{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:QHTOT2AWN4UJ3BTIHDCZKHZPGX","short_pith_number":"pith:QHTOT2AW","canonical_record":{"source":{"id":"2410.17624","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-23T07:29:30Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"aa1cac746990aabbf394be1b964bbf675c1ac400b7873247e89cbe66073676f1","abstract_canon_sha256":"8689fa3da10d36f060420187ad318fbd637e359587ae6840029a2c2e8af17e92"},"schema_version":"1.0"},"canonical_sha256":"81e6e9e8166f289d866838c5951f2f35d3638ff3016da1470d406d9109a92f8c","source":{"kind":"arxiv","id":"2410.17624","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.17624","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"arxiv_version","alias_value":"2410.17624v1","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.17624","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"pith_short_12","alias_value":"QHTOT2AWN4UJ","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"pith_short_16","alias_value":"QHTOT2AWN4UJ3BTI","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"pith_short_8","alias_value":"QHTOT2AW","created_at":"2026-07-05T09:24:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:QHTOT2AWN4UJ3BTIHDCZKHZPGX","target":"record","payload":{"canonical_record":{"source":{"id":"2410.17624","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-23T07:29:30Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"aa1cac746990aabbf394be1b964bbf675c1ac400b7873247e89cbe66073676f1","abstract_canon_sha256":"8689fa3da10d36f060420187ad318fbd637e359587ae6840029a2c2e8af17e92"},"schema_version":"1.0"},"canonical_sha256":"81e6e9e8166f289d866838c5951f2f35d3638ff3016da1470d406d9109a92f8c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:24:41.702691Z","signature_b64":"9qIFGJNHkRZI4Mcdm2IDviOT/FpYnVm982AVXI7njwlPnaZmLNCqJvJS79av1Oxbx1ejWTxLojdsO3DSUAUjCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81e6e9e8166f289d866838c5951f2f35d3638ff3016da1470d406d9109a92f8c","last_reissued_at":"2026-07-05T09:24:41.702206Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:24:41.702206Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.17624","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":"2026-07-05T09:24:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1yfWXtdyphBy/besedaCuiF1/fg8KaDPcz18ykyazpJoWbhKVLRzhmjYwUsMstyx0yhABJLEIMR7AFl3VnKQCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:13:41.289963Z"},"content_sha256":"af95d772cb065f5f94d88eb75c3dc5b7537f9ee453a29dd33f98d725e3915127","schema_version":"1.0","event_id":"sha256:af95d772cb065f5f94d88eb75c3dc5b7537f9ee453a29dd33f98d725e3915127"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:QHTOT2AWN4UJ3BTIHDCZKHZPGX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Incremental Learning of Affordances using Markov Logic Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"George Potter, Gertjan Burghouts, Joris Sijs","submitted_at":"2024-10-23T07:29:30Z","abstract_excerpt":"Affordances enable robots to have a semantic understanding of their surroundings. This allows them to have more acting flexibility when completing a given task. Capturing object affordances in a machine learning model is a difficult task, because of their dependence on contextual information. Markov Logic Networks (MLN) combine probabilistic reasoning with logic that is able to capture such context. Mobile robots operate in partially known environments wherein unseen object affordances can be observed. This new information must be incorporated into the existing knowledge, without having to ret"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.17624","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.17624/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T09:24:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8enjAyfeEHeMP4uFaCRbSIcDZlZ1P5Sw4464vopxd7pRxXNnAL27C2sgdKaB0wcTTC44TBfGIM5/2j+JYn/sCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:13:41.290492Z"},"content_sha256":"d2af203a0c050f36fab5d2543f2b5b16a3cf3e65a73ced62f7dc62638712b822","schema_version":"1.0","event_id":"sha256:d2af203a0c050f36fab5d2543f2b5b16a3cf3e65a73ced62f7dc62638712b822"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QHTOT2AWN4UJ3BTIHDCZKHZPGX/bundle.json","state_url":"https://pith.science