Each arc of the network represent the inference steps through which the node was derived. A premise is a fundamental belief which is assumed to be true. They do not need justifications. The set of premises are the basis from which justifications for all other nodes will be derived. There are two types of justification for a node. They are: Support list [SL] Conditional proof (CP) Many kinds of truth maintenance systems exist. Two major types are single-context and multi-context truth maintenance. In single context systems, consistency is maintained among all facts in memory (KB) and relates to the notion of consistency found in classical logic. Multi-context systems support paraconsistency by allowing consistency to be relevant to a subset of facts in memory, a context, according to the history of logical inference. This is achieved by tagging each fact or deduction with its logical history. Multi-agent truth maintenance systems perform truth maintenance across multiple memories, often located on different machines. de Kleer's assumption-based truth maintenance system (ATMS, 1986) was utilized in systems based upon KEE on the Lisp Machine. The first multi-agent TMS was created by Mason and Johnson. It was a multi-context system. Bridgeland and Huhns created the first single-context multi-agent system. See also Artificial intelligence Belief revision Knowledge acquisition Knowledge representation Neurath's boat References Other references Bridgeland, D. M. & Huhns, M. N., Distributed Truth Maintenance. Proceedings of. AAAI–90: Eighth National Conference on Artificial Intelligence, 1990. J. de Kleer (1986). An assumption-based TMS. Artificial Intelligence, 28:127–162. J. Doyle. A Truth Maintenance System. AI. Vol. 12. No 3, pp. 251–272. 1979. U. Junker and K. Konolige (1990). Computing the extensions of autoepistemic and default logics with a truth maintenance system. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI'90), pages 278–283. MIT Press. Mason, C. and Johnson, R. DATMS: A Framework for Assumption Based Reasoning, in Distributed Artificial Intelligence, Vol. 2, Morgan Kaufmann Publishers, Inc., 1989. D. A. McAllester. A three valued maintenance system. Massachusetts Institute of Technology, Artificial Intelligence Laboratory. AI Memo 473. 1978. G. M. Provan (1988). A complexity analysis of assumption-based truth maintenance systems. In B. Smith and G. Kelleher, editors, Reason Maintenance Systems and their Applications, pages 98–113. Ellis Horwood, New York. G. M. Provan (1990). The computational complexity of multiple-context truth maintenance systems. In Proceedings of the Ninth European Conference on Artificial Intelligence (ECAI'90), pages 522–527. R. Reiter and J. de Kleer