The Data and Information Fusion domains have for some time addressed the issues involved with Situation Estimation and Situation Refinement as part of the characterization of the "higher" levels of fusion processing, meaning those levels of processing that deal with more abstract and complex world states of interest that people call "situations". It is usually agreed however that at the moment at least the research in the Data and Information Fusion (DIF) field has by far been on the aspects of estimating single and sometimes multiple-object attributes from composite observational data, and usually from electronic or physics-based sensing devices such as radars and imaging systems, that is, on the so-called "lower" levels of fusion. As both the world and the technology have changed, and as research in the DIF arena has matured, there has been a considerable interest in directing the research to methods for estimating the higher state levels of DIF, usually called Situation Refinement and Threat or Impact Refinement, and related to "Level 2" and "Level 3" of the well-known "JDL" DIF process Model (Ref 1). Note that the "refinement" term is important, implying an awareness of the fact that the focus of DIF processing is almost always on dynamic events in the world; it also reflects the need for a temporally-adaptive, recursive state estimation process.

Maritime Domain Awareness is important for both civilian and military applications. An important part of MDA is detection of unusual vessel activities such as piracy, smuggling, poaching, collisions, etc. Today's interconnected sensorsystems provide us with huge amounts of information over large geographical areas which can make the operators reach their cognitive capacity and start to miss important events. We propose and agent-based situation management system that automatically analyse sensor information to detect unusual activity and anomalies. The system combines knowledge-based detection with data-driven anomaly detection. The system is evaluated using information from both radar and AIS sensors.

Situational awareness is critical on the modern battlefield. A large amount of intelligence information is collected to improve decision-making processes, but in many cases this huge information load is even decelerating analysis and decision-making because of the lack of reasonable tools and methods to process information. To improve the decision making process and situational awareness, lots of research is done to analyze and visualize intelligence information data automatically. Different data fusion and mining techniques are applied to produce an understandable situational picture. Automated processes are based on a data model which is used in information exchange between war operators. The data model requires formal message structures which makes information processing simpler in many cases. In this paper, generated formal intelligence message data is visualized and analyzed by using the self-organizing map (SOM). The SOM is a widely used neural network model, and it has shown its effectiveness in representing multi-dimensional data in two or three dimensional space. The results show that multidimensional intelligence data can be visualized and classified with this technique. The SOM can be used for monitoring intelligence message data (e.g. in purpose of error hunting), message classification and hunting correlations. Thus with the SOM it is possible to speed up the intelligence process and make better and faster decisions.

The process of tracking and identifying developing situations is an ability of importance within the surveillance domain. We refer to this as situation recognition and believe that it can enhance situation awareness for decision makers. Situation recognition requires that many subproblems are solved. For instance, we need to establish which situations are interesting, how to represent these situations, and which inferable events and states that can be used for representing them. We also need to know how to track and identify situations and how to determine the correlation between present information about situations with knowledge. For some of these subproblems, data-driven approaches are suitable, whilst knowledge-driven approaches are more suitable for others. In this paper we discuss our current research efforts and goals concerning template-based situation recognition. We provide a categorization of approaches for situation recognition together with a formalization of the template-based situation recognition problem. We also discuss this formalization in the light of a pick-pocket scenario. Finally, we discuss future directions for our research on situation recognition. We conclude that situation recognition is an important problem to look into for enhancing the overall situation awareness of decision makers.

Research on information fusion and situation management within the military domain, is often focused on data-driven approaches for aiding decision makers in achieving situation awareness. We have in a companion paper identified situation recognition as an important topic for further studies on knowledge-driven approaches. When developing new algorithms it is of utmost importance to have data for studying the problem at hand (as well as for evaluation purposes). This often become a problem within the military domain as there is a high level of secrecy, resulting in a lack of data, and instead one often needs to resort to artificial data. Many tools and simulation environments can be used for constructing scenarios in virtual worlds. Most of these are however data-centered, that is, their purpose is to simulate the real-world as accurately as possible, in contrast to simulating complex scenarios. In high-level information fusion we can however often assume that lower-level problems have already been solved - thus the separation of abstraction - and we should instead focus on solving problems concerning complex relationships, i.e. situations and threats. In this paper we discuss requirements that research on situation recognition puts on simulation tools. Based on these requirements we present a component-based simulator for quickly adapting the simulation environment to the needs of the research problem at hand. This is achieved by defining new components that define behaviors of entities in the simulated world.