/pith/QHTOT2AWN4UJ3BTIHDCZKHZPGX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QHTOT2AWN4UJ3BTIHDCZKHZPGX/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":"2026-08-03T16:13:41Z","links":{"resolver":"https://pith.science/pith/QHTOT2AWN4UJ3BTIHDCZKHZPGX","bundle":"https://pith.science/pith/QHTOT2AWN4UJ3BTIHDCZKHZPGX/bundle.json","state":"https://pith.science/pith/QHTOT2AWN4UJ3BTIHDCZKHZPGX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QHTOT2AWN4UJ3BTIHDCZKHZPGX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QHTOT2AWN4UJ3BTIHDCZKHZPGX","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":"8689fa3da10d36f060420187ad318fbd637e359587ae6840029a2c2e8af17e92","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-23T07:29:30Z","title_canon_sha256":"aa1cac746990aabbf394be1b964bbf675c1ac400b7873247e89cbe66073676f1"},"schema_version":"1.0","source":{"id":"2410.17624","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.17624","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"arxiv_version","alias_value":"2410.17624v1","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.17624","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"pith_short_12","alias_value":"QHTOT2AWN4UJ","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"pith_short_16","alias_value":"QHTOT2AWN4UJ3BTI","created_at":"2026-07-05T09:24:41Z"},{"alias_kind":"pith_short_8","alias_value":"QHTOT2AW","created_at":"2026-07-05T09:24:41Z"}],"graph_snapshots":[{"event_id":"sha256:d2af203a0c050f36fab5d2543f2b5b16a3cf3e65a73ced62f7dc62638712b822","target":"graph","created_at":"2026-07-05T09:24:41Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2410.17624/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Affordances enable robots to have a semantic understanding of their surroundings. This allows them to have more acting flexibility when completing a given task. Capturing object affordances in a machine learning model is a difficult task, because of their dependence on contextual information. Markov Logic Networks (MLN) combine probabilistic reasoning with logic that is able to capture such context. Mobile robots operate in partially known environments wherein unseen object affordances can be observed. This new information must be incorporated into the existing knowledge, without having to ret","authors_text":"George Potter, Gertjan Burghouts, Joris Sijs","cross_cats":["cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-23T07:29:30Z","title":"Incremental Learning of Affordances using Markov Logic Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.17624","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:af95d772cb065f5f94d88eb75c3dc5b7537f9ee453a29dd33f98d725e3915127","target":"record","created_at":"2026-07-05T09:24:41Z","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":"8689fa3da10d36f060420187ad318fbd637e359587ae6840029a2c2e8af17e92","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-23T07:29:30Z","title_canon_sha256":"aa1cac746990aabbf394be1b964bbf675c1ac400b7873247e89cbe66073676f1"},"schema_version":"1.0","source":{"id":"2410.17624","kind":"arxiv","version":1}},"canonical_sha256":"81e6e9e8166f289d866838c5951f2f35d3638ff3016da1470d406d9109a92f8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81e6e9e8166f289d866838c5951f2f35d3638ff3016da1470d406d9109a92f8c","first_computed_at":"2026-07-05T09:24:41.702206Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:24:41.702206Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9qIFGJNHkRZI4Mcdm2IDviOT/FpYnVm982AVXI7njwlPnaZmLNCqJvJS79av1Oxbx1ejWTxLojdsO3DSUAUjCw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:24:41.702691Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.17624","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:af95d772cb065f5f94d88eb75c3dc5b7537f9ee453a29dd33f98d725e3915127","sha256:d2af203a0c050f36fab5d2543f2b5b16a3cf3e65a73ced62f7dc62638712b822"],"state_sha256":"8c4ce5fd78135e5dc525acf36191c538caf4344fe74210cc15de8a26cca5e9f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lo5o5+kYeSPD3w8IxOQC1UTRPfmVSxwxjrRWc+9zjt7QxvMd+1V4eAqDeHWEbGbWEXkcR0dy91LwZ/dQADjeAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T16:13:41.296447Z","bundle_sha256":"6624eeea523d3b13c8e6c6f4eaa539595b655cc50f3c0e155dea1ee94a7c37e3"}}