{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:B3I7GJYX7UF73ME76PC7KMXSKS","short_pith_number":"pith:B3I7GJYX","schema_version":"1.0","canonical_sha256":"0ed1f32717fd0bfdb09ff3c5f532f254ae56acd2360f7e47af063d94b1e2a2c8","source":{"kind":"arxiv","id":"1901.01851","version":1},"attestation_state":"computed","paper":{"title":"Personal Universes: A Solution to the Multi-Agent Value Alignment Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Roman V. Yampolskiy","submitted_at":"2019-01-01T18:05:43Z","abstract_excerpt":"AI Safety researchers attempting to align values of highly capable intelligent systems with those of humanity face a number of challenges including personal value extraction, multi-agent value merger and finally in-silico encoding. State-of-the-art research in value alignment shows difficulties in every stage in this process, but merger of incompatible preferences is a particularly difficult challenge to overcome. In this paper we assume that the value extraction problem will be solved and propose a possible way to implement an AI solution which optimally aligns with individual preferences of "},"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":"1901.01851","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-01-01T18:05:43Z","cross_cats_sorted":[],"title_canon_sha256":"ab8dd174e9caeb76512b590fce96edebdc42b59bff6de3437ecc0f926a3c1b92","abstract_canon_sha256":"ad2878cb06a2ff52f2a2d95a3893a519a999fb99d2ec8f83c1935fc1b3acdd27"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:49.489543Z","signature_b64":"Y6TvEkWJzQ1Ff5kaWDuTGYbtKR3PF5Xp2Bxgxgae75Y3WrSFPKpKR4onNDJF4XwcLRW2fflPrdudvlv1ilqcCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ed1f32717fd0bfdb09ff3c5f532f254ae56acd2360f7e47af063d94b1e2a2c8","last_reissued_at":"2026-05-17T23:56:49.489146Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:49.489146Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Personal Universes: A Solution to the Multi-Agent Value Alignment Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Roman V. Yampolskiy","submitted_at":"2019-01-01T18:05:43Z","abstract_excerpt":"AI Safety researchers attempting to align values of highly capable intelligent systems with those of humanity face a number of challenges including personal value extraction, multi-agent value merger and finally in-silico encoding. State-of-the-art research in value alignment shows difficulties in every stage in this process, but merger of incompatible preferences is a particularly difficult challenge to overcome. In this paper we assume that the value extraction problem will be solved and propose a possible way to implement an AI solution which optimally aligns with individual preferences of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.01851","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":"1901.01851","created_at":"2026-05-17T23:56:49.489203+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.01851v1","created_at":"2026-05-17T23:56:49.489203+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.01851","created_at":"2026-05-17T23:56:49.489203+00:00"},{"alias_kind":"pith_short_12","alias_value":"B3I7GJYX7UF7","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"B3I7GJYX7UF73ME7","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"B3I7GJYX","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.03869","citing_title":"Unexplainability and Incomprehensibility of Artificial Intelligence","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B3I7GJYX7UF73ME76PC7KMXSKS","json":"https://pith.science/pith/B3I7GJYX7UF73ME76PC7KMXSKS.json","graph_json":"https://pith.science/api/pith-number/B3I7GJYX7UF73ME76PC7KMXSKS/graph.json","events_json":"https://pith.science/api/pith-number/B3I7GJYX7UF73ME76PC7KMXSKS/events.json","paper":"https://pith.science/paper/B3I7GJYX"},"agent_actions":{"view_html":"https://pith.science/pith/B3I7GJYX7UF73ME76PC7KMXSKS","download_json":"https://pith.science/pith/B3I7GJYX7UF73ME76PC7KMXSKS.json","view_paper":"https://pith.science/paper/B3I7GJYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.01851&json=true","fetch_graph":"https://pith.science/api/pith-number/B3I7GJYX7UF73ME76PC7KMXSKS/graph.json","fetch_events":"https://pith.science/api/pith-number/B3I7GJYX7UF73ME76PC7KMXSKS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B3I7GJYX7UF73ME76PC7KMXSKS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B3I7GJYX7UF73ME76PC7KMXSKS/action/storage_attestation","attest_author":"https://pith.science/pith/B3I7GJYX7UF73ME76PC7KMXSKS/action/author_attestation","sign_citation":"https://pith.science/pith/B3I7GJYX7UF73ME76PC7KMXSKS/action/citation_signature","submit_replication":"https://pith.science/pith/B3I7GJYX7UF73ME76PC7KMXSKS/action/replication_record"}},"created_at":"2026-05-17T23:56:49.489203+00:00","updated_at":"2026-05-17T23:56:49.489203+00:00"}