Wireless Sensor Networks (WSNs) are systems that may contain hundreds to thousands of low-power and low-cost sensor nodes. The potential applicability of such systems is enormous; security surveillance and intrusion detection systems for smart buildings and military bases, monitoring chemical plants for safety, wireless body area networks for first responders, and monitoring habitats and natural environments for scientific and other purposes, among others. As sensor network technology matures, we expect to witness an increasing number of such systems deployed in the real world. This renders sensor networks more accessible to a wide variety of possible attacks and functional faults, as they are deployed in remote, un-trusted, hostile environments. While different basic cryptographic building blocks and hardened hardware architectures for most sensor network platforms are currently available and allow for protection on a single sensor node basis, such building blocks are not effective in preventing wider scale attacks once a node has been compromised. To this end, UtopiaCompression is proposing Proactive Trust Management System (PTMS) for WSNs. Our solution is based on an easily extensible framework, tailored to deal with the resource constrained WSNs, and uses a combination of novel outlier detection mechanisms and trust management algorithms to effectively cope with common sensor faults and network attack models. Moreover, our solution is based on distributed in-network processing, which significantly improves scalability and extends life time of the system. This paper also discusses the implementation and evaluation of our solution on Sun SPOT sensors.

The work is based on the interactions among the nodes of a wireless sensor network (WSN) to cooperatively process data from multiple sensors. Quality-of-service (QoS) metrics are associated with the quality of fused information: throughput, delay, packet error rate, etc. A multivariate point process (MVPP) model of discrete random events in WSNs establishes stochastic characteristics of optimal cross-layer protocols. In previous work by the author, discreteevent, cross-layer interactions in the MANET protocol are modeled in very general analytical terms with a set of concatenated design parameters and associated resource levels by multivariate point processes (MVPPs). Characterization of the "best" cross-layer designs for the MANET is formulated by applying the general theory of martingale representations to controlled MVPPs. Performance is described in terms of concatenated protocol parameters and controlled through conditional rates of the MVPPs. Assumptions on WSN characteristics simplify the dynamic programming conditions to yield mathematically tractable descriptions for the optimal routing protocols. Modeling limitations on the determination of closed-form solutions versus iterative explicit solutions for ad hoc WSN controls are presented.

The applicability of a sparse sensor network with only two sensor nodes and a small number of directional LIDAR sensors to detect and track humans in an area of surveillance is investigated. The detection and tracking performances are evaluated for various positions of the two nodes as a function of the number of sensors per node and the sensor beamwidths. A quality factor incorporating the area coverage ratio and the position error is introduced to find the best network configuration with a minimal number of sensors yielding a position accuracy sufficient for the task at hand. Extensive simulations and measurements with two laserscanners to emulate the LIDAR sensors were carried out for straight trajectories uniformly distributed over the area of surveillance. In order to improve the tracking performance, we used a Kalman filter based approach. As in our application a spatial mean RMS position error of approx. 0.6 m is sufficient, each of the two sensor nodes must be equipped with 4 LIDAR sensors with a -3dB-beamwidth of 12°.

Detection of radioactive materials in an urban environment usually requires large, portal-monitor-style radiation detectors. However, this may not be a practical solution in many transport scenarios. Alternatively, a distributed sensor network (DSN) could complement portal-style detection of radiological materials through the implementation of arrays of low cost, small heterogeneous sensors with the ability to detect the presence of radioactive materials in a moving vehicle over a specific region. In this paper, we report on the use of a heterogeneous, wireless, distributed sensor network for traffic monitoring in a field demonstration. Through wireless communications, the energy spectra from different radiation detectors are combined to improve the detection confidence. In addition, the DSN exploits other sensor technologies and algorithms to provide additional information about the vehicle, such as its speed, location, class (e.g. car, truck), and license plate number. The sensors are in-situ and data is processed in real-time at each node. Relevant information from each node is sent to a base station computer which is used to assess the movement of radioactive materials.

