Papers and Publications, Center of Excellence in Command, Control, Communications, and Intelligence
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This collection contains papers written by members and fellows of the C4I Center.
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Browsing Papers and Publications, Center of Excellence in Command, Control, Communications, and Intelligence by Subject "First-Order Bayesian Logic"
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Item Multi-Entity Bayesian Networks without Multi-Tears(2006-01-27T23:47:24Z) Costa, Paulo C. G.; Laskey, Kathryn B.An introduction is provided to Multi-Entity Bayesian Networks (MEBN), a logic system that integrates First Order Logic (FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated sub-structures. Knowledge is encoded as a collection of Bayesian network fragments (MFrags) that can be instantiated and combined to form highly complex situation-specific Bayesian networks. A MEBN theory (MTheory) implicitly represents a joint probability distribution over possibly unbounded numbers of hypotheses, and uses Bayesian learning to refine a knowledge base as observations accrue. MEBN provides a logical foundation for the emerging collection of highly expressive probability-based languages. A running example illustrates the representation and reasoning power of the MEBN formalism.Item Of Starships and Klingons: Bayesian Logic for the 23rd Century(AUAI Press, 2005-07) Laskey, Kathryn B.; Costa, Paulo C. G.Intelligent systems in an open world must reason about many interacting entities related to each other in diverse ways and having uncertain features and relationships. Traditional probabilistic languages lack the expressive power to handle relational domains, whereas classical first-order logic is sufficiently expressive but lacks a coherent plausible reasoning capability. Recent years have seen the emergence of a variety of approaches to integrating first-order logic, probability, and machine learning. This paper presents Multi-entity Bayesian networks (MEBN), a formal system that integrates First Order Logic (FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated sub-structures. We present the logic using an example inspired by the Paramount Series Star Trek. MEBN semantics integrates random variables as formalized in mathematical statistics with model theoretic semantics for first-order logic.