This paper presents a plan-based architecture for response generation in collaborative consultation dialogues, with emphasis on cases in which the system (consultant) and user (executing agent) disagree. Our work contributes to an overall system for collaborative problem-solving by providing a plan-based framework that captures the Propose-Evaluate-Modify cycle of collaboration, and by allowing the system to initiate subdialogues to negotiate proposed additions to the shared plan and to provide support for its claims. In addition, our system handles in a unified manner the negotiation of proposed domain actions, proposed problem-solving actions, and beliefs proposed by discourse actions. Furthermore, it captures cooperative responses within the collaborative framework and accounts for why questions are sometimes never answered.
Researchers have utilized plan-based mechanisms to generate natural language responses, including explanations @cite_9 @cite_6 @cite_8 . However, they only handle cases in which the user fails to understand the system, instead of cases in which the user disagrees with the system. Maybury developed plan operators for persuasive utterances, but does not provide a framework for negotiation of conflicting views.
@cite_9 @cite_6 @cite_8
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To participate in a dialogue a system must be capable of reasoning about its own previous utterances. Follow-up questions must be interpreted in the context of the ongoing conversation, and the system's previous contributions form part of this context. Furthermore, if a system is to be able to clarify misunderstood explanations or to elaborate on prior explanations, it must understand what it has conveyed in prior explanations. Previous approaches to generating multisentential texts have relied solely on rhetorical structuring techniques. In this paper, we argue that, to handle explanation dialogues successfully, a discourse model must include information about the intended effect of individual parts of the text on the hearer, as well as how the parts relate to one another rhetorically. We present a text planner that records this information and show how the resulting structure is used to respond appropriately to a follow-up question. Abstract Knowledge-based systems that interact with humans often need to define their terminology, elucidate their behavior or support their recommendations or conclusions. In general, they need to explain themselves. Unfortunately, current computer systems, if they can explain themselves at all, often generate explanations that are unnatural, ill-connected or simply incoherent. They typically have only one method of explanation which does not allow them to recover from failed communication. At a minimum, this can irritate an end-user and potentially decrease their productivity. More dangerous, poorly conveyed information may result in misconceptions on the part of the user which can lead to bad decisions or invalid conclusions, which may have costly or even dangerous implications. To address this problem, we analyse human-produced explanations with the aim of transferring explanation expertise to machines. Guided by this analysis, we present a classification of explanatory utterances based on their content and communicative function. We then use these utterance classes and additional text analysis to construct a taxonomy of text types. This text taxonomy characterizes multisentence explanations according to the content they convey, the communicative acts they perform, and their intended effect on the addressee's knowledge, beliefs, goals and plans. We then argue that the act of explanation presentation is an action-based endeavor and introduce and define an integrated theory of communicative acts (rhetorical, illocutionary, and locutionary acts). To illustrate this theory we formalize several of these communicative acts as plan operators and then show their use by a hierarchical text planner (TEXPLAN—Textual EXplanation PLANner) that composes natural language explanations. Finally, we classify a range of reactions readers may have to explanations and illustrate how a system can respond to these given a plan-based approach. Our research thus contributes (1) a domain-independent taxonomy of abstract explanatory utterances, (2) a taxonomy of multisentence explanations based on these utterance classes and (3) a classification of reactions readers may have to explanations as well as (4) an illustration of how these classifications can be applied computationally. Abstract Human verbal explanations are essentially interactive. If someone is giving a complex explanation, the hearer will be given the opportunity to indicate whether they are following as the explanation proceeds, and if necessary interrupt with clarification questions. These interactions allow the speaker to both clear up the hearer's immediate difficulties as they arise, and to update assumptions about their level of understanding. Better models of the hearer's level of understanding in turn allow the speaker to continue the explanation in a more appropriate manner, lessening the risk of continuing confusion. Despite its apparent importance, existing explanation and text generation systems fail to allow for this sort of interaction. Although some systems allow follow-up questions at the end of an explanation, they assume that a complete explanation has been planned and generated before such interactions are allowed. However, for complex explanations interactions with the user should take place as the explanation progresses, and should influence how that explanation continues. This paper describes the EDGE system, which is able to plan complex, extended explanations which allow such interactions with the user. The system can update assumptions about the user's knowledge on the basis of these interactions, and uses this information to influence the detailed further planning of the explanation. When the user appears confused, the system can attempt to fill in missing knowledge or to explain things another way.