Threat evaluation is the process in which threat values are assigned to detected targets, based upon the inferred capabilities and intents of the targets to inflict damage to blue force defended assets. This is a high-level information fusion process of high importance, since the calculated threat values are used as input when blue force weapon systems are allocated to the incoming targets, a process often referred to as weapon allocation. Threat values can be calculated from a number of different parameters, such as the position of the closest point of approach (CPA) with respect to blue force defended assets, time required to reach the CPA, the target's velocity, and its type. A number of algorithms for calculating threat values have been suggested throughout literature, however, criteria to evaluate the performance of such algorithms seem to be lacking. In this paper, we discuss different ways to assess the performance of threat evaluation algorithms. In specific, we describe an implemented testbed in which threat evaluation algorithms can be compared to each other, based on a survivability criterion. Survivability is measured by running the threat evaluation algorithms on simulated scenarios and using the resulting threat values as input to a weapon allocation module. Depending on how well the threat evaluation is performed, the ability of the blue force weapon systems to eliminate the incoming targets will vary (and thereby also the survivability of the defended assets). Our obtained results for two different threat evaluation algorithms are presented and analyzed.

Extensive discussions have taken place in recent year regarding impact assessment - what is it and how can we do it? It is especially intriguing in this modern era where non-traditional warfare has caused either information overload or limited understanding of adversary doctrines. This work provides a methodical discussion of key elements for the broad definition of impact assessment (IA). The discussion will start with a process flow involving components related to IA. Two key functional components, impact estimation and threat projection, are compared and illustrated in detail. These details include a discussion of when to model red and blue knowledge. Algorithmic approaches will be discussed, augmented with lessons learned from our IA development for cyber situation awareness. This paper aims at providing the community with a systematic understanding of IA and its open issues with specific examples.

The terrorist attack of 9/11 has revealed how vulnerable the civil aviation industry is from both security and safety points of view. Dealing with several aircrafts cruising in the sky of a specific region requires decision makers to have an automated system that can raise their situational awareness of how much a threat an aircraft presents. In this research, an in-flight array of sensors has been deployed in a simulated aircraft to extract knowledge-base information of how passengers and equipment behave in normal flighttime which has been used to train artificial neural networks to provide real-time streams of normal behaviours. Finally, a cascading of fuzzy logic networks is designed to measure the deviation of real-time data from the predicted ones. The results suggest that Neural-Fuzzy networks have a promising future to raise the awareness of decision makers about certain aviation situations.

Planning a mission to monitor, control or prevent activity requires postulation of subject behaviours, specification of goals, and the identification of suitable effects, candidate methods, information requirements, and effective infrastructure. In an operation that comprises many missions, it is desirable to base decisions to assign assets and computation time or communications bandwidth on the value of the result of doing so in a particular mission to the operation. We describe initial investigations of a holistic approach for judging the value of candidate sensing service designs by stochastic modeling of information delivery, knowledge building, synthesis of situational awareness, and the selection of actions and achievement of goals. Abstraction of physical and information transformations to interdependent stochastic state transition models enables calculation of probability distributions over uncertain futures using wellcharacterized approximations. This complements traditional Monte Carlo war gaming in which example futures are explored individually, by capturing probability distributions over loci of behaviours that show the importance and value of mission component designs. The overall model is driven by sensing processes that are constructed by abstracting from the physics of sensing to a stochastic model of the system's trajectories through sensing modes. This is formulated by analysing probabilistic projections of subject behaviours against functions which describe the quality of information delivered by the sensing service. This enables energy consumption predictions, and when composed into a mission model, supports calculation of situational awareness formulation and command satisfaction timing probabilities. These outcome probabilities then support calculation of relative utility and value.

This paper describes a bio-inspired VISion based actionable INTelligence system (VISINT) that provides automated capabilities to (1) understand objects, patterns, events and behaviors in vision data; (2) translate this understanding into timely recognition of novel and anomalous entities; and (3) discover underlying hierarchies and relationships between disparate labels entered by multiple users to provide a consistent data representation. VISINT is both a system and a novel collection of novel bio-inspired algorithms/modules. These modules can be used independently for various aspects of the actionable intelligence problem or sequenced together for an end-to-end actionable intelligence system. The algorithms can be useful in many other applications such as scene understanding, behavioral analysis, automatic surveillance systems, etc. The bio-inspired algorithms are a novel combination of hierarchical spatial and temporal networks based on the Adaptive Resonance Theory (ART). The novel aspects of this work are that it is an end-to-end system for actionable intelligence that combines existing and novel implementations of various modules in innovative ways to develop a system concept for actionable intelligence. Although there are other algorithms/implementations of several of the modules in VISINT, they suffer from various limitations and often system integration is not considered. The overall VISINT system can be viewed an incremental learning system where no offline training is required and data from multiple sources and times can be seamlessly integrated. The user is in the loop, but due to the semi-supervised nature of the underlying algorithms, only significant variations of entities, not all false alarms, are shown to the user. It does not forget the past even with new learning. While VISINT is designed as a vision-based system, it could also work with other kinds of sensor data that can recognize and locate individual objects in the scene. Beyond that stage of object recognition and localization, all aspects of VISINT are applicable to other kinds of sensor data.

