{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CPXCIRBTK7YYOBABTPABUGOPSR","short_pith_number":"pith:CPXCIRBT","schema_version":"1.0","canonical_sha256":"13ee24443357f18704019bc01a19cf945c51097100c4615c5a74e2d00a19316e","source":{"kind":"arxiv","id":"2405.04941","version":2},"attestation_state":"computed","paper":{"title":"Imprecise Probabilities Meet Partial Observability: Game Semantics for Robust POMDPs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GT"],"primary_cat":"cs.AI","authors_text":"Eline M. Bovy, Marnix Suilen, Nils Jansen, Sebastian Junges","submitted_at":"2024-05-08T10:22:49Z","abstract_excerpt":"Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this concern by defining imprecise probabilities, referred to as uncertainty sets. While robust MDPs have been studied extensively, work on RPOMDPs is limited and primarily focuses on algorithmic solution methods. We expand the theoretical understanding of RPOMDPs by showing that 1) different assumptions on the uncertainty sets affect optimal policies and values; 2) RPOMDPs have a partially observable stochastic game (POSG) sem"},"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":"2405.04941","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-08T10:22:49Z","cross_cats_sorted":["cs.GT"],"title_canon_sha256":"8c68953cc42f034e0927ec0599322414f59e015fbbf4b2fa330477a4abe3fbae","abstract_canon_sha256":"1bf099b7b9459aa6c88d8cc19a2834f25ea75e21e3e047af860cc818e944c0ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:49:34.265040Z","signature_b64":"me2Dd/eaG0jbpnp7awn5JxMVUgvKc6jnnauiMf8n8D7Sh/LNPogoLh/YXrQ3I+SU4MNI8GVAtB3duszTHvlUCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13ee24443357f18704019bc01a19cf945c51097100c4615c5a74e2d00a19316e","last_reissued_at":"2026-07-05T08:49:34.264579Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:49:34.264579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Imprecise Probabilities Meet Partial Observability: Game Semantics for Robust POMDPs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GT"],"primary_cat":"cs.AI","authors_text":"Eline M. Bovy, Marnix Suilen, Nils Jansen, Sebastian Junges","submitted_at":"2024-05-08T10:22:49Z","abstract_excerpt":"Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this concern by defining imprecise probabilities, referred to as uncertainty sets. While robust MDPs have been studied extensively, work on RPOMDPs is limited and primarily focuses on algorithmic solution methods. We expand the theoretical understanding of RPOMDPs by showing that 1) different assumptions on the uncertainty sets affect optimal policies and values; 2) RPOMDPs have a partially observable stochastic game (POSG) sem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.04941","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/2405.04941/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":"2405.04941","created_at":"2026-07-05T08:49:34.264635+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.04941v2","created_at":"2026-07-05T08:49:34.264635+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04941","created_at":"2026-07-05T08:49:34.264635+00:00"},{"alias_kind":"pith_short_12","alias_value":"CPXCIRBTK7YY","created_at":"2026-07-05T08:49:34.264635+00:00"},{"alias_kind":"pith_short_16","alias_value":"CPXCIRBTK7YYOBAB","created_at":"2026-07-05T08:49:34.264635+00:00"},{"alias_kind":"pith_short_8","alias_value":"CPXCIRBT","created_at":"2026-07-05T08:49:34.264635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.11451","citing_title":"Robust Markov Decision Processes: A Place Where AI and Formal Methods Meet","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CPXCIRBTK7YYOBABTPABUGOPSR","json":"https://pith.science/pith/CPXCIRBTK7YYOBABTPABUGOPSR.json","graph_json":"https://pith.science/api/pith-number/CPXCIRBTK7YYOBABTPABUGOPSR/graph.json","events_json":"https://pith.science/api/pith-number/CPXCIRBTK7YYOBABTPABUGOPSR/events.json","paper":"https://pith.science/paper/CPXCIRBT"},"agent_actions":{"view_html":"https://pith.science/pith/CPXCIRBTK7YYOBABTPABUGOPSR","download_json":"https://pith.science/pith/CPXCIRBTK7YYOBABTPABUGOPSR.json","view_paper":"https://pith.science/paper/CPXCIRBT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.04941&json=true","fetch_graph":"https://pith.science/api/pith-number/CPXCIRBTK7YYOBABTPABUGOPSR/graph.json","fetch_events":"https://pith.science/api/pith-number/CPXCIRBTK7YYOBABTPABUGOPSR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CPXCIRBTK7YYOBABTPABUGOPSR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CPXCIRBTK7YYOBABTPABUGOPSR/action/storage_attestation","attest_author":"https://pith.science/pith/CPXCIRBTK7YYOBABTPABUGOPSR/action/author_attestation","sign_citation":"https://pith.science/pith/CPXCIRBTK7YYOBABTPABUGOPSR/action/citation_signature","submit_replication":"https://pith.science/pith/CPXCIRBTK7YYOBABTPABUGOPSR/action/replication_record"}},"created_at":"2026-07-05T08:49:34.264635+00:00","updated_at":"2026-07-05T08:49:34.264635+00:00"}