A common way of learning to perform a task is to observe how it is carried
out by experts. However, it is well known that for most tasks there is no
unique way to perform them. This is especially noticeable the more complex the
task is because factors such as the skill or the know-how of the expert may
well affect the way she solves the task. In addition, learning from experts
also suffers of having a small set of training examples generally coming from
several experts (since experts are usually a limited and expensive resource),
being all of them positive examples (i.e. examples that represent successful
executions of the task). Traditional machine learning techniques are not useful
in such scenarios, as they require extensive training data. Starting from very
few executions of the task presented as activity sequences, we introduce a
novel inductive approach for learning multiple models, with each one
representing an alternative strategy of performing a task. By an iterative
process based on generalisation and specialisation, we learn the underlying
patterns that capture the different styles of performing a task exhibited by
the examples. We illustrate our approach on two common activity recognition
tasks: a surgical skills training task and a cooking domain. We evaluate the
inferred models with respect to two metrics that measure how well the models
represent the examples and capture the different forms of executing a task
showed by the examples. We compare our results with the traditional process
mining approach and show that a small set of meaningful examples is enough to
obtain patterns that capture the different strategies that are followed to
solve the tasks.

通过学习少量执行任务的示例，我们引入了一种归纳性方法来学习多种模型，每个模型都代表一种执行任务的替代策略。我们将此方法应用于手术技能训练和烹饪领域的常见活动识别任务，并通过评估模型是否能够代表示例并捕捉执行任务的不同形式来验证其有效性。研究结果表明，一小组有意义的示例足以获得捕捉解决任务的不同策略的模式。

学习任务的替代方法

Learning Alternative Ways of Performing a Task

Comment on ``Demystifying Double Robustness: A Comparison of Alternative
Strategies for Estimating a Population Mean from Incomplete Data''
[arXiv:0804.2958]

探讨使用双重稳健法和替代策略估算不完整数据中总体均值的方法。