In previous work by the author, parameters across network protocol layers were selected as features in supervised algorithms that detect and identify certain intrusion attacks on wireless ad hoc sensor networks (WSNs) carrying multisensor data. The algorithms improved the residual performance of the intrusion prevention measures provided by any dynamic key-management schemes and trust models implemented among network nodes. The approach of this paper does not train algorithms on the signature of known attack traffic, but, instead, the approach is based on unsupervised anomaly detection techniques that learn the signature of normal network traffic. Unsupervised learning does not require the data to be labeled or to be purely of one type, i.e., normal or attack traffic. The approach can be augmented to add any security attributes and quantified trust levels, established during data exchanges among nodes, to the set of cross-layer features from the WSN protocols. A two-stage framework is introduced for the security algorithms to overcome the problems of input size and resource constraints. The first stage is an unsupervised clustering algorithm which reduces the payload of network data packets to a tractable size. The second stage is a traditional anomaly detection algorithm based on a variation of support vector machines (SVMs), whose efficiency is improved by the availability of data in the packet payload. In the first stage, selected algorithms are adapted to WSN platforms to meet system requirements for simple parallel distributed computation, distributed storage and data robustness. A set of mobile software agents, acting like an ant colony in securing the WSN, are distributed at the nodes to implement the algorithms. The agents move among the layers involved in the network response to the intrusions at each active node and trustworthy neighborhood, collecting parametric values and executing assigned decision tasks. This minimizes the need to move large amounts of audit-log data through resource-limited nodes and locates routines closer to that data. Performance of the unsupervised algorithms is evaluated against the network intrusions of black hole, flooding, Sybil and other denial-of-service attacks in simulations of published scenarios. Results for scenarios with intentionally malfunctioning sensors show the robustness of the two-stage approach to intrusion anomalies.

Wireless sensor networks have become viable solutions to many commercial and military applications. This research focuses on utilizing the I-TRM to develop an architecture which supports adaptive, self-healing, and self-aware intelligent wireless sensor networks capable of supporting mobile nodes. Sensor subsystems are crucial in the development of projects to test complex systems such as the Future Combat System, a multi-layered system consisting of soldiers and 18 subsystems connected by a network. The proposed architecture utilizes the Sensor Web Enablement (SWE), a standard for sensor networks being developed by the Open Geospatial Consortium (OGC), and the Integrated Technical Reference Model (I-TRM), a multi-layered technical reference model consisting of a behavior-centric technical reference model, information-centric technical reference model, and control technical reference model. The designed architecture has been implemented on MPR2400CA motes using the nesC programming language. Preliminary results show the architecture meets needs of systems such as the Future Combat System. The architecture supports standard and tailored sensors, mobile and immobile sensors nodes, and is scalable. Also, functionality was implemented which produces adaptive, self-healing, and self-aware behavior in the wireless sensor network.

Daily sensor data volumes are increasing from gigabytes to multiple terabytes. The manpower and resources needed to analyze the increasing amount of data are not growing at the same rate. Current volumes of diverse data, both live streaming and historical, are not fully analyzed. Analysts are left mostly to analyzing the individual data sources manually. This is both time consuming and mentally exhausting. Expanding data collections only exacerbate this problem. Improved data management techniques and analysis methods are required to process the increasing volumes of historical and live streaming data sources simultaneously. Improved techniques are needed to reduce an analysts decision response time and to enable more intelligent and immediate situation awareness. This paper describes the Sensor Data and Analysis Framework (SDAF) system built to provide analysts with the ability to pose integrated queries on diverse live and historical data sources, and plug in needed algorithms for upstream processing and filtering. The SDAF system was inspired by input and feedback from field analysts and experts. This paper presents SDAF's capabilities, implementation, and reasoning behind implementation decisions. Finally, lessons learned from preliminary tests and deployments are captured for future work.

