R&D

Publications

IET has been a leader in the scientific community for the last decade. Following are a sample of the IET publications that characterize this leadership position.



Multisource Fusion for Probabilistic Detection and Opportunistic Assessment of Terrorist Threats
This paper illustrates the use of Multi-Entity Bayesian Networks (MEBNs) with an example concerning a multi-city coordinated biowarfare attack...



An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners
Site Profiler applies knowledge-based Bayesian network construction to allow users to manage a portfolio of hundreds of threat/asset pairs...



Knowledge and Data Fusion in Probabilistic Networks
Bayesian learning, the focus of this paper, is specifically designed to incorporate both expert knowledge and observations...



Hypothesis Management in Situation-Specific Network Construction
This paper considers the problem of knowledge based model construction in the presence of uncertainty about the association of domain entities to random variables...



Use of Domain Knowledge Models to Recognize Cooperative Force Activities
This paper provides results of experiments on the use of domain knowledge models to recognize military groups, units, and activities from input track data...



Computational Inference for Evidential Reasoning in support of Judicial Proof
Bayesian inference networks are combined with knowledge representations from artificial intelligence to structure and analyze evidential argumentation...



Measuring Performance for Situation Assessment
To be useful to decision makers, there must be a way to evaluate the quality of situation assessments. This paper presents and illustrates an approach to meeting this difficult challenge...



How Radical is Van Fraassen's Voluntarism? (Assessing Recent Developments in Bayesian Reasoning)
...the version of voluntarism consistent with both Reflection and van Fraassen's belief that epistemic judgments are commitments is a much different kind of voluntarism than that envisioned by William James or Blaise Pascal...



Extending Techniques From 'Standard' Error Theory To Categorical Data And Local GIS Operations
This paper demonstrates that standard techniques from error theory can be considered to be special cases of more general graphical models that can be applied to GIS data and GIS operations...



Probabilistic Models In Geographic Information Systems - Bayesian Networks For Management Of Uncertainty
This paper reports on an implementation of Bayesian Network (BN) techniques to construct probabilistic models that meet the requirements of managing uncertainty in GIS applications...



Knowledge Engineering for Probabilistic Models
General purpose tutorial that outlines IET's knowledge engineering process...



Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms
This paper describes stochastic search approaches, including a new stochastic algorithm and an adaptive mutation operator, for learning Bayesian networks from incomplete data...



Representing and Combining Partially Specified CPTs
This paper extends previous work with network fragments and situation-specific network construction...



Learning Extensible Multi-Entity Directed Graphical Models
This paper describes a framework for representing probabilistic knowledge as fragments of belief networks and an approach to learning both structure and parameters from observations...



Extensible Multi-Entity Models: A Framework for Incremental Model Construction
This paper describes a framework for representing probabilistic knowledge as fragments of belief networks and a method for constructing situation-specific belief networks for particular problem instances...



Constructing Situation Specific Networks
This paper describes a process for constructing situation-specific belief networks from a knowledge base of network fragments...



Network Fragments: Representing Knowledge for Constructing Probabilistic Models
This paper presents a knowledge representation framework that permits the knowledge base designer to specify knowledge in larger semantically meaningful units, which we call network fragments...



Network Engineering for Complex Belief Networks
Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process...



Evaluating SME-Elicited Knowledge
This paper describes an approach to mixed-initiative systems evaluation ... in which the methods and results used are assessed and recommendations are provided for future mixed-initiative evaluations.



Evaluating Expert-Authored Rules for Military Reasoning
This paper describes a large-scale experiment to evaluate tools designed to produce SME-authored rule bases.



An Applied Calculus for Spatial Accessibility Reasoning
Recent attempts to perform formal knowledge representation and reasoning in cell biology have presented new challenges to spatial reasoning. This paper provides two distinct notions of containment that...



Modeling Insider User Behavior Using Multi-Entity Bayesian Network
In today's net-centric environment, information security is a vital concern for command and control systems. Intentional or accidental misuse of C2 system resources may present a major threat to all three...



MEBN Logic: A Key Enabler for Network Centric Warfare
Among the lessons learned from recent conflicts stands the dramatic change in the very way wars are fought. There are no more clear-cut enemies or allies; rules of engagement have become increasingly fuzzy...



Is It Worth a Hoot? Qualms about OWL for Uncertainty Reasoning
IET is developing the Knowledge Elicitation Environment for Probabilistic Event and Entity Relation system, a tool for eliciting, storing, updating, and implementing probabilistic relational models. The KEEPER...



Evidence Aggregation in Hierarchical Evidential Reasoning
Several reasoning tasks need to scale over the volume of evidence and the entities of interest. A technique used by statisticians is to represent a collection of evidence by the sufficient statistics of the data. This ...