Papers and Publications, Center of Excellence in Command, Control, Communications, and Intelligence

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https://hdl.handle.net/1920/267

This collection contains papers written by members and fellows of the C4I Center.

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Evaluation of Geospatial Digital Support Products
(2010-04-20T14:50:40Z) Powell, Walter A.; Laskey, Kathryn Blackmond; Adelman, Leonard; Johnson, Ryan; Dorgan, Shiloh; Hieb, Micheal; Braswell, Kenneth; Powers, Michael
Geospatial Reasoning has been an essential aspect of military planning since the invention of cartography. Geospatial Digital Support Products (GDSPs) are ubiquitous within current military forces as well as civil and humanitarian organizations. Nevertheless, there is too little empirical evidence to quantify the military value of geospatial products to the warfighter. We conducted a hypothesis-driven experiment to evaluate the military value of the Battlefield Terrain Reasoning and Awareness – Battle Command (BTRA-BC) GDSP. Realistic scenarios and appropriate measures to assess performance were developed in collaboration with Subject Matter Experts (SMEs). The measures included time to completion, objectively assessed solution quality, subjectively assessed solution quality, and terrain understanding. BTRA-BC was integrated into the Army’s Digital Topographic Support System (DTSS). A within-subjects design was employed, in which the participants completed scenarios using DTSS with and without BTRA-BC functionality. Statistical analysis of the data indicated that when the participants used BTRA-BC functionality, they created outputs faster and of higher quality without reducing their knowledge of the impact of the terrain on military decision-making. This paper discusses the scope of the current experiment, the hypotheses, the experimental design, and the results.
Belief in Belief Functions: An Examination of Shafer's Canonical Examples
(North-Holland, 1989) Laskey, Kathryn B.
In the canonical examples underlying Shafer-Dempster theory, beliefs over the hypotheses of interest are derived from a probability model for a set of auxiliary hypotheses. A belief function differs from a Bayesian probability model in that one does not condition on those parts of the evidence for which no probabilities are specified. The significance of this difference in conditioning assumptions is illustrated with two examples giving rise to identical belief functions but different Bayesian probability distributions.
Credibility Models for Multi-Source Fusion
(2006-07) Wright, Edward J.; Laskey, Kathryn B.
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 multi-source fusion process.
A Proposal for a W3C XG on Uncertainty Reasoning for the World Wide Web
(Information Extraction & Transport, Inc. (IET), 2006-11) Laskey, Kenneth J.; Costa, Paulo C. G.; Laskey, Kathryn B.
The Semantic Web envisions effortless cooperation between humans and computers, seamless interoperability and information exchange among web applications, and rapid and accurate identification and invocation of appropriate Web services. At the current stage of evolution in Semantic Web research, there is a growing understanding that a major step towards this vision involves the implementation of principled uncertainty representation and reasoning in SW applications. This position paper introduces initial thoughts on how the World Wide Web Consortium (W3C) Incubator XG process could be employed to move forward the concept of a Web with uncertainty.
Probabilistic Ontologies for Efficient Resource Sharing in Semantic Web Services
(Information Extraction & Transport, Inc. (IET), 2006-11) Costa, Paulo C. G.; Laskey, Kathryn B.; Laskey, Kenneth J.
Service Oriented Architecture (SOA) is a key technology to support interoperability among data and processing resources. Semantic interoperability requires mapping between vocabularies of independently developed resources, a task fraught with uncertainty. Probabilistic ontologies enable representation of knowledge in domains characterized by uncertainty. As such, they promise to improve the quality of service descriptions, enable more thorough analysis of service composition opportunities, and provide a theoretically sound methodology for semantic mapping under uncertainty. This paper defines probabilistic ontologies, discusses their application to SOA, and presents a conceptual scheme for using a federation of ontologies (with both common and probabilistic ontologies) as a semantic mapping tool for service oriented information exchange systems with different levels of service descriptions (including legacy and probabilistic enabled descriptions).
PR-OWL: A Framework for Bayesian Ontologies
(IOS Press, 2006-11) Costa, Paulo C.G.; Laskey, Kathryn B.
