Semantic Attachments for HTN Planning release_2yu67gnkzraktecbaaurmwf6ym

by Maurício Cecílio Magnaguagno, Felipe Meneguzzi

Published in PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE by Association for the Advancement of Artificial Intelligence (AAAI).

2020   Volume 34, Issue 06, p9933-9940

Abstract

Hierarchical Task Networks (HTN) planning uses a decomposition process guided by domain knowledge to guide search towards a planning task. While many HTN planners allow calls to external processes (e.g. to a simulator interface) during the decomposition process, this is a computationally expensive process, so planner implementations often use such calls in an ad-hoc way using very specialized domain knowledge to limit the number of calls. Conversely, the classical planners that are capable of using external calls (often called semantic attachments) during planning are limited to generating a fixed number of ground operators at problem grounding time. We formalize Semantic Attachments for HTN planning using semi coroutines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner, such as numerical results from a robotics simulator. The resulting planner then uses such coroutines as part of its backtracking mechanism to search through parallel dimensions of the state-space (e.g. through numeric variables). We show empirically that our planner outperforms the state-of-the-art numeric planners in a number of domains using minimal extra domain knowledge.
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Date   2020-04-03
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