{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:TGVULPTKOMW62XLLPTM42LJIXU","short_pith_number":"pith:TGVULPTK","schema_version":"1.0","canonical_sha256":"99ab45be6a732ded5d6b7cd9cd2d28bd14f86f889a6358d8e91300d6231e1c69","source":{"kind":"arxiv","id":"2011.00524","version":2},"attestation_state":"computed","paper":{"title":"Towards Personalized Explanation of Robot Path Planning via User Feedback","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Kayla Boggess, Lu Feng, Shenghui Chen","submitted_at":"2020-11-01T15:10:43Z","abstract_excerpt":"Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaboration. In this paper, we present a system for generating personalized explanations of robot path planning via user feedback. We consider a robot navigating in an environment modeled as a Markov decision process (MDP), and develop an algorithm to automatically generate a personalized explanation of an optimal MDP policy, based on the user preference regarding four elements (i.e., objective, locality, specificity, and cor"},"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":"2011.00524","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-11-01T15:10:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"86ade55777dc6c6f2343683578427e47c9bf10fd9d2e0eedd315706f5d0cc32c","abstract_canon_sha256":"513c02c4e1ee7707fb664ad99c72a6311b12a2a805f3f399cd8749f6219be73e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:20:42.529138Z","signature_b64":"XADT6JOG8Auz1XDF8WWUrp9wGGGRa7jui+eQa/q3+AvOSSeRdah322yB8CDQ45SHe7p8HpU5IUFyDfA9ubF6Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"99ab45be6a732ded5d6b7cd9cd2d28bd14f86f889a6358d8e91300d6231e1c69","last_reissued_at":"2026-07-05T02:20:42.528797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:20:42.528797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Personalized Explanation of Robot Path Planning via User Feedback","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Kayla Boggess, Lu Feng, Shenghui Chen","submitted_at":"2020-11-01T15:10:43Z","abstract_excerpt":"Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaboration. In this paper, we present a system for generating personalized explanations of robot path planning via user feedback. We consider a robot navigating in an environment modeled as a Markov decision process (MDP), and develop an algorithm to automatically generate a personalized explanation of an optimal MDP policy, based on the user preference regarding four elements (i.e., objective, locality, specificity, and cor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.00524","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.00524/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2011.00524","created_at":"2026-07-05T02:20:42.528857+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.00524v2","created_at":"2026-07-05T02:20:42.528857+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.00524","created_at":"2026-07-05T02:20:42.528857+00:00"},{"alias_kind":"pith_short_12","alias_value":"TGVULPTKOMW6","created_at":"2026-07-05T02:20:42.528857+00:00"},{"alias_kind":"pith_short_16","alias_value":"TGVULPTKOMW62XLL","created_at":"2026-07-05T02:20:42.528857+00:00"},{"alias_kind":"pith_short_8","alias_value":"TGVULPTK","created_at":"2026-07-05T02:20:42.528857+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03049","citing_title":"Personalised Explanations in Long-term Human-Robot Interactions","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TGVULPTKOMW62XLLPTM42LJIXU","json":"https://pith.science/pith/TGVULPTKOMW62XLLPTM42LJIXU.json","graph_json":"https://pith.science/api/pith-number/TGVULPTKOMW62XLLPTM42LJIXU/graph.json","events_json":"https://pith.science/api/pith-number/TGVULPTKOMW62XLLPTM42LJIXU/events.json","paper":"https://pith.science/paper/TGVULPTK"},"agent_actions":{"view_html":"https://pith.science/pith/TGVULPTKOMW62XLLPTM42LJIXU","download_json":"https://pith.science/pith/TGVULPTKOMW62XLLPTM42LJIXU.json","view_paper":"https://pith.science/paper/TGVULPTK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.00524&json=true","fetch_graph":"https://pith.science/api/pith-number/TGVULPTKOMW62XLLPTM42LJIXU/graph.json","fetch_events":"https://pith.science/api/pith-number/TGVULPTKOMW62XLLPTM42LJIXU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TGVULPTKOMW62XLLPTM42LJIXU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TGVULPTKOMW62XLLPTM42LJIXU/action/storage_attestation","attest_author":"https://pith.science/pith/TGVULPTKOMW62XLLPTM42LJIXU/action/author_attestation","sign_citation":"https://pith.science/pith/TGVULPTKOMW62XLLPTM42LJIXU/action/citation_signature","submit_replication":"https://pith.science/pith/TGVULPTKOMW62XLLPTM42LJIXU/action/replication_record"}},"created_at":"2026-07-05T02:20:42.528857+00:00","updated_at":"2026-07-05T02:20:42.528857+00:00"}