Modeling large military targets is a challenge as they can be complex systems encompassing myriad combinations of
human, technological, and social elements that interact, leading to complex behaviors. Moreover, such targets have
multiple components and structures, extending across multiple spatial and temporal scales, and are in a state of change,
either in response to events in the environment or changes within the system. Complex adaptive system (CAS) theory can
help in capturing the dynamism, interactions, and more importantly various emergent behaviors, displayed by the targets.
However, a key stumbling block is incorporating information from various intelligence, surveillance and reconnaissance
(ISR) sources, while dealing with the inherent uncertainty, incompleteness and time criticality of real world information.
To overcome these challenges, we present a probabilistic reasoning network based framework called complex adaptive
Bayesian Knowledge Base (caBKB). caBKB is a rigorous, overarching and axiomatic framework that models two key
processes, namely information aggregation and information composition. While information aggregation deals with the
union, merger and concatenation of information and takes into account issues such as source reliability and information
inconsistencies, information composition focuses on combining information components where such components may
have well defined operations. Since caBKBs can explicitly model the relationships between information pieces at various
scales, it provides unique capabilities such as the ability to de-aggregate and de-compose information for detailed analysis.
Using a scenario from the Network Centric Operations (NCO) domain, we will describe how our framework can be used
for modeling targets with a focus on methodologies for quantifying NCO performance metrics.
Modeling real-world scenarios is a challenge for traditional social science researchers, as it is often hard to capture the intricacies and dynamisms of real-world situations without making simplistic assumptions. This imposes severe limitations on the capabilities of such models and frameworks. Complex population dynamics during natural disasters such as pandemics is an area where computational social science can provide useful insights and explanations. In this paper, we employ a novel intent-driven modeling paradigm for such real-world scenarios by causally mapping beliefs, goals, and actions of individuals and groups to overall behavior using a probabilistic representation called Bayesian Knowledge Bases (BKBs). To validate our framework we examine emergent behavior occurring near a national border during pandemics, specifically the 2009 H1N1 pandemic in Mexico. The novelty of the work in this paper lies in representing the dynamism at multiple scales by including both coarse-grained (events at the national level) and finegrained (events at two separate border locations) information. This is especially useful for analysts in disaster management and first responder organizations who need to be able to understand both macro-level behavior and changes in the immediate vicinity, to help with planning, prevention, and mitigation. We demonstrate the capabilities of our framework in uncovering previously hidden connections and explanations by comparing independent models of the border locations with their fused model to identify emergent behaviors not found in either independent location models nor in a simple linear combination of those models.
The major focus in the field of modeling & simulation for network centric environments has been on the physical layer
while making simplifications for the human-in-the-loop. However, the human element has a big impact on the
capabilities of network centric systems. Taking into account the socio-behavioral aspects of processes such as team
building, group decision-making, etc. are critical to realistically modeling and analyzing system performance. Modeling
socio-cultural processes is a challenge because of the complexity of the networks, dynamism in the physical and social
layers, feedback loops and uncertainty in the modeling data. We propose an overarching framework to represent, model
and analyze various socio-cultural processes within network centric environments. The key innovation in our
methodology is to simultaneously model the dynamism in both the physical and social layers while providing functional
mappings between them. We represent socio-cultural information such as friendships, professional relationships and
temperament by leveraging the Culturally Infused Social Network (CISN) framework. The notion of intent is used to
relate the underlying socio-cultural factors to observed behavior. We will model intent using Bayesian Knowledge Bases
(BKBs), a probabilistic reasoning network, which can represent incomplete and uncertain socio-cultural information. We
will leverage previous work on a network performance modeling framework called Network-Centric Operations
Performance and Prediction (N-COPP) to incorporate dynamism in various aspects of the physical layer such as node
mobility, transmission parameters, etc. We validate our framework by simulating a suitable scenario, incorporating
relevant factors and providing analyses of the results.
