John Thomas Wilkinson

Fusing multiple Bayesian knowledge sources

We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the probability of a certain event or they may disagree on the direction of causality between two events (e.g., one thinks A causes B while another thinks B causes A). They may even disagree on the entire structure of dependencies among a set of variables in a probabilistic network. In our proposed solution to this problem, we represent the probabilistic models as Bayesian Knowledge Bases (BKBs) and propose an algorithm called Bayesian knowledge fusion that allows the fusion of multiple BKBs into a single BKB that retains the information from all input sources. This allows for easy aggregation and de-aggregation of information from multiple expert sources and facilitates multi-expert decision making by providing a framework in which all opinions can be preserved and reasoned over.

Intent-Driven Insider Threat Detection in Intelligence Analyses

When decisions need to be made in government, the intelligence community (IC) is tasked with analyzing the situation. This analysis is based on a huge amount of information and usually under severe time constraints. As such, it is particularly vulnerable to attacks from insiders with malicious intent. A malicious insider may alter, fabricate, or hide critical information in their analytical products, such as reports, in order to interfere with the decision making process. In this paper, we focus on detecting such malicious insiders. Malicious actions such as disinformation tend to be very subtle and thus difficult to detect. Therefore, we employ a user modeling technique to model an insider based on logged information and documents accessed while accomplishing an intelligence analysis task. We create a computational model for each insider and apply several detection metrics to analyze this model as it changes over time. If any deviation of behavior is detected, alerts can be issued. A pilot test revealed that the computed deviations had a high correlation with insiderspsila cognitive styles. Based on this finding, we designed a framework that minimized the impact of differences in cognitive styles. In our evaluation, we used data collected from intelligence analysts, and simulated malicious insiders based on this data. A high percentage of the simulated malicious insiders were successfully detected.

Modeling complex social scenarios using Culturally Infused Social Networks

Modeling complex real world scenarios require representing and analyzing information from multiple domains including social, economic and political aspects. However, most of the current frameworks in social networks are not generic enough to incorporate multi-domain information or to be applied in different scenarios. Current frameworks also make simplifications in other modeling aspects such as incorporating dynamism and providing multi-scale analyses. Representing culture is critical to truly capture the nuances of various social processes. It also helps to make the framework generic enough to be applied in multiple application domains.We will leverage a novel framework called the Culturally Infused Social Network (CISN) to represent culture using probabilistic reasoning networks called Bayesian Knowledge Bases (BKBs), in representations known as cultural fragments. Cultural fragments model the intent of actors by relating their actions to underlying beliefs and goals. CISN also supports analysis algorithms to make predictions and provide explanations. We validate CISN by simulating the 2006 Somali conflict involving the Islamic Court Union (ICU). The Somali conflict is a complex scenario requiring deep understanding of myriad factors. We focus on analyzing the group stability of ICU, how changing alliance caused conflicts and led to its ultimate demise. We define a metric to measure instability in a group, identify critical factors that led to instability in ICU and provide analyses.

Infusing Social Networks With Culture

Social Network Analysis (SNA) is a powerful tool for analyzing social phenomena that is based on studying how actors are connected or interact with each other. All Social Networks (SNs) are inherently embedded in particular cultures. However, the effect of cultural influence is often missing from SNA techniques. Moreover, to incorporate culture, modeling approaches have to deal with inaccurate, unrealistic, and incomplete cultural data. In order to address this problem, we propose a generic approach to systematically represent culture in the form of relevant factors and relationships, while leveraging relevant social theories, and to infuse them into SNs in order to obtain more realistic and complete analyses. Using two sets of experiments, we validate the effectiveness of our approach and demonstrate the significant advantages obtained through culturally infused SNA.

On a framework for the prediction and explanation of changing opinions

One of the greatest challenges in accurately modeling a human system is the integration of dynamic, fine-grained information in a meaningful way. A model must allow for reasoning in the face of uncertain and incomplete information and be able to provide an easy to understand explanation of why the system is behaving as it is. To date, work in multi-agent systems has failed to come close to capturing these critical elements. Much of the problem is due the fact that most theories about the behavior of such a system are not computational in nature, they come from the social sciences. It is very difficult to successfully get from these qualitative social theories to meaningful computational models of the same phenomena. We focus on analysis of human populations where discerning the opinions of the members of the populace is integral in understanding behavior on an individual and group level. Our approach allows the easy aggregation and de-aggregation of information from multiple sources and in multiple data types into a unified model. We also present an algorithm that can be used to automatically detect the variables in the model that are causing changes in opinion over time. This gives our model the capability to explain why swings in opinion may be experienced in a principled, computational manner. An example is given based on the 2008 South Carolina Democratic Primary election. We show that our model is able to provide both predictions of how the population may vote and why they are voting this way. Our results compare favorably with the election results and our explanation of the changing trends compares favorably with the explanations given by experts.

Capturing User Intent for Analytic Process

We are working on the problem of modeling an analysts intent in order to improve collaboration among intelligence analysts. Our approach is to infer the analysts goals, commitment, and actions to improve the effectiveness of collaboration. This is a crucial problem to ensure successful collaboration because analyst intent provides a deeper understanding of what analysts are trying to achieve and how they are achieving their goals than simply modeling their interests. The novelty of our approach relies on modeling the process of committing to a goal as opposed to simply modeling topical interests. Additionally, we dynamically generate a goal hierarchy by exploring the relationships between concepts related to a goal. In this short paper, we present the formal framework of our intent model, and demonstrate how it is used to detect the common goals between analysts using the APEX dataset.

Impacts of Analysts' Cognitive Styles on the Analytic Process

A user’s cognitive style has been found to affect how they search for information, how they analyze the information, and how they make decisions in an analytical process. In this paper, we propose an approach that uses Hidden Markov Models (HMM) to dynamically capture a user’s cognitive style by automatically exploring the sequence of actions and relevant information with respect to the content of the actions. The evaluation results show that our HMM model achieves an average of 72% recall with the APEX 07 collection. We also study the link between a user’s cognitive style and the various attributes relating to document content during an analytical process. The results show that the “analytic” group tends to focus on documents with significantly more specific information than the “wholist” group. The specific/general attribute of documents can help us in classifying a user’s cognitive styles automatically

A framework for reasoning under uncertainty with temporal constraints

Time is the key stimulus to change, causality and interaction which are the main components of a dynamic world. Therefore, the modeling of knowledge, especially in complex and dynamic domains like economics, sociology, and ecology, must incorporate the concept of time. Although there has been much research over the years on the representation of knowledge (causality, implication, and uncertainty) and on the representation of time, it has been a continuing challenge to unify them in a meaningful and useful fashion. In this paper, we propose a framework for reasoning under uncertainty with temporal constraints. The framework is extended from Bayesian knowledge-bases (BKBs), which represent knowledge in an “if-then” structure and represent uncertainty based on probability theory. By adding temporal constraints to BKBs, the framework provides a comprehensive model that incorporates the semantics of both time and uncertainty.