{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:VIR5METBE7KV2C5BZR34YM56J5","short_pith_number":"pith:VIR5METB","schema_version":"1.0","canonical_sha256":"aa23d6126127d55d0ba1cc77cc33be4f6fd335acf7dc3e6b298c6f8454def3f4","source":{"kind":"arxiv","id":"1803.03491","version":1},"attestation_state":"computed","paper":{"title":"Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.AP","stat.ML"],"primary_cat":"cs.MA","authors_text":"Hussain Kazmi, Johan Driesen, Johan Suykens","submitted_at":"2018-03-09T12:48:03Z","abstract_excerpt":"Increasing energy efficiency in buildings can reduce costs and emissions substantially. Historically, this has been treated as a local, or single-agent, optimization problem. However, many buildings utilize the same types of thermal equipment e.g. electric heaters and hot water vessels. During operation, occupants in these buildings interact with the equipment differently thereby driving them to diverse regions in the state-space. Reinforcement learning agents can learn from these interactions, recorded as sensor data, to optimize the overall energy efficiency. However, if these agents operate"},"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":"1803.03491","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2018-03-09T12:48:03Z","cross_cats_sorted":["cs.AI","stat.AP","stat.ML"],"title_canon_sha256":"84a87a4dfa958f4b2a4b888a9e5cf8134c99ee637aa8ae72ce146ac0a49f8046","abstract_canon_sha256":"34481d0d9c884f80ea1ea816422dadd0634ceffe389f4e721f5575eac6c317f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:39.403354Z","signature_b64":"aTivJFxqZtYHFkrA/yGffHfOCMzhPIyqpL6CVNtzxnY4jzdoHU0uAihQGkRxSFI8UxL3bTK7HNrUyL4NGWMkCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa23d6126127d55d0ba1cc77cc33be4f6fd335acf7dc3e6b298c6f8454def3f4","last_reissued_at":"2026-05-18T00:21:39.402812Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:39.402812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.AP","stat.ML"],"primary_cat":"cs.MA","authors_text":"Hussain Kazmi, Johan Driesen, Johan Suykens","submitted_at":"2018-03-09T12:48:03Z","abstract_excerpt":"Increasing energy efficiency in buildings can reduce costs and emissions substantially. Historically, this has been treated as a local, or single-agent, optimization problem. However, many buildings utilize the same types of thermal equipment e.g. electric heaters and hot water vessels. During operation, occupants in these buildings interact with the equipment differently thereby driving them to diverse regions in the state-space. Reinforcement learning agents can learn from these interactions, recorded as sensor data, to optimize the overall energy efficiency. However, if these agents operate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.03491","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":"1803.03491","created_at":"2026-05-18T00:21:39.402894+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.03491v1","created_at":"2026-05-18T00:21:39.402894+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.03491","created_at":"2026-05-18T00:21:39.402894+00:00"},{"alias_kind":"pith_short_12","alias_value":"VIR5METBE7KV","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"VIR5METBE7KV2C5B","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"VIR5METB","created_at":"2026-05-18T12:32:59.047623+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/VIR5METBE7KV2C5BZR34YM56J5","json":"https://pith.science/pith/VIR5METBE7KV2C5BZR34YM56J5.json","graph_json":"https://pith.science/api/pith-number/VIR5METBE7KV2C5BZR34YM56J5/graph.json","events_json":"https://pith.science/api/pith-number/VIR5METBE7KV2C5BZR34YM56J5/events.json","paper":"https://pith.science/paper/VIR5METB"},"agent_actions":{"view_html":"https://pith.science/pith/VIR5METBE7KV2C5BZR34YM56J5","download_json":"https://pith.science/pith/VIR5METBE7KV2C5BZR34YM56J5.json","view_paper":"https://pith.science/paper/VIR5METB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.03491&json=true","fetch_graph":"https://pith.science/api/pith-number/VIR5METBE7KV2C5BZR34YM56J5/graph.json","fetch_events":"https://pith.science/api/pith-number/VIR5METBE7KV2C5BZR34YM56J5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VIR5METBE7KV2C5BZR34YM56J5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VIR5METBE7KV2C5BZR34YM56J5/action/storage_attestation","attest_author":"https://pith.science/pith/VIR5METBE7KV2C5BZR34YM56J5/action/author_attestation","sign_citation":"https://pith.science/pith/VIR5METBE7KV2C5BZR34YM56J5/action/citation_signature","submit_replication":"https://pith.science/pith/VIR5METBE7KV2C5BZR34YM56J5/action/replication_record"}},"created_at":"2026-05-18T00:21:39.402894+00:00","updated_at":"2026-05-18T00:21:39.402894+00:00"}