{"paper":{"title":"Conformant Planning via Symbolic Model Checking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"A. Cimatti, M. Roveri","submitted_at":"2011-06-01T16:40:44Z","abstract_excerpt":"We tackle the problem of planning in nondeterministic    domains, by presenting a new approach to conformant planning.    Conformant planning is the problem of finding a sequence of actions    that is guaranteed to achieve the goal despite the nondeterminism of    the domain. Our approach is based on the representation of the    planning domain as a finite state automaton. We use Symbolic Model    Checking techniques, in particular Binary Decision Diagrams, to    compactly represent and efficiently search the automaton. In this    paper we make the following contributions. First, we present a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.0252","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}