Modeling Situation awareness (SA) in NCO/NCW environments is inherently challenging due to the complexity of the
underlying network, highly dynamic nature of processes, and the need for real time analysis. In this paper, we present a
performance model for SA using the Network Centric Operations Performance & Prediction (NCO-PP) framework, an
established framework for analyzing and predicting performance of NCO/NCW networks. In this paper, we continue to
formulate a realistic model that represents dynamism in both the information and network spaces and also their effects
on each other. We validate our model via simulations that compare the performance of SA under various information
sharing and filtering paradigms. We provide and define a number of relevant performance metrics for SA and show with
experimental results that modeling the dynamism in the network lead to superior SA. We also show that the performance
of the SA can be significantly improved with proactive resource allocation that takes into account the real time
predictions of the future states of the network and the environment.
Intelligent Foraging, Gathering and Matching (I-FGM) combines a unique multi-agent architecture with a novel partial
processing paradigm to provide a solution for real-time information retrieval in large and dynamic databases. I-FGM
provides a unified framework for combining the results from various heterogeneous databases and seeks to provide
easily verifiable performance guarantees. In our previous work, I-FGM had been implemented and validated with
experiments on dynamic text data. However, the heterogeneity of search spaces requires our system having the ability to
effectively handle various types of data. Besides texts, images are the most significant and fundamental data for
information retrieval. In this paper, we extend the I-FGM system to incorporate images in its search spaces using a
region-based Wavelet Image Retrieval algorithm called WALRUS. Similar to what we did for text retrieval, we
modified the WALRUS algorithm to partially and incrementally extract the regions from an image and measure the
similarity value of this image. Based on the obtained partial results, we refine our computational resources by updating
the priority values of image documents. Experiments have been conducted on I-FGM system with image retrieval. The
results show that I-FGM outperforms its control systems. Also, in this paper we present theoretical analysis of the
systems with a focus on performance. Based on probability theory, we provide models and predictions of the average
performance of the I-FGM system and its two control systems, as well as the systems without partial processing.
Intelligent foraging, gathering and matching (I-FGM) has been shown to be an effective tool for intelligence analysts
who have to deal with large and dynamic search spaces. I-FGM introduced a unique resource allocation strategy based
on a partial information processing paradigm which, along with a modular system architecture, makes it a truly novel
and comprehensive solution to information retrieval in such search spaces. This paper provides further validation of its
performance by studying its behavior while working with highly dynamic databases. Results from earlier experiments
were analyzed and important changes have been made in the system parameters to deal with dynamism in the search
space. These changes also help in our goal of providing relevant search results quickly and with minimum wastage of
computational resources. Experiments have been conducted on I-FGM in a realistic and dynamic simulation
environment, and its results are compared with two other control systems. I-FGM clearly outperforms the control
With the proliferation of online resources, there is an increasing need to effectively and efficiently retrieve data and knowledge from distributed geospatial databases. One of the key challenges of this problem is the fact that geospatial databases are usually large and dynamic. In this paper, we address this problem by developing a large scale distributed intelligent foraging, gathering and matching (I-FGM) framework for massive and dynamic information spaces. We assess the effectiveness of our approach by comparing a prototype I-FGM against two simple controls systems (randomized selection and partially intelligent systems). We designed and employed a medium-sized testbed to get an accurate measure of retrieval precision and recall for each system. The results obtained show that I-FGM retrieves relevant information more quickly than the two other control approaches.
There are four different levels of QoS traffic classes stipulated in UMTS specifications, which UMTS network need to support. These traffic classes are: Conversational, Streaming, Interactive, and Background. The main distinguishing factor between these classes is how delay sensitive the traffic is. Video stream class of traffic for a broadband wireless network has lot of challenges to overcome; principal among them is the preservation of time delay variation between information entities within a flow. In this paper we model video stream traffic over an UMTS network and examine end-to-end delay variation. Using the results we discuss the architectural implications of radio access network (UTRAN), core network, and the video server on the overall delay. In addition we also analyze the performance impacts of the underlying transport and network layer protocols.