{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:IGQHVPGSGUGLHE2SJECLW4OVTD","short_pith_number":"pith:IGQHVPGS","schema_version":"1.0","canonical_sha256":"41a07abcd2350cb393524904bb71d598e61d61872309ae05f2c4cc8327344f9f","source":{"kind":"arxiv","id":"1707.03336","version":1},"attestation_state":"computed","paper":{"title":"CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Adam Summerville, Joseph Osborn, Michael Mateas","submitted_at":"2017-07-11T15:50:09Z","abstract_excerpt":"We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system. Working from a sequence of continuous state values and predicates about the environment, CHARDA recovers the distinct dynamic modes, learns a model for each mode from a given set of templates, and postulates causal guard conditions which trigger transitions between modes. Our main contribution is the use of information-theoretic measures (1)~as a cost function for data segmentation and model selection to penalize over-fitting and (2)~to determine the likel"},"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":"1707.03336","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-07-11T15:50:09Z","cross_cats_sorted":[],"title_canon_sha256":"805ab295c5d14e46d3bdb1b3240ad95a44cf12337ba4ebc69bfde838bf0d6cb6","abstract_canon_sha256":"270ba39baf10cd0187bb67e93d1022330cba8601761c081b3bbe62a5ffdea8fe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:29.300858Z","signature_b64":"JggTrZktvnCU0QTIpOrFshURu2f1hFSmbI02nL5VmH+bZvmF19L/VNPYbVpkjMp/3pRBzy3xbWnhq4CSMHZECw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41a07abcd2350cb393524904bb71d598e61d61872309ae05f2c4cc8327344f9f","last_reissued_at":"2026-05-18T00:40:29.300292Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:29.300292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Adam Summerville, Joseph Osborn, Michael Mateas","submitted_at":"2017-07-11T15:50:09Z","abstract_excerpt":"We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system. Working from a sequence of continuous state values and predicates about the environment, CHARDA recovers the distinct dynamic modes, learns a model for each mode from a given set of templates, and postulates causal guard conditions which trigger transitions between modes. Our main contribution is the use of information-theoretic measures (1)~as a cost function for data segmentation and model selection to penalize over-fitting and (2)~to determine the likel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.03336","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":"1707.03336","created_at":"2026-05-18T00:40:29.300382+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.03336v1","created_at":"2026-05-18T00:40:29.300382+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.03336","created_at":"2026-05-18T00:40:29.300382+00:00"},{"alias_kind":"pith_short_12","alias_value":"IGQHVPGSGUGL","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"IGQHVPGSGUGLHE2S","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"IGQHVPGS","created_at":"2026-05-18T12:31:21.493067+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/IGQHVPGSGUGLHE2SJECLW4OVTD","json":"https://pith.science/pith/IGQHVPGSGUGLHE2SJECLW4OVTD.json","graph_json":"https://pith.science/api/pith-number/IGQHVPGSGUGLHE2SJECLW4OVTD/graph.json","events_json":"https://pith.science/api/pith-number/IGQHVPGSGUGLHE2SJECLW4OVTD/events.json","paper":"https://pith.science/paper/IGQHVPGS"},"agent_actions":{"view_html":"https://pith.science/pith/IGQHVPGSGUGLHE2SJECLW4OVTD","download_json":"https://pith.science/pith/IGQHVPGSGUGLHE2SJECLW4OVTD.json","view_paper":"https://pith.science/paper/IGQHVPGS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.03336&json=true","fetch_graph":"https://pith.science/api/pith-number/IGQHVPGSGUGLHE2SJECLW4OVTD/graph.json","fetch_events":"https://pith.science/api/pith-number/IGQHVPGSGUGLHE2SJECLW4OVTD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IGQHVPGSGUGLHE2SJECLW4OVTD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IGQHVPGSGUGLHE2SJECLW4OVTD/action/storage_attestation","attest_author":"https://pith.science/pith/IGQHVPGSGUGLHE2SJECLW4OVTD/action/author_attestation","sign_citation":"https://pith.science/pith/IGQHVPGSGUGLHE2SJECLW4OVTD/action/citation_signature","submit_replication":"https://pith.science/pith/IGQHVPGSGUGLHE2SJECLW4OVTD/action/replication_record"}},"created_at":"2026-05-18T00:40:29.300382+00:00","updated_at":"2026-05-18T00:40:29.300382+00:00"}