Learning Unknown Event Models
Learning Unknown Event Models
复制标题
学习未知事件模型
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
D. Aha
中科院分区：
文献类型：
--
作者：
M. Molineaux;D. Aha
具有不完整环境模型的代理可能会感到惊讶，这代表了学习的机会。我们调查的方法，位于代理检测惊喜，区分不同形式的惊喜，并假设新的模型的未知事件，让他们感到惊讶。我们实例化这些方法在一个新的目标推理代理（名为FoolMeTwice），研究其在模拟研究中的性能，并报告说，它产生的计划，显着降低执行成本相比，不学习模型的令人惊讶的事件。
Agents with incomplete environment models are likely to be surprised, and this represents an opportunity to learn. We investigate approaches for situated agents to detect surprises, discriminate among different forms of surprise, and hypothesize new models for the unknown events that surprised them. We instantiate these approaches in a new goal reasoning agent (named FoolMeTwice), investigate its performance in simulation studies, and report that it produces plans with significantly reduced execution cost in comparison to not learning models for surprising events.