In expert-consultation dialogues, it is inevitable that an agent will at times have insufficient information to determine whether to accept or reject a proposal by the other agent. This results in the need for the agent to initiate an information-sharing subdialogue to form a set of shared beliefs within which the agents can effectively re-evaluate the proposal. This paper presents a computational strategy for initiating such information-sharing subdialogues to resolve the system's uncertainty regarding the acceptance of a user proposal. Our model determines when information-sharing should be pursued, selects a focus of information-sharing among multiple uncertain beliefs, chooses the most effective information-sharing strategy, and utilizes the newly obtained information to re-evaluate the user proposal. Furthermore, our model is capable of handling embedded information-sharing subdialogues.
Grosz, Sidner and Lochbaum @cite_7 @cite_1 developed a SharedPlan approach to modelling collaborative discourse, and Sidner formulated an artificial language for modeling such discourse. Sidner viewed a collaborative planning process as proposal acceptance and proposal rejection sequences. Her artificial language treats an utterance such as Why do X? as a proposal for the hearer to provide support for his proposal to do X. However, Sidner's work is descriptive and does not provide a mechanism for determining when and how such a proposal should be made nor how responses should be formulated in information-sharing subdialogues.
@cite_1 @cite_7
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A model of plan recognition in discourse must be based on intended recognition, distinguish each agent's beliefs and intentions from the other's, and avoid assumptions about the correctness or completeness of the agents' beliefs. In this paper, we present an algorithm for plan recognition that is based on the Shared-Plan model of collaboration (Grosz and Sidner, 1990; , 1990) and that satisfies these constraints. This paper addresses the problem of designing a system that accepts a plan structure of the sort generated by AI planning programs and produces natural language text explaining how to execute the plan. We describe a system that generates text from plans produced by the NONLIN planner (Tate 1976).The results of our system are promising, but the texts still lack much of the smoothness of human-generated text. This is partly because, although the domain of plans seems a priori to provide rich structure that a natural language generator can use, in practice a plan that is generated without the production of explanations in mind rarely contains the kinds of information that would yield an interesting natural language account. For instance, the hierarchical organization assigned to a plan is liable to reflect more a programmer's approach to generating a class of plans efficiently than the way that a human would naturally "chunk" the relevant actions. Such problems are, of course, similar to those that Swartout (1983) encountered with expert systems. In addition, AI planners have a restricted view of the world that is hard to match up with the normal semantics of natural language expressions. Thus constructs that are primitive to the planner may be only clumsily or misleadingly expressed in natural language, and the range of possible natural language constructs may be artificially limited by the shallowness of the planner's representations. The paper presents a knowledge representation formalism, in the form of a high-level Action Description Language (ADL) for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specications of the portion of the problem pertaining a single agent. Each of these specications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog. To appear in Theory and Practice of Logic Programming (TPLP). Research partially funded by GNCS-INdAM projects, MUR-PRIN: Innovative and multidisciplinary approaches for constraint and preference reasoning project; NSF grants IIS-0812267 and HRD-0420407; and grants 2009.010.0336 and 2010.011.0403. Most previous work on trainable language generation has focused on two paradigms: (a) using a generation decisions of an existing generator. Both approaches rely on the existence of a handcrafted generation component, which is likely to limit their scalability to new domains. The first contribution of this article is to present Bagel, a fully data-driven generation method that treats the language generation task as a search for the most likely sequence of semantic concepts and realization phrases, according to Factored Language Models (FLMs). As domain utterances are not readily available for most natural language generation tasks, a large creative effort is required to produce the data necessary to represent human linguistic variation for nontrivial domains. This article is based on the assumption that learning to produce paraphrases can be facilitated by collecting data from a large sample of untrained annotators using crowdsourcing—rather than a few domain experts—by relying on a coarse meaning representation. A second contribution of this article is to use crowdsourced data to show how dialogue naturalness can be improved by learning to vary the output utterances generated for a given semantic input. Two data-driven methods for generating paraphrases in dialogue are presented: (a) by sampling from the n-best list of realizations produced by Bagel's FLM reranker; and (b) by learning a structured perceptron predicting whether candidate realizations are valid paraphrases. We train Bagel on a set of 1,956 utterances produced by 137 annotators, which covers 10 types of dialogue acts and 128 semantic concepts in a tourist information system for Cambridge. An automated evaluation shows that Bagel outperforms utterance class LM baselines on this domain. A human evaluation of 600 resynthesized dialogue extracts shows that Bagel's FLM output produces utterances comparable to a handcrafted baseline, whereas the perceptron classifier performs worse. Interestingly, human judges find the system sampling from the n-best list to be more natural than a system always returning the first-best utterance. The judges are also more willing to interact with the n-best system in the future. These results suggest that capturing the large variation found in human language using data-driven methods is beneficial for dialogue interaction.