rules, which are applied in a prescriptive manner to generate discourse that conveys the intended information, and in a predictive mode to draw further conclusions from the presented information. In addition, our mechanism minimizes the generated discourse by presenting only information the user does not know or about which s/he has misconceptions. The domain of our implementation is the explanation of concepts in high school algebra.

This paper offers a mechanism for the generation of expressions that convey a concept to a hearer. These expressions often include rhetorical devices, such as Descriptions and Instantiations. Our mechanism is based on the representation of the preconditions for the understanding of a concept in terms of failures in communication. W e distinguish between two types of communication failures based on their cause: failure due to a hearer's inability to evoke an intended concept, and failure due to the hearer's lack of expertise with respect to the aspects of a concept which are necessary for the comprehension of the discourse. This categorization supports the principled selection of rhetorical devices which are tailored to the prevention of undesirable situations that result in the failure of a communicative goal. These ideas are being implemented in a discourse planner in the domain of high-school algebra.

Meta Comments, such as "however," "as I have stated before" and "if you pardon the expression," are pervasive in human discourse. In this paper, we present a predictive mechanism for the generation of Meta Comments based on the tenet that they signal a change in the status of beliefs and expectations a listener is conjectured to possess. In particular, our mechanism anticipates a listener's expectations by activating prescribed inferences on the state of the discourse. It is being implemented in a system called FIGMENT II which generates commentaries on the solution of algebraic equations.

Content planning systems generate explanations to achieve a communicative intent, often with respect to a particular audience. However, current research in con?tent planning does not take into consideration the fact that an addressee may stop paying attention to an expla?nation because of boredom or cognitive overload. In this case, the generated explanation fails to achieve the com?municative intent. In this paper, we present a computa?tional representation of boredom and cognitive overload, and cast the problem of content planning as a constraint?based optimization problem. The objective function in this problem is a probabilistic function of a user's beliefs, and the constraints Jire restrictions pl

During natural language interactions, it is often the case that a set of statements issued by a speaker/writer can be interpreted in a number of ways by a listener/reader. Sometimes the intended interpretation can not be determined by considering only conversation coherence and relevance of the presented information, and specialized domain knowledge m a y be necessary in choosing the intended interpretation. In this paper, we identify the points during the inference process where such specialized knowledge can be successfully applied to aid in assessing the likelihood of an interpretation, and present the results of an inference process that uses domain knowledge, in addition to other factors, such as coherence and relevance, to choose the interpretation intended by the speaker. Our mechanism has been developed for use in task-oriented consultation systems. The particular domain that we have chosen for exploration is that of a travel agency.

In cooperative information-seeking environments, we have observed that the dialogues have the following characteristics: (1) they contain sufficient relevant information, (2) they are coherent, and (3) they are well-structured. In this paper, we describe a mechanism for plan inference which takes advantage of these observed features to reduce the number of alternate interpretations of a user's statements. This reduction is achieved as follows: initially, we take advantage of the relevant information trait by using guiding principles and meta predicates to constrain the number of possible interpretations of a single statement. Discourse coherence considerations are then applied to integrate subsequent statements and drop incoherent interpretations. The retained interpretations are evaluated using a measure of information content, which is used to prefer the interpretations that have more relevant information. The entire mechanism is based on an approach that takes advantage of the well-structured nature of information-seeking dialogues to arrive at the intended interpretation as efficiently as possible.

In order to argue effectively one must have a grasp of both the normative strength of the inferences that come into play and the effect that the proposed inferences will have on the audience. In this paper we describe a program, NAG (Nice Argument Generator), that attempts to generate arguments that are both persuasive and correct. To do so NAG incorporates two models: a normative model, forjudging the normative correctness of an argument, and a user model, for judging the persuasive effect of the same argument upon the user. The user model incorporates some of the common errors humans make when reasoning. In order to limit the scope of its reasoning during argument evaluation and generation NAG explicitly simulates attentional processes in both the user and the normative models.

Analysis of naturally occurring information- seeking dialogues indicates that information providers often query a user when there is insuffi- cient information to formulate a plan that satisfies the user's intentions. In this paper, we present a mechanism that determines when queries are re- quired to elicit additional information from a user and the manner in which these queries should be posed. Query generation is done by taking into account the amount of relevant information in the user's intentions eis recognized by a plan recogni- tion mechanism. The mechanism for query gen- eration described in this paper has been imple- mented as a component of a computerized infor- mation providing system in the travel domain.

Effective arguments must be presented in a cohesive manner: simple collections of believed premises and connecting inferences supporting a goal may not persuade the recipient if they are not well ordered. We use semantic activation and Bayesian propagation in a user model to simulate the effect of presenting an argument generated by our system, NAG, to the user. This simulation is used to select a strategy for presenting the argument to the user. The simulation also identifies superfluous lines of reasoning that may be removed, and enables NAG to determine how multiple subarguments for points should be presented, e.g., as multiple individual supports or collectively. A greedy algorithm is then used to apply probabilistic pruning and semantic suppression to further simplify the argument. Probabilistic pruning removes unnecessary premises from the argument. Semantic suppression is used to select portions of the argument which are within the user's focus of attention, and which are also readily inferred, and hence can be left implicit without damaging the effectiveness of the argument.