Creating Effective Task Descriptions from Action Plans

人工智能规划系统正被用于确定各种智能交互系统的活动。这类系统向人类用户解释其计划的能力对于系统的成功采用和使用至关重要。本课题研究计算机计划的自然语言描述的生成。本课题研究任务情境的认知和计算模型及其在动作描述生成中的作用，具体而言，研究如何使用否定约束和证明来创建更有效的任务描述。 该项目的方法包括实验和理论方面;自然发生的文本语料库收集的项目被用来形成一个计算模型，用于生产的计划描述，占上述话语功能。该模型，然后进行实证评估，以确定该模型的efficiency.The工作表明，有效地使用自动生成的计划结构作为基于任务的话语的底层知识表示。 研究结果对培训和教育中使用智能信息技术产生了直接影响。 这使得应用程序能够向本身不是信息技术专家的计算机用户提供上下文敏感的帮助，例如在预先设计的教学材料或其他资源不可用的情况下自动生成指令。

Artificial intelligence planning systems are being put to use to determine the activities of a wide range of intelligent interactive systems. The ability for these kinds of systems to explain their plans to human users is essential for the systems' successful adoption and use. This project is investigating the generation of natural language descriptions of computer plans.This work is developing a cognitive and computational model of task context and its role in the generation of action descriptions, specifically, the means by which negative constraints and justifications are used to create more effective task descriptions. The project methodology includes both experimental and theoretical aspects; naturally occurring text corpora collected by the project is used to form a computational model for the production of plan descriptions that accounts for the discourse features described above. This model is then empirically evaluated to determine the model's efficacy.The work demonstrates the effective use of automatically generated plan structures as underlying knowledge representations for task-based discourse. The results have a direct impact on the use of intelligent information technologies used in training and education. This enables applications that provide context-sensitive help to computer users that are themselves not experts in information technology, for example in the automatic generation of instructions in situations where pre-designed instructional materials or other resources are not available.