English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Hierarchical planners that produce interpretable and appropriate plans aredesired, especially in its application to supporting human decision making. Inthe typical development of the hierarchical planners, higher-level planners andsymbol grounding functions are manually created, and this manual creationrequires much human effort. In this paper, we propose a framework that canautomatically refine symbol grounding functions and a high-level planner toreduce human effort for designing these modules. In our framework, symbolgrounding and high-level planning, which are based on manually-designedknowledge bases, are modeled with semi-Markov decision processes. A policygradient method is then applied to refine the modules, in which two terms forupdating the modules are considered. The first term, called a reinforcementterm, contributes to updating the modules to improve the overall performance ofa hierarchical planner to produce appropriate plans. The second term, called apenalty term, contributes to keeping refined modules consistent with themanually-designed original modules. Namely, it keeps the planner, which usesthe refined modules, producing interpretable plans. We perform preliminaryexperiments to solve the Mountain car problem, and its results show that amanually-designed high-level planner and symbol grounding function weresuccessfully refined by our framework.

Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients