Active Learning of Relational Action Models
Active Learning of Relational Action Models
复制标题
关系动作模型的主动学习
DOI：
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发表时间：
2011
期刊：
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通讯作者：
H. Soldano
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文献类型：
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作者：
Christophe Rodrigues;Pierre Gérard;C. Rouveirol;H. Soldano
我们考虑一个学习关系动作模型的智能体，以便能够预测其动作的效果。该模型由一组类似 STRIPS 的规则组成，即在应用给定操作时预测当前状态发生变化的规则，只要当前状态满足一组先决条件。这里可以将多个规则与给定的操作相关联，因此允许对条件效果进行建模。学习是在线的，因为示例是由代理执行的操作产生的，并且是增量的，因为当前的操作模型每次与他的操作所产生的意外效果相矛盾时都会被修改。模型的形式允许将其用作标准规划者的输入。
在这项工作中，学习单元 IRALe 嵌入到一个集成系统中，该系统能够 i）学习行动模型 ii）选择其行动 iii）计划实现目标。代理使用当前的动作模型来执行主动学习，即选择动作以达到强制模型修订的状态，并使用其规划能力对模型的准确性进行实际评估。
We consider an agent which learns a relational action model in order to be able to predict the effects of his actions. The model consists of a set of STRIPS-like rules, i.e. rules predicting what has changed in the current state when applying a given action as far as a set of preconditions is satisfied by the current state. Here several rules can be associated to a given action, therefore allowing to model conditional effects. Learning is online, as examples result from actions performed by the agent, and incremental, as the current action model is revised each time it is contradicted by unexpected effects resulting from his actions. The form of the model allows using it as an input of standard planners.
In this work, the learning unit IRALe is embedded in an integrated system able to i) learn an action model ii) select its actions iii) plan to reach a goal. The agent uses the current action model to perform active learning, i.e. to select actions with the purpose of reaching states that will enforce a revision of the model, and uses its planning abilities to have a realistic evaluation of the accuracy of the model.