Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)
Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)
复制标题
通过立方空间先验学习神经符号描述性规划模型：回家之旅（至 STRIPS）
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Christian Muise
中科院分区：
文献类型：
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作者：
Masataro Asai;Christian Muise
我们在使智能体能够自主学习其环境的艰巨任务中取得了新的里程碑。我们的神经符号架构经过端到端的训练，仅从图像中生成简洁有效的离散状态转换模型。我们的目标表示（规划域定义语言）已经以现成的求解器可以使用的形式出现，并为现代启发式搜索功能的丰富阵列打开了大门。我们展示了如何复杂的先天先验，我们放置在学习过程中显着降低了复杂性的学习表示，并揭示了连接到图论的概念“立方体样图”，从而打开了大门，更深入地了解理想的属性学习符号表示。我们证明了强大的独立于域的算法允许我们的系统解决视觉15-Puzzle实例，这些实例超出了盲搜索的范围，而无需求助于强化学习方法，该方法需要对依赖于域的奖励信息进行大量的训练。
We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graph-theoretic notion of ``cube-like graphs'', thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information.
DOI：
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发表时间：
2015
期刊：
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影响因子：
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作者：
Chrpa, L.
通讯作者：
Chrpa, L.
DOI：
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发表时间：
2018-07
期刊：
ArXiv
影响因子：
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作者：
Thanard Kurutach;Aviv Tamar;Ge Yang;Stuart J. Russell;P. Abbeel
通讯作者：
Thanard Kurutach;Aviv Tamar;Ge Yang;Stuart J. Russell;P. Abbeel