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。这个项目正在研究计算机计划的自然语言描述的生成。这项工作正在开发一个任务上下文的认知和计算模型及其在动作描述生成中的作用，具体地说，是使用负面约束和理由来创建更有效的任务描述的方法。项目方法包括实验和理论两个方面；项目收集的自然文本语料库被用来形成一个计算模型，用于产生说明上述话语特征的计划说明。这项工作证明了自动生成的计划结构作为基于任务的话语的潜在知识表征的有效使用。这些成果对培训和教育中使用的智能信息技术有直接影响。这使得能够向本身不是信息技术专家的计算机用户提供上下文相关帮助的应用程序，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.