{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:DOX2JW5WEMSDK5ESZRUZNWG7ND","short_pith_number":"pith:DOX2JW5W","schema_version":"1.0","canonical_sha256":"1bafa4dbb62324357492cc6996d8df68f82a00f84550143b15daac3cf6471df8","source":{"kind":"arxiv","id":"1903.08218","version":1},"attestation_state":"computed","paper":{"title":"Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"David Smith, Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati","submitted_at":"2019-03-19T19:08:32Z","abstract_excerpt":"Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans. While there has been a lot of research on generating explanations of solutions to planning problems, explaining the absence of solutions remains an open and under-studied problem, even though such situations can be the hardest to understand or debug. In this paper, we show that hierarchical abstractions can be used to efficiently generate reasons for unsolvability of planning problems. In contrast to related work on computing certificates of unsolvability, we show that these method"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1903.08218","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-03-19T19:08:32Z","cross_cats_sorted":[],"title_canon_sha256":"504c895ff53cc6c5353073cc003f1eb6f37aa897bde2e18c375a74c2f38f5d73","abstract_canon_sha256":"f23c0e2b034166aabd3e6bfd9d991e4470f125f69b2502951a0dc7ea98c42654"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:50.021031Z","signature_b64":"QY4SvvTFrGtRqGZf1mK0Rl094TY+yTb70RBijGJ9KZJplfa+G7SlZGRM5YAegiHDVgO33ag92RCxFOrbKBwACQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1bafa4dbb62324357492cc6996d8df68f82a00f84550143b15daac3cf6471df8","last_reissued_at":"2026-05-17T23:50:50.020228Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:50.020228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"David Smith, Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati","submitted_at":"2019-03-19T19:08:32Z","abstract_excerpt":"Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans. While there has been a lot of research on generating explanations of solutions to planning problems, explaining the absence of solutions remains an open and under-studied problem, even though such situations can be the hardest to understand or debug. In this paper, we show that hierarchical abstractions can be used to efficiently generate reasons for unsolvability of planning problems. In contrast to related work on computing certificates of unsolvability, we show that these method"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.08218","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1903.08218","created_at":"2026-05-17T23:50:50.020377+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.08218v1","created_at":"2026-05-17T23:50:50.020377+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.08218","created_at":"2026-05-17T23:50:50.020377+00:00"},{"alias_kind":"pith_short_12","alias_value":"DOX2JW5WEMSD","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"DOX2JW5WEMSDK5ES","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"DOX2JW5W","created_at":"2026-05-18T12:33:15.570797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DOX2JW5WEMSDK5ESZRUZNWG7ND","json":"https://pith.science/pith/DOX2JW5WEMSDK5ESZRUZNWG7ND.json","graph_json":"https://pith.science/api/pith-number/DOX2JW5WEMSDK5ESZRUZNWG7ND/graph.json","events_json":"https://pith.science/api/pith-number/DOX2JW5WEMSDK5ESZRUZNWG7ND/events.json","paper":"https://pith.science/paper/DOX2JW5W"},"agent_actions":{"view_html":"https://pith.science/pith/DOX2JW5WEMSDK5ESZRUZNWG7ND","download_json":"https://pith.science/pith/DOX2JW5WEMSDK5ESZRUZNWG7ND.json","view_paper":"https://pith.science/paper/DOX2JW5W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.08218&json=true","fetch_graph":"https://pith.science/api/pith-number/DOX2JW5WEMSDK5ESZRUZNWG7ND/graph.json","fetch_events":"https://pith.science/api/pith-number/DOX2JW5WEMSDK5ESZRUZNWG7ND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DOX2JW5WEMSDK5ESZRUZNWG7ND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DOX2JW5WEMSDK5ESZRUZNWG7ND/action/storage_attestation","attest_author":"https://pith.science/pith/DOX2JW5WEMSDK5ESZRUZNWG7ND/action/author_attestation","sign_citation":"https://pith.science/pith/DOX2JW5WEMSDK5ESZRUZNWG7ND/action/citation_signature","submit_replication":"https://pith.science/pith/DOX2JW5WEMSDK5ESZRUZNWG7ND/action/replication_record"}},"created_at":"2026-05-17T23:50:50.020377+00:00","updated_at":"2026-05-17T23:50:50.020377+00:00"}