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MEBN: A Language for FirstOrder Bayesian Knowledge Bases
"... Although classical firstorder logic is the de facto standard logical foundation for artificial intelligence, the lack of a builtin, semantically grounded capability for reasoning under uncertainty renders it inadequate for many important classes of problems. Probability is the bestunderstood and m ..."
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Cited by 65 (24 self)
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Although classical firstorder logic is the de facto standard logical foundation for artificial intelligence, the lack of a builtin, semantically grounded capability for reasoning under uncertainty renders it inadequate for many important classes of problems. Probability is the bestunderstood and most widely applied formalism for computational scientific reasoning under uncertainty. Increasingly expressive languages are emerging for which the fundamental logical basis is probability. This paper presents MultiEntity Bayesian Networks (MEBN), a firstorder language for specifying probabilistic knowledge bases as parameterized fragments of Bayesian networks. MEBN fragments (MFrags) can be instantiated and combined to form arbitrarily complex graphical probability models. An MFrag represents probabilistic relationships among a conceptually meaningful group of uncertain hypotheses. Thus, MEBN facilitates representation of knowledge at a natural level of granularity. The semantics of MEBN assigns a probability distribution over interpretations of an associated classical firstorder theory on a finite or countably infinite domain. Bayesian inference provides both a proof theory for combining prior knowledge with observations, and a learning theory for refining a representation as evidence accrues. A proof is given that MEBN can represent a probability distribution on interpretations of any finitely axiomatizable firstorder theory.
MEBN: A Logic for OpenWorld Probabilistic Reasoning
 Research Paper
, 2004
"... Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Probability is the most wellunderstood and widely applied logic for computational scientific reasoning under uncertainty. As theory and practice advance, generalpurpose languages are beginning to emerge for which ..."
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Cited by 20 (8 self)
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Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Probability is the most wellunderstood and widely applied logic for computational scientific reasoning under uncertainty. As theory and practice advance, generalpurpose languages are beginning to emerge for which the fundamental logical basis is probability. However, such languages have lacked a logical foundation that fully integrates classical firstorder logic with probability theory. This paper presents such an integrated logical foundation. A formal specification is presented for multientity Bayesian networks (MEBN), a knowledge representation language based on directed graphical probability models. A proof is given that a probability distribution over interpretations of any consistent, finitely axiomatizable firstorder theory can be defined using MEBN. A semantics based on random variables provides a logically coherent foundation for open world reasoning and a means of analyzing tradeoffs between accuracy and computation cost. Furthermore, the underlying Bayesian logic is inherently open, having the ability to absorb new facts about the world, incorporate them into existing theories, and/or modify theories in the light of evidence. Bayesian inference provides both a proof theory for combining prior knowledge with observations, and a learning theory for refining a representation as evidence accrues. The results of this paper provide a logical foundation for the rapidly evolving literature on firstorder Bayesian knowledge representation, and point the way toward Bayesian languages suitable for generalpurpose knowledge representation and computing. Because firstorder Bayesian logic contains classical firstorder logic as a deterministic subset, it is a natural candidate as a universal representation for integrating domain ontologies expressed in languages based on classical firstorder logic or subsets thereof.
MultiEntity Bayesian Networks Without MultiTears
"... An introduction is provided to MultiEntity 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 substructures. Knowledge is encoded as a c ..."
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Cited by 9 (6 self)
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An introduction is provided to MultiEntity 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 substructures. Knowledge is encoded as a collection of Bayesian network fragments (MFrags) that can be instantiated and combined to form highly complex situationspecific 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 probabilitybased languages. A running example illustrates the representation and reasoning power of the MEBN formalism.
FirstOrder Bayesian Logic
, 2005
"... Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Until recently, classical firstorder logic has reigned as the de facto standard logical foundation for artificial intelligence. The lack of a builtin, semantically grounded capability for reasoning under uncertai ..."
