Plan recognition in exploratory domains
Plan recognition in exploratory domains
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DOI：
10.1016/j.artint.2011.09.002
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发表时间：
2012-01-01
影响因子：
14.4
通讯作者：
Grosz, Barbara J.
中科院分区：
文献类型：
--
作者：
Gal, Ya'akov;Reddy, Swapna;Grosz, Barbara J.
本文描述了一个具有挑战性的计划识别问题，在环境中，代理广泛从事探索性行为，并提出了新的算法，有效的计划识别在这样的设置。在探索性领域中，代理的动作映射到行为日志，包括在活动之间切换、无关动作和错误。灵活的教学软件，如本文所考虑的统计教育的应用程序，是一个典型的例子，这些领域，但许多其他设置表现出类似的特点。本文建立了任务的计划识别在探索领域是NP-难的，并比较了几种方法来识别计划在这些领域，包括新的启发式方法，不同的程度上，他们采用回溯，以及减少约束满意度问题。这些算法是根据人们与学校使用的灵活、开放式统计教育软件的互动进行经验评估的。数据是从在实验室环境中使用该软件的成年人以及在课堂上使用该软件的中学生中收集的。约束满足方法是完整的，但比启发式方法慢一个数量级。此外，启发式方法能够在4%的约束满意度的方法从课堂上的学生数据，这反映了软件的预期用户群体。这些结果表明，启发式方法提供了一个很好的平衡性能和计算时间时，认识到人们的活动在教学领域的兴趣。(C)出版社：Elsevier B. V.
This paper describes a challenging plan recognition problem that arises in environments in which agents engage widely in exploratory behavior, and presents new algorithms for effective plan recognition in such settings. In exploratory domains, agents' actions map onto logs of behavior that include switching between activities, extraneous actions, and mistakes. Flexible pedagogical software, such as the application considered in this paper for statistics education, is a paradigmatic example of such domains, but many other settings exhibit similar characteristics. The paper establishes the task of plan recognition in exploratory domains to be NP-hard and compares several approaches for recognizing plans in these domains, including new heuristic methods that vary the extent to which they employ backtracking, as well as a reduction to constraint-satisfaction problems. The algorithms were empirically evaluated on people's interaction with flexible, open-ended statistics education software used in schools. Data was collected from adults using the software in a lab setting as well as middle school students using the software in the classroom. The constraint satisfaction approaches were complete, but were an order of magnitude slower than the heuristic approaches. In addition, the heuristic approaches were able to perform within 4% of the constraint satisfaction approaches on student data from the classroom, which reflects the intended user population of the software. These results demonstrate that the heuristic approaches offer a good balance between performance and computation time when recognizing people's activities in the pedagogical domain of interest. (C) 2011 Published by Elsevier B.V.