论文标题
认识到预测期货的负担能够考虑到非典型的负担效应
Recognising Affordances in Predicted Futures to Plan with Consideration of Non-canonical Affordance Effects
论文作者
论文摘要
我们提出了一个基于负担能力识别和一种神经远期模型的组合来预测负担执行效果的新型动作序列规划系统。通过对预测的未来进行负担能力识别，我们避免依赖多步计划的明确负担效应定义。由于该系统从经验数据中学习了负担能力效果，因此该系统不仅可以预见到负担的规范效应，还可以预见到特定情况的副作用。这使系统能够避免由于这种非规范效应而避免计划故障，并可以利用非规范效应来实现给定目标。我们在一组需要考虑规范和非典型负担效应的测试任务上评估了模拟系统的系统。
We propose a novel system for action sequence planning based on a combination of affordance recognition and a neural forward model predicting the effects of affordance execution. By performing affordance recognition on predicted futures, we avoid reliance on explicit affordance effect definitions for multi-step planning. Because the system learns affordance effects from experience data, the system can foresee not just the canonical effects of an affordance, but also situation-specific side-effects. This allows the system to avoid planning failures due to such non-canonical effects, and makes it possible to exploit non-canonical effects for realising a given goal. We evaluate the system in simulation, on a set of test tasks that require consideration of canonical and non-canonical affordance effects.