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Cited by 8 (3 self)
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Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Until recently, classical firstorder logic has reigned as the de facto standard logical foundation for artificial intelligence. The lack of a builtin, semantically grounded capability for reasoning under uncertainty renders classical firstorder logic inadequate for many important classes of problems. Generalpurpose languages are beginning to emerge for which the fundamental logical basis is probability. Increasingly expressive probabilistic languages demand a theoretical foundation that fully integrates classical firstorder logic and probability. In firstorder Bayesian logic (FOBL), probability distributions are defined over interpretations of classical firstorder axiom systems. Predicates and functions of a classical firstorder theory correspond to a random variables in the corresponding firstorder Bayesian theory. This is a natural correspondence, given that random variables are formalized in mathematical statistics as measurable functions on a probability space. A formal system called MultiEntity Bayesian Networks (MEBN) is presented for composing distributions on interpretations by instantiating and combining parameterized fragments of directed graphical models. A construction is given of a MEBN theory that assigns a nonzero
Credibility Models for MultiSource Fusion
"... This paper presents a technical approach for fusing information from diverse sources. Fusion requires appropriate weighting of information based on the quality of the source of the information. A credibility model characterizes the quality of information based on the source and the circumstances und ..."
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Cited by 5 (1 self)
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This paper presents a technical approach for fusing information from diverse sources. Fusion requires appropriate weighting of information based on the quality of the source of the information. A credibility model characterizes the quality of information based on the source and the circumstances under which the information is collected. In many cases credibility is uncertain, so inference is necessary. Explicit probabilistic credibility models provide a computational model of the quality of the information that allows use of prior information, evidence when available, and opportunities for learning from data. This paper provides an overview of the challenges, describes the advanced probabilistic reasoning tools used to implement credibility models, and provides an example of the use of credibility models in a multisource fusion process.
Technical Report: Activity Recognition in Wide Aerial Video Surveillance Using Entity Relationship Models
, 2012
"... We present the design and implementation of an activity recognition system for wide area aerial video surveillance using Entity Relationship Models (ERM). In this approach, finding an activity is equivalent to sending a query to the Relational DataBase Management System (RDBMS). By incorporating ref ..."
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We present the design and implementation of an activity recognition system for wide area aerial video surveillance using Entity Relationship Models (ERM). In this approach, finding an activity is equivalent to sending a query to the Relational DataBase Management System (RDBMS). By incorporating reference imagery and Geographic Information System (GIS) data, tracked objects can be associated with physical meanings, and several high levels of reasoning, such as traffic patterns or abnormal activity detection, can be performed. We demonstrate that different types of activities, with hierarchical structure, multiple actors, and context information, are effectively and efficiently defined and inferred using the ERM framework. We also show how visual tracks can be better interpreted as activities by using geo information. Experimental results on both real visual tracks and GPS traces validate our approach. 1.
MultiEntity Bayesian Networks Without MultiTears
"... An introduction is provided to MultiEntity 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 substructures. Knowledge is encoded as a c ..."
Abstract
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An introduction is provided to MultiEntity 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 substructures. Knowledge is encoded as a collection of Bayesian network fragments (MFrags) that can be instantiated and combined to form highly complex situationspecific 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 probabilitybased languages. A running example illustrates the representation and reasoning power of the MEBN formalism.
THE FUTURE OF C2 MEBN LOGIC: A KEY ENABLER FOR NETWORK CENTRIC WARFARE Student Paper Modeling and Simulation
"... Among the lessons learned from recent conflicts stands the dramatic change in the very way wars are fought. There are no more clearcut enemies or allies; rules of engagement have become increasingly fuzzy; guerrilla and insurgent tactics are now commonplace: in short, the battlespace is a very diff ..."
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Among the lessons learned from recent conflicts stands the dramatic change in the very way wars are fought. There are no more clearcut enemies or allies; rules of engagement have become increasingly fuzzy; guerrilla and insurgent tactics are now commonplace: in short, the battlespace is a very different place from what it used to be. Furthermore, advances in sensor technology and network computing have brought a new element to the complex equation of warfare: information overload. Nowadays, instead of merely gathering information and displaying assets, command and control systems must be able to fill the gap between the glut of information arriving from a networked grid of sensors and the capacity of human commanders to make sense of it. In short, the quest today is for systems that work under the knowledge paradigm. Systems must automatically provide decision makers with a clear picture of what is happening, how it relates to the current situation, and what are the options and their respective consequences. Facing this challenge with technologies of the past is a recipe for failure. New, more powerful approaches are needed. The objective of this paper is to argue for two claims: (1) Bayesian decision
ACKNOWLEDGMENTS
, 2008
"... 2 To my Parents because they were there when I was alone. To my Sister because she loves me without questions. To my Professors because they answer my questions. To my Friends because they were patient with me. Thank you 3 ..."
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2 To my Parents because they were there when I was alone. To my Sister because she loves me without questions. To my Professors because they answer my questions. To my Friends because they were patient with me. Thank you 3