Across a wide range of domains, there is an urgent need for a wellfounded approach to incorporating uncertain and incomplete knowledge into formal domain ontologies. Although this subject is receiving increasing attention from ontology researchers, there is as yet no broad consensus on the definition of a probabilistic ontology and on the most suitable approach to extending current ontology languages to support uncertainty. This paper presents two contributions to developing a coherent framework for probabilistic ontologies: (1) a formal definition of a probabilistic ontology, and (2) an extension of the OWL Web Ontology Language that is consistent with our formal definition. This extension, PR-OWL, is based on Multi-Entity Bayesian Networks (MEBN), a first-order Bayesian logic that unifies Bayesian probability with First-Order Logic. As such, PR-OWL combines the full representation power of OWL with the flexibility and inferential power of Bayesian logic.
Bayesian ontologies in AI systems
(2006-07-30T03:10:44Z) Costa, Paulo C. G.; Laskey, Kathryn B.; AlGhamdi, Ghazi
Ontologies have become ubiquitous in current-generation information systems. An ontology is an explicit, formal representation of the entities and relationships that can exist in a domain of application. Following a well-trodden path, initial research in computational ontology has neglected uncertainty, developing almost exclusively within the framework of classical logic. As appreciation grows of the limitations of ontology formalisms that cannot represent uncertainty, the demand from user communities increases for ontology formalisms with the power to express uncertainty. Support for uncertainty is essential for interoperability, knowledge sharing, and knowledge reuse. Bayesian ontologies are used to describe knowledge about a domain with its associated uncertainty in a principled, structured, sharable, and machine-understandable way. This paper considers Multi-Entity Bayesian Networks (MEBN) as a logical basis for Bayesian ontologies, and describes PR-OWL, a MEBN-based probabilistic extension to the ontology language OWL. To illustrate the potentialities of Bayesian probabilistic ontologies in the development of AI systems, we present a case study in information security, in which ontology development played a key role.
An Investigation of Machine Learning Techniques for Use in Training Agents for Military Simulations
(2006-05-05T14:27:34Z) Hieb, Michael R.; Pullen, J. Mark
Agents assist users with performing tasks in computer-based applications. The current practice of building an agent involves a developer programming it for each task it must perform, but agents constructed in this manner are difficult to modify and cannot be trained by a user. Agent- Disciple is a system for training instructable agents through user-agent interaction. In Agent-Disciple a user trains an instructable agent through the interface of the user’s application by providing specific examples of tasks and their solutions, explanations of these solutions and supervises the agent as it performs new tasks. We report here on our work that uses Agent-Disciple to provide a learning agent that can command simulated military forces. Military simulations currently have many limitations in modeling human behavior. While it is relatively straightforward to build models of doctrine, it is difficult to have agents utilize this doctrine in varying contexts. There are too many factors to consider when building deterministic models of behavior, even in well-defined situations. We applied Agent-Disciple to circumvent this problem by using heuristic learning methods. A case study is presented in developing an instructable Company Commander Agent for the Modular Semi-Automated Forces (ModSAF) simulation – a state-of-the-art, real-time, distributed interactive military simulation currently utilized in large-scale training exercises. A ModSAF user can train the Company Commander Agent interactively, using the ModSAF interface, to perform various military missions using the Captain system based on Agent-Disciple. A training session with the agent illustrates the different types of learning interactions available in Agent-Disciple.
Detecting Threatening Behavior Using Bayesian Networks
(2006-03-06T15:11:39Z) AlGhamdi, Ghazi; Laskey, Kathryn B.; Wang, Xun; Barbará, Daniel; Shackelford, Thomas; Wright, Edward J.; Fitzgerald, Julie
This paper presents an innovative use of human behavior models for detecting insider threats to information systems. While most work in information security concerns detecting and responding to intruders, violations of system security policy by authorized computer users present a major threat to information security. A promising approach to detection and response is to model behavior of normal users and threats, and apply sophisticated inference methods to detect patterns of behavior that deviate from normal behavior in ways suggesting a possible security threat. This paper presents an approach, based on multi-entity Bayesian networks, to modeling user queries and detecting situations in which users in sensitive positions may be accessing documents outside their assigned areas of responsibility. Such unusual access patterns might be characteristic of users attempting illegal activities such as disclosure of classified information. We present a scalable proof of concept behavior model, provide an experimental demonstration of its ability to detect unusual access patterns in simulated situations, and describe future plans to increase the realism and fidelity of the model.
Modeling Insider Behavior Using Multi-Entity Bayesian Networks
(2006-03-06T15:01:08Z) AlGhamdi, Ghazi; Laskey, Kathryn B.; Wright, Edward J.; Barbará, Daniel; Chang, K.C.
