{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:KF7VUEDL6VBBPJSH6YLTVGEJ2H","short_pith_number":"pith:KF7VUEDL","canonical_record":{"source":{"id":"1707.01859","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PF","submitted_at":"2017-07-06T16:41:07Z","cross_cats_sorted":["cs.LO"],"title_canon_sha256":"028fb10c638d25ad3aba76da5c6c98f1a50d5c09dde90f682492f0efe07a51b1","abstract_canon_sha256":"780d22e6fc22420ad9e9df43fcd4c987daaa78ce6b50b8ae2cab1982a5216a5d"},"schema_version":"1.0"},"canonical_sha256":"517f5a106bf54217a647f6173a9889d1c22323efdf3add005432bc90d0f29d50","source":{"kind":"arxiv","id":"1707.01859","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.01859","created_at":"2026-05-18T00:35:51Z"},{"alias_kind":"arxiv_version","alias_value":"1707.01859v2","created_at":"2026-05-18T00:35:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.01859","created_at":"2026-05-18T00:35:51Z"},{"alias_kind":"pith_short_12","alias_value":"KF7VUEDL6VBB","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"KF7VUEDL6VBBPJSH","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"KF7VUEDL","created_at":"2026-05-18T12:31:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:KF7VUEDL6VBBPJSH6YLTVGEJ2H","target":"record","payload":{"canonical_record":{"source":{"id":"1707.01859","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PF","submitted_at":"2017-07-06T16:41:07Z","cross_cats_sorted":["cs.LO"],"title_canon_sha256":"028fb10c638d25ad3aba76da5c6c98f1a50d5c09dde90f682492f0efe07a51b1","abstract_canon_sha256":"780d22e6fc22420ad9e9df43fcd4c987daaa78ce6b50b8ae2cab1982a5216a5d"},"schema_version":"1.0"},"canonical_sha256":"517f5a106bf54217a647f6173a9889d1c22323efdf3add005432bc90d0f29d50","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:51.105535Z","signature_b64":"JVa4n34IwvAJ+/exGxka1xpkzHDaeo0Slw74XFj8ZCN35A2pr/+hk9pyo7ylQdZppc6SlZXnNPAXtlYYvb+0AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"517f5a106bf54217a647f6173a9889d1c22323efdf3add005432bc90d0f29d50","last_reissued_at":"2026-05-18T00:35:51.105057Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:51.105057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.01859","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:35:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P6etKllIPUSGi+PwEj8zElNb1urOsDvCTXQTMIU9YFppy85CvCbQyNKXS0gvZu30jLyr8GIAYkbeXD8ed+8DDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T12:19:04.071179Z"},"content_sha256":"1a25581341538338c71922756cdc5d54a4f1b7412e88689f13ee46dd50faddf9","schema_version":"1.0","event_id":"sha256:1a25581341538338c71922756cdc5d54a4f1b7412e88689f13ee46dd50faddf9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:KF7VUEDL6VBBPJSH6YLTVGEJ2H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Strategy Iteration for Mean Payoff in Markov Decision Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LO"],"primary_cat":"cs.PF","authors_text":"Jan K\\v{r}et\\'insk\\'y, Tobias Meggendorfer","submitted_at":"2017-07-06T16:41:07Z","abstract_excerpt":"Markov decision processes (MDPs) are standard models for probabilistic systems with non-deterministic behaviours. Mean payoff (or long-run average reward) provides a mathematically elegant formalism to express performance related properties. Strategy iteration is one of the solution techniques applicable in this context. While in many other contexts it is the technique of choice due to advantages over e.g. value iteration, such as precision or possibility of domain-knowledge-aware initialization, it is rarely used for MDPs, since there it scales worse than value iteration. We provide several t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.01859","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:35:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1S3SRLU20b8FlWdBFKQRCPI/6YSmnEGATh+766ryyOKIeV5oWu/uzNXKVlTPUjvy0yQTv2YxyFAUjGhoakmKDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T12:19:04.071724Z"},"content_sha256":"cf16387b4941855e94259f4d5cb5328fb94210eb6b28fb7f02f87ce5b3537cbf","schema_version":"1.0","event_id":"sha256:cf16387b4941855e94259f4d5cb5328fb94210eb6b28fb7f02f87ce5b3537cbf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KF7VUEDL6VBBPJSH6YLTVGEJ2H/bundle.json","state_url":"https://pith.science/pith/KF7VUEDL6VBBPJSH6YLTVGEJ2H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KF7VUEDL6VBBPJSH6YLTVGEJ2H/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T12:19:04Z","links":{"resolver":"https://pith.science/pith/KF7VUEDL6VBBPJSH6YLTVGEJ2H","bundle":"https://pith.science/pith/KF7VUEDL6VBBPJSH6YLTVGEJ2H/bundle.json","state":"https://pith.science/pith/KF7VUEDL6VBBPJSH6YLTVGEJ2H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KF7VUEDL6VBBPJSH6YLTVGEJ2H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:KF7VUEDL6VBBPJSH6YLTVGEJ2H","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"780d22e6fc22420ad9e9df43fcd4c987daaa78ce6b50b8ae2cab1982a5216a5d","cross_cats_sorted":["cs.LO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PF","submitted_at":"2017-07-06T16:41:07Z","title_canon_sha256":"028fb10c638d25ad3aba76da5c6c98f1a50d5c09dde90f682492f0efe07a51b1"},"schema_version":"1.0","source":{"id":"1707.01859","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.01859","created_at":"2026-05-18T00:35:51Z"},{"alias_kind":"arxiv_version","alias_value":"1707.01859v2","created_at":"2026-05-18T00:35:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.01859","created_at":"2026-05-18T00:35:51Z"},{"alias_kind":"pith_short_12","alias_value":"KF7VUEDL6VBB","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"KF7VUEDL6VBBPJSH","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"KF7VUEDL","created_at":"2026-05-18T12:31:24Z"}],"graph_snapshots":[{"event_id":"sha256:cf16387b4941855e94259f4d5cb5328fb94210eb6b28fb7f02f87ce5b3537cbf","target":"graph","created_at":"2026-05-18T00:35:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Markov decision processes (MDPs) are standard models for probabilistic systems with non-deterministic behaviours. Mean payoff (or long-run average reward) provides a mathematically elegant formalism to express performance related properties. Strategy iteration is one of the solution techniques applicable in this context. While in many other contexts it is the technique of choice due to advantages over e.g. value iteration, such as precision or possibility of domain-knowledge-aware initialization, it is rarely used for MDPs, since there it scales worse than value iteration. We provide several t","authors_text":"Jan K\\v{r}et\\'insk\\'y, Tobias Meggendorfer","cross_cats":["cs.LO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PF","submitted_at":"2017-07-06T16:41:07Z","title":"Efficient Strategy Iteration for Mean Payoff in Markov Decision Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.01859","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1a25581341538338c71922756cdc5d54a4f1b7412e88689f13ee46dd50faddf9","target":"record","created_at":"2026-05-18T00:35:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"780d22e6fc22420ad9e9df43fcd4c987daaa78ce6b50b8ae2cab1982a5216a5d","cross_cats_sorted":["cs.LO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PF","submitted_at":"2017-07-06T16:41:07Z","title_canon_sha256":"028fb10c638d25ad3aba76da5c6c98f1a50d5c09dde90f682492f0efe07a51b1"},"schema_version":"1.0","source":{"id":"1707.01859","kind":"arxiv","version":2}},"canonical_sha256":"517f5a106bf54217a647f6173a9889d1c22323efdf3add005432bc90d0f29d50","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"517f5a106bf54217a647f6173a9889d1c22323efdf3add005432bc90d0f29d50","first_computed_at":"2026-05-18T00:35:51.105057Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:35:51.105057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JVa4n34IwvAJ+/exGxka1xpkzHDaeo0Slw74XFj8ZCN35A2pr/+hk9pyo7ylQdZppc6SlZXnNPAXtlYYvb+0AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:35:51.105535Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.01859","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1a25581341538338c71922756cdc5d54a4f1b7412e88689f13ee46dd50faddf9","sha256:cf16387b4941855e94259f4d5cb5328fb94210eb6b28fb7f02f87ce5b3537cbf"],"state_sha256":"328e9481fff34fb35e5a5933c376682d2cf077485997543e6badb82d485d4e9d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u8nuG+u3BPZMMjKP8tcMO5hUpGWH0IECxhsrV3DA/OIyBF5ouB95OfS/BQP/XTK2P9aWbm6yHsCKxmQhDVmvDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T12:19:04.074074Z","bundle_sha256":"b00a92457e243eb276ed1ecb9722cdb3086aa3977289056f2eb7ca521ac4f56a"}}