In this paper, we describe the problem of efficiently supplying high-level fusion services (situation and impact assessment) with adequate information using semantic technology and formulate an optimization problem version of it. We begin by discussing situation awareness and the need for computer tools that assist human analysts and decision makers with their sense-making. Such tools are necessary in part because of the vast amount of information that is available for analysis in today's command and control systems: the human operators need help to sort out the relevant parts. This kind of filtering requirement is however not limited to humans: automatic or semi-automatic fusion tools also need to limit the information they use in their processing. Simple such filtering could be done based on geographical location, but as the number of advanced fusion services used in the command and control system increases, more advanced techniques need to be used. We describe the information supply process when dealing with several (possibly heterogeneous) sources of differing quality and describe the concepts of information view and information scope. We describe how semantic queries can be used to achieve such filtering, and in particular describe this implemented for Impactorium, a framework tool for situation and impact assessment developed by FOI. The threat models in Impactorium previously relied solely on simple indicator tags for information supply. This can be done more robustly by adding semantic queries to the threat models. The paper concludes with a summary and some discussion of future work in this area.

To evaluate security systems, the observer must be analyzed relative to the area of observation and targets expected to be observed therein. The algorithm constructs a probability model for detection in both spatial and temporal references within the area of observation. Targets must be identified with characteristics such as size and event-duration. Additional criteria from a physical security risk assessment can characterize the area of observation and expected targets. Inputs for the equations include base terrain coordinates in a regularly-gridded system, expected viewobstruction changes across a time period, sensor and recording limitations, and events of interest. An event is an action of a physical nature in which the observation platform is expected to detect, corroborate, or validate the occurrence. Temporal and spatial models of observation system weaknesses are generated, and optimal sensor positioning is computed under constraints. Incorporating this into the results allows for consideration that a sensor system may only provide limited characteristics of an event to help identify objects or individuals within the scene. Consideration is given to the ability to immediately respond to an event or place reliance on the system for evidentiary purposes. The rigorous algorithm can be used to analyze physical site security; to identify improvements in observation platforms; and to compute optimal sensor placement. Noted constraints of the current research include optical sensors, predictable obstruction causes, and sensor-placement with characterized pan-tilt-zoom functionality.

Supporting human decision making in tasks such as disaster planning and time-sensitive targeting is challenging because of the breadth and depth of knowledge that goes into the decision making process and the need to reason with this knowledge within tight time constraints. Ontologies are well suited for representing the concepts that humans use in describing the domain of interest. However, ontologies can be costly to develop and, by themselves, are inadequate to capture the kinds of decision making knowledge that arise in practice-for instance, those that refer to multiple ontologies or to established precedent. Such decision making knowledge can be represented by using a knowledge representation formalism that we call decision rules. These decision rules are similar to the rules used in rule based systems but can (a) include primitives from multiple ontologies and primitives that are defined by algorithms that run outside of the rule framework (b) be time dependent and (c) incorporate default assumptions. We report on our ongoing experience in using such a combination of ontologies and decision rules in building a decision support application for time sensitive targeting.

Context plays a significant role in situation resolution by intelligent agents (human or machine) by affecting how the situations are recognized, interpreted, acted upon or predicted. Many definitions and formalisms for the notion of context have emerged in various research fields including psychology, economics and computer science (computational linguistics, data management, control theory, artificial intelligence and others). In this paper we examine the role of context in situation management, particularly how to resolve situations that are described by using fuzzy (inexact) relations among their components. We propose a language for describing context sensitive inexact constraints and an algorithm for interpreting relations using inexact (fuzzy) computations.

When to implement a standard and how much benefit would result from its implementation is often a seat of the pants value judgment. We will address lack of cost/benefit metrics for interoperability standards by presenting a generalized model of the interoperability problem which defines the tasks required to implement an NxN matrix of interoperating system types. The model is then used to assess the work load required to achieve interoperability and quantify the extent to which the introduction of standards reduces the work load as a function of delineated standards characteristics. Characteristics such as format, execution, speed, bandwidth, and, must notably knowledge definition mechanisms are delineated. Standards effectiveness in terms of task costs are then estimated as a function of standards characteristics, latent ambiguities, and number interoperating nodes. Use case studies of several standards and guidelines for standards effectiveness evaluation will be discussed.