This paper tackles a key aspect of the information security problem: modeling the behavior of insider threats. The specific problem addressed by this paper is the identification of malicious insider behavior in trusted computing environments. Although most security techniques in intrusion detection systems (IDS’s) focus on protecting the system boundaries from outside attacks, defending against an insider who attempts to misuse privileges is an equally significant problem for network security. It is usually assumed that users who are given access to network resources can be trusted. However, the eighth annual CSI/FBI 2003 report found that insider abuse of network access was the most cited form of attack or abuse. 80% of respondents were concerned about insider abuse, although 92% of the responding organizations employed some form of access control mechanism [7]. Therefore, though insider users are legally granted access to network resources, it is essential to protect against misuse by insiders. This paper presents a scalable model to represent insider behavior. We provide simulation experiments to demonstrate the ability of the model to detect threat behavior. Information security objectives can be accomplished through a layered approach that represents several lines of defense. This approach constitutes one of these lines of defense.
PLASMA: Combining Predicate Logic and Probability for Information Fusion and Decision Support
(2006-02-06T16:47:07Z) Fung, Francis; Laskey, Kathryn B.; Pool, Michael; Takikawa, Masami; Wright, Edward J.
Decision support and information fusion in complex domains requires reasoning about inherently uncertain properties of and relationships among varied and often unknown number of entities interacting in differing and often unspecified ways. Tractable probabilistic reasoning about such complex situations requires combining efficient inference with logical reasoning about which variables to include in a model and what the appropriate probability distributions are. This paper describes the PLASMA architecture for predicate logic based assembly of situationspecific probabilistic models. PLASMA maintains a declarative representation of a decision theoretically coherent first-order probabilistic domain theory. As evidence about a situation is absorbed and queries are processed, PLASMA uses logical inference to reason about which known and/or hypothetical entities to represent explicitly in the situation model, which known and/or uncertain relationships to represent, what functional forms and parameters to specify for the local distributions, and which exact or approximate inference and/or ptimization techniques to apply. We report on a prototype implementation of the PLASMA architecture within IET’s Quiddity*Suite, a knowledge-based probabilistic reasoning toolkit. Examples from our application experience are discussed.
MEBN: A Logic for Open-World Probabilistic Reasoning
(2006-02-03T18:07:33Z) Laskey, Kathryn B.
Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Probability is the most well-understood and widely applied logic for computational scientific reasoning under uncertainty. As theory and practice advance, general-purpose 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 first-order logic with probability theory. This paper presents such an integrated logical foundation. A formal specification is presented for multi-entity 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 first-order 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 combing 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 first-order Bayesian knowledge representation, and point the way toward Bayesian languages suitable for general-purpose knowledge representation and computing. Because first-order Bayesian logic contains classical first-order logic as a deterministic subset, it is a natural candidate as a universal representation for integrating domain ontologies expressed in languages based on classical first-order logic or subsets thereof.
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.
Bayesian Semantics for the Semantic Web
(George Mason University, 2005-07-12) Costa, Paulo C. G.
Uncertainty is ubiquitous. Any representation scheme intended to model real-world actions and processes must be able to cope with the effects of uncertain phenomena. A major shortcoming of existing Semantic Web technologies is their inability to represent and reason about uncertainty in a sound and principled manner. This not only hinders the realization of the original vision for the Semantic Web (Berners-Lee & Fischetti, 2000), but also raises an unnecessary barrier to the development of new, powerful features for general knowledge applications. The overall goal of our research is to establish a Bayesian framework for probabilistic ontologies, providing a basis for plausible reasoning services in the Semantic Web. As an initial effort towards this broad objective, this dissertation introduces a probabilistic extension to the Web ontology language OWL, thereby creating a crucial enabling technology for the development of probabilistic ontologies. The extended language, PR-OWL (pronounced as “prowl”), adds new definitions to current OWL while retaining backward compatibility with its base language. Thus, OWL-built legacy ontologies will be able to interoperate with newly developed probabilistic ontologies. PR-OWL moves beyond deterministic classical logic (Frege, 1879; Peirce, 1885), having its formal semantics based on MEBN probabilistic logic (Laskey, 2005). By providing a means of modeling uncertainty in ontologies, PR-OWL will serve as a supporting tool for many applications that can benefit from probabilistic inference within an ontology language, thus representing an important step toward the World Wide Web Consortium’s (W3C) vision for the Semantic Web. In addition, PR-OWL will be suitable for a broad range of applications, which includes improvements to current ontology solutions (i.e. by providing proper support for modeling uncertain phenomena) and much-improved versions of probabilistic expert systems currently in use in a variety of domains (e.g. medical, intelligence, military, etc).
