{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UKO6YSKLK32WHSWIWWJF62NOG5","short_pith_number":"pith:UKO6YSKL","schema_version":"1.0","canonical_sha256":"a29dec494b56f563cac8b5925f69ae37799157f98a56f4ee82a26aa473118f61","source":{"kind":"arxiv","id":"1706.06486","version":1},"attestation_state":"computed","paper":{"title":"Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LO"],"primary_cat":"cs.PF","authors_text":"Anton\\'in Ku\\v{c}era, Christel Baier, Clemens Dubslaff, \\v{L}ubo\\v{s} Koren\\v{c}iak, Vojt\\v{e}ch \\v{R}eh\\'ak","submitted_at":"2017-06-20T14:39:14Z","abstract_excerpt":"Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events that can be non-exponentially distributed. Within parametric ACTMCs, the parameters of alarm-event distributions are not given explicitly and can be subject of parameter synthesis. An algorithm solving the $\\varepsilon$-optimal parameter synthesis problem for parametric ACTMCs with long-run average optimization objectives is presented. Our approach is based on reduction of the problem to finding long-run average optimal strategies in semi-Markov decision processes (semi-MDPs) and sufficient discretization of parameter (i"},"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":"1706.06486","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.PF","submitted_at":"2017-06-20T14:39:14Z","cross_cats_sorted":["cs.LO"],"title_canon_sha256":"f3c525e8f2ab42d877de19974489fc5db0329107a081cd73b7a72451888051ed","abstract_canon_sha256":"7b2e659e2b96bdfa3a100f09adab476e1554373263e79a6b4474bd9ec7d8538a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:03.106990Z","signature_b64":"veVYdVzTdLUp0C5db3xK2raqfDJTWJ/7GZHlWUC/OLfMje9qedsVojrNQXeId6qEMHhUZpht2YOaq5YUMSiGAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a29dec494b56f563cac8b5925f69ae37799157f98a56f4ee82a26aa473118f61","last_reissued_at":"2026-05-18T00:42:03.106429Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:03.106429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LO"],"primary_cat":"cs.PF","authors_text":"Anton\\'in Ku\\v{c}era, Christel Baier, Clemens Dubslaff, \\v{L}ubo\\v{s} Koren\\v{c}iak, Vojt\\v{e}ch \\v{R}eh\\'ak","submitted_at":"2017-06-20T14:39:14Z","abstract_excerpt":"Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events that can be non-exponentially distributed. Within parametric ACTMCs, the parameters of alarm-event distributions are not given explicitly and can be subject of parameter synthesis. An algorithm solving the $\\varepsilon$-optimal parameter synthesis problem for parametric ACTMCs with long-run average optimization objectives is presented. Our approach is based on reduction of the problem to finding long-run average optimal strategies in semi-Markov decision processes (semi-MDPs) and sufficient discretization of parameter (i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06486","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":"1706.06486","created_at":"2026-05-18T00:42:03.106509+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.06486v1","created_at":"2026-05-18T00:42:03.106509+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06486","created_at":"2026-05-18T00:42:03.106509+00:00"},{"alias_kind":"pith_short_12","alias_value":"UKO6YSKLK32W","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"UKO6YSKLK32WHSWI","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"UKO6YSKL","created_at":"2026-05-18T12:31:46.661854+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/UKO6YSKLK32WHSWIWWJF62NOG5","json":"https://pith.science/pith/UKO6YSKLK32WHSWIWWJF62NOG5.json","graph_json":"https://pith.science/api/pith-number/UKO6YSKLK32WHSWIWWJF62NOG5/graph.json","events_json":"https://pith.science/api/pith-number/UKO6YSKLK32WHSWIWWJF62NOG5/events.json","paper":"https://pith.science/paper/UKO6YSKL"},"agent_actions":{"view_html":"https://pith.science/pith/UKO6YSKLK32WHSWIWWJF62NOG5","download_json":"https://pith.science/pith/UKO6YSKLK32WHSWIWWJF62NOG5.json","view_paper":"https://pith.science/paper/UKO6YSKL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.06486&json=true","fetch_graph":"https://pith.science/api/pith-number/UKO6YSKLK32WHSWIWWJF62NOG5/graph.json","fetch_events":"https://pith.science/api/pith-number/UKO6YSKLK32WHSWIWWJF62NOG5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UKO6YSKLK32WHSWIWWJF62NOG5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UKO6YSKLK32WHSWIWWJF62NOG5/action/storage_attestation","attest_author":"https://pith.science/pith/UKO6YSKLK32WHSWIWWJF62NOG5/action/author_attestation","sign_citation":"https://pith.science/pith/UKO6YSKLK32WHSWIWWJF62NOG5/action/citation_signature","submit_replication":"https://pith.science/pith/UKO6YSKLK32WHSWIWWJF62NOG5/action/replication_record"}},"created_at":"2026-05-18T00:42:03.106509+00:00","updated_at":"2026-05-18T00:42:03.106509+00:00"}