{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ZXX3V3Q5DGPMRU3Q55DLIQNPAC","short_pith_number":"pith:ZXX3V3Q5","canonical_record":{"source":{"id":"1901.00723","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2019-01-03T14:03:23Z","cross_cats_sorted":[],"title_canon_sha256":"20363b6479e1090e878085c4a164bb429c67c741883da4bb450d8fb16ddc20fe","abstract_canon_sha256":"e7c81bef8a6f8a67dc89f9b49839e9e0513564e48eb96b994e3ffe8452c95946"},"schema_version":"1.0"},"canonical_sha256":"cdefbaee1d199ec8d370ef46b441af00bb05541531edfeb2e299dce393516343","source":{"kind":"arxiv","id":"1901.00723","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.00723","created_at":"2026-05-17T23:57:01Z"},{"alias_kind":"arxiv_version","alias_value":"1901.00723v1","created_at":"2026-05-17T23:57:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.00723","created_at":"2026-05-17T23:57:01Z"},{"alias_kind":"pith_short_12","alias_value":"ZXX3V3Q5DGPM","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZXX3V3Q5DGPMRU3Q","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZXX3V3Q5","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ZXX3V3Q5DGPMRU3Q55DLIQNPAC","target":"record","payload":{"canonical_record":{"source":{"id":"1901.00723","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2019-01-03T14:03:23Z","cross_cats_sorted":[],"title_canon_sha256":"20363b6479e1090e878085c4a164bb429c67c741883da4bb450d8fb16ddc20fe","abstract_canon_sha256":"e7c81bef8a6f8a67dc89f9b49839e9e0513564e48eb96b994e3ffe8452c95946"},"schema_version":"1.0"},"canonical_sha256":"cdefbaee1d199ec8d370ef46b441af00bb05541531edfeb2e299dce393516343","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:01.521807Z","signature_b64":"r22to9wW8MINqdsZiUMwRk9uCO8JKcWJeNei5kP2tqnR+lOAj7yCW1+F7ObQuTVpE0kQaxLe3H/hUVPWukKxDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdefbaee1d199ec8d370ef46b441af00bb05541531edfeb2e299dce393516343","last_reissued_at":"2026-05-17T23:57:01.521197Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:01.521197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.00723","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:57:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BwIfmJI4S+h66cTG+39MLtP0weI+avfznOU7ii5D/VgkljfDgoJixLpLjk+aPptMfDd+oM5zw5lw13l5o8v7AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T04:52:37.396159Z"},"content_sha256":"a8bfdafee887f9a0a3c67f460ac6bb03716b17054c0a5f88e4aa2ddd642b7dc0","schema_version":"1.0","event_id":"sha256:a8bfdafee887f9a0a3c67f460ac6bb03716b17054c0a5f88e4aa2ddd642b7dc0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ZXX3V3Q5DGPMRU3Q55DLIQNPAC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Diego Perez-Liebana, Ivan Bravi, Jialin Liu, John Woodward, Raluca D. Gaina, Simon M. Lucas, Vanessa Volz","submitted_at":"2019-01-03T14:03:23Z","abstract_excerpt":"This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation. Planet Wars is a real-time strategy game with simple rules but complex game-play. The variant introduced in this paper is designed for speed to enable efficient experimentation, and also for a fixed action space to enable practical inter-operability with General Video Game AI agents. If we treat the game as a win-loss game (which is standard), then this leads to challenging noisy optimisation problems both in tuning agents to pl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.00723","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:57:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LsIlUq5KqzG4oKrneits+f5tBAfyA3BMi+IOWL5T0b5o6FGMUqHfctiehs2aL6kjii6r2hph5Vq89oZpustiDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T04:52:37.396525Z"},"content_sha256":"898027240a3a085fbf09b00d4f06dfbdf343c219f838a25845f94e8d3ed3f4f3","schema_version":"1.0","event_id":"sha256:898027240a3a085fbf09b00d4f06dfbdf343c219f838a25845f94e8d3ed3f4f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZXX3V3Q5DGPMRU3Q55DLIQNPAC/bundle.json","state_url":"https://pith.science/pith/ZXX3V3Q5DGPMRU3Q55DLIQNPAC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZXX3V3Q5DGPMRU3Q55DLIQNPAC/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":"2026-06-03T04:52:37Z","links":{"resolver":"https://pith.science/pith/ZXX3V3Q5DGPMRU3Q55DLIQNPAC","bundle":"https://pith.science/pith/ZXX3V3Q5DGPMRU3Q55DLIQNPAC/bundle.json","state":"https://pith.science/pith/ZXX3V3Q5DGPMRU3Q55DLIQNPAC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZXX3V3Q5DGPMRU3Q55DLIQNPAC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ZXX3V3Q5DGPMRU3Q55DLIQNPAC","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":"e7c81bef8a6f8a67dc89f9b49839e9e0513564e48eb96b994e3ffe8452c95946","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2019-01-03T14:03:23Z","title_canon_sha256":"20363b6479e1090e878085c4a164bb429c67c741883da4bb450d8fb16ddc20fe"},"schema_version":"1.0","source":{"id":"1901.00723","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.00723","created_at":"2026-05-17T23:57:01Z"},{"alias_kind":"arxiv_version","alias_value":"1901.00723v1","created_at":"2026-05-17T23:57:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.00723","created_at":"2026-05-17T23:57:01Z"},{"alias_kind":"pith_short_12","alias_value":"ZXX3V3Q5DGPM","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZXX3V3Q5DGPMRU3Q","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZXX3V3Q5","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:898027240a3a085fbf09b00d4f06dfbdf343c219f838a25845f94e8d3ed3f4f3","target":"graph","created_at":"2026-05-17T23:57:01Z","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":"This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation. Planet Wars is a real-time strategy game with simple rules but complex game-play. The variant introduced in this paper is designed for speed to enable efficient experimentation, and also for a fixed action space to enable practical inter-operability with General Video Game AI agents. If we treat the game as a win-loss game (which is standard), then this leads to challenging noisy optimisation problems both in tuning agents to pl","authors_text":"Diego Perez-Liebana, Ivan Bravi, Jialin Liu, John Woodward, Raluca D. Gaina, Simon M. Lucas, Vanessa Volz","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2019-01-03T14:03:23Z","title":"Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.00723","kind":"arxiv","version":1},"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:a8bfdafee887f9a0a3c67f460ac6bb03716b17054c0a5f88e4aa2ddd642b7dc0","target":"record","created_at":"2026-05-17T23:57:01Z","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":"e7c81bef8a6f8a67dc89f9b49839e9e0513564e48eb96b994e3ffe8452c95946","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2019-01-03T14:03:23Z","title_canon_sha256":"20363b6479e1090e878085c4a164bb429c67c741883da4bb450d8fb16ddc20fe"},"schema_version":"1.0","source":{"id":"1901.00723","kind":"arxiv","version":1}},"canonical_sha256":"cdefbaee1d199ec8d370ef46b441af00bb05541531edfeb2e299dce393516343","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cdefbaee1d199ec8d370ef46b441af00bb05541531edfeb2e299dce393516343","first_computed_at":"2026-05-17T23:57:01.521197Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:01.521197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r22to9wW8MINqdsZiUMwRk9uCO8JKcWJeNei5kP2tqnR+lOAj7yCW1+F7ObQuTVpE0kQaxLe3H/hUVPWukKxDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:01.521807Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.00723","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a8bfdafee887f9a0a3c67f460ac6bb03716b17054c0a5f88e4aa2ddd642b7dc0","sha256:898027240a3a085fbf09b00d4f06dfbdf343c219f838a25845f94e8d3ed3f4f3"],"state_sha256":"3b73f080664e1bb7a49b4f37f7afc627a6cdac8d13265f13735d89658f33fd65"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8nRzPIpjCa5ov8vFoIHk1b3nKmkOc/w0Bx94ZsgKDGOnruxP7ycM9aSf+6yIcswhEII6meaEgkJAr68XfNcyDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T04:52:37.398453Z","bundle_sha256":"57428ed9f275929bef089317c388dbc167be26b11a5dc781882128a43d23cfa7"}}