PR-OWL: A Bayesian Framework for the Semantic Web
(2005-11-07) Costa, Paulo C. G.; Laskey, Kathryn B.; Laskey, Kenneth J.
This paper addresses a major weakness of current technologies for the Semantic Web, namely the lack of a principled means to represent and reason about uncertainty. This not only hinders the realization of the original vision for the Semantic Web, but also creates a barrier to the development of new, powerful features for general knowledge applications that require proper treatment of uncertain phenomena. We propose to extend OWL, the ontology language recommended by the World Wide Web Consortium (W3C), to provide the ability to express probabilistic knowledge. The new language, PR-OWL, will allow legacy ontologies to interoperate with newly developed probabilistic ontologies. PR-OWL will move beyond the current limitations of deterministic classical logic to a full first-order probabilistic logic. By providing a principled means of modeling uncertainty in ontologies, PR-OWL will serve as a supporting tool for many applications that can benefit from probabilistic inference within an ontology language, thus representing an important step toward the W3C’s vision for the Semantic Web.
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.
DTB Project: A Behavioral Model for Detecting Insider Threats
(MITRE Corporation, 2005-05) Costa, Paulo C. G.; Laskey, Kathryn B.; AlGhamdi, Ghazi; Barbará, Daniel; Shackelford, Thomas; Mirza, Sepideh; Revankar, Mehul
This paper describes the Detection of Threat Behavior (DTB) project, a joint effort being conducted by George Mason University (GMU) and Information Extraction and Transport, Inc. (IET). DTB uses novel approaches for detecting insiders in tightly controlled computing environments. Innovations include a distributed system of dynamically generated document-centric intelligent agents for document control, object oriented hybrid logic-based and probabilistic modeling to characterize and detect illicit insider behaviors, and automated data collection and data mining of the operational environment to continually learn and update the underlying statistical and probabilistic nature of characteristic behaviors. To evaluate the DTB concept, we are conducting a human subjects experiment, which we will also include in our discussion.
MEBN Logic: A Key Enabler for Network Centric Warfare
(CCRP Publications, 2005-06) Costa, Paulo C. G.; Laskey, Kathryn B.; Takikawa, Masami; Pool, Michael; Fung, Francis; Wright, Edward J.
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; 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 theory is an appropriate technology for modeling human decision-making in complex, ambiguous scenarios; and (2) Bayesian reasoning technology is a promising enabler for Network Centric Warfare. To support both claims, we have applied Multi-Entity Bayesian Networks (MEBN) to model a historical tactical decision from the naval domain. MEBN is a breakthrough Bayesian reasoning system in which complex probabilistic models are constructed from modular components that can be replicated and combined in an infinite variety of ways. MEBN allows models to capture important and subtle aspects of objects and their interrelationships that would be impossible to model using existing technologies. We provide a brief overview of modeling in MEBN and then present our model and the outcome of applying it to a historical scenario. Our results clearly support the validity of our approach.
An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners
(2005-11-18T20:51:11Z) Hudson, Linwood D.; Ware, Bryan S.; Laskey, Kathryn B.; Mahoney, Suzanne M.
Recent events underscore the need for effective tools for managing the risks posed by terrorists. Assessing the threat of terrorist attack requires combining information from multiple disparate sources, most of which involve intrinsic and irreducible uncertainties. This paper describes Site Profiler® Installation Security Planner, a tool initially built to assist antiterrorism planners at military installations to draw inferences about the risk of terrorist attack. Site Profiler applies knowledge-based Bayesian network construction to allow users to manage a portfolio of hundreds of threat/asset pairs. The constructed networks combine evidence from analytic models, simulations, historical data, and user judgments. Site Profiler was constructed using our generic application development environment that combines a dynamically generated object model, a Bayesian inference engine, a graphical editor for defining the object model, and persistent storage for a knowledge base of Bayesian network fragment objects. Site Profiler's human-computer interaction system is tailored to mathematically unsophisticated users. Future extensions to Site Profiler will use data warehousing to allow analysis and validation of the network’s ability to predict the most effective antiterrorism risk management solutions.

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