In this paper, the controllability of a Mecanum omnidirectional vehicle (ODV) is investigated. An adaptive drive controller is developed that guides the ODV over irregular and unpredictable driving surfaces. Using sensor fusion with appropriate filtering, the ODV gets an accurate perception of the conditions it encounters and then adapts to them to robustly control its motion. Current applications of Mecanum ODVs are designed for use on smooth, regular driving surfaces, and don't actively detect the characteristics of disturbances in the terrain. The intention of this work is to take advantage of the mobility of ODVs in environments where they weren't originally intended to be used. The methods proposed in this paper were implemented in hardware on an ODV. Experimental results did not perform as designed due to incorrect assumptions and over-simplification of the system model. Future work will concentrate on developing more robust control schemes to account for the unknown nonlinear dynamics inherent in the system.

A chipless sensor tag-based radio frequency identification (RFID) technology that allows wireless collection of information from the environment, and the monitoring and accessing of the given information through cyberspace is presented. The developed system consists of a cyber enabled RFID reader and passive chipless RFID sensor tags. The reader is comprised of an analog part that wirelessly communicates with the sensor tags, and a single board computer (SBC) part. Each passive chipless sensor tag consists of a microstrip antenna and a sensor. The sensor information is amplitude modulated in the backscattered signal of the tag. The analog reader part receives the backscattered signal and feeds it to the SBC, which computes the sensor information into a 96 bit serialized global trade item number (SGTIN-96) electronic product code (EPC). Moreover, the SBC makes the information available on a cyberspace-accessible secure user interface. The reported system has been applied for temperature sensing, where the change in temperature at the tag ranging from 27°C to 140°C resulted in a 28% amplitude change at the analog part of the reader. The temperature at the tag has been monitored by accessing the reader through cyberspace using a web-based user interfaces developed for the SBC.

We develop a hierarchical immunological model to detect bot activities in a computer network. In the proposed model antibody (detector)-antigen (foreign object) reactions are defined using negative selection based approach and negative systems-properties are defined by various temporal as well as non-temporal systems features. Theory of sequential hypothesis testing has been used in the literature for identifying spatial-temporal correlations among malicious remote hosts and among the bots within a botnet. We use it for combining multiple immunocomputing based decisions too. Negative selection based approach defines a self and helps identifying non-selves. We define non-selves with respect to various systems characteristics and then use different combinations of non-selves to design bot detectors. Each detector operates at the client sites of the network under surveillance. A match with any of the detectors suggests presence of a bot. Preliminary results suggest that the proposed model based solutions can improve the identification of bot activities.

Detection and patching of coverage holes in Wireless Sensor Networks (WSNs) are important measures of Quality of Service (QoS) for security and other applications that emphasize sensor network coverage. In this paper, we model a WSN using simplicial complexes based on its communication graph by which the network can be represented as connections of sensor nodes without knowing exact locations of nodes. Thus, the coverage problem is converted to a connectivity problem under some assumptions presented in the paper. We discuss two major topics in this paper, namely sensor network coverage hole detection and patching. We present a novel, decentralized, coordinate-free, node-based coverage hole detection algorithm. The algorithm can be implemented on a single node with connectivity information gathered from one-hop away neighbors. Thus, the coverage hole detection algorithm can be run on individual nodes and does not require time-consuming, centralized data processing. The hole-patching algorithm is based on the concept of perpendicular bisector line. Every hole-boundary edge has a corresponding perpendicular bisector and new sensor nodes are deployed on hole-boundary bisectors. Deployment of new sensor nodes maintains network connectivity, while reduces coverage holes.

Detecting network intruders and malicious software is a significant problem for network administrators and security experts. New threats are emerging at an increasing rate, and current signature and statistics-based techniques are failing to keep pace. Intelligent systems that can adapt to new threats are needed to mitigate these new strains of malware as they are released. This research develops a system that uses contextual relationships and information across different layers of abstraction to detect malware based on its qualia, or essence. By looking for the underlying concepts that make a piece of software malicious, this system avoids the pitfalls of static solutions that focus on predefined signatures or anomaly thresholds. If successful, this type of qualia-based system would provide a framework for developing intelligent classification and decision-making systems for any number of application areas.