In this paper, we first describe a system we have implemented that takes expressions of policies, expressed in a fragment of English called SBVR SE (Semantics of Business Vocabulary and Rules Structured English), an OMG standard, and automatically translates them into an executable semantic web formalism (OWL 2 and semantic web rules). Specifically, we describe how these policies can be used to automatically enforce compliance with policies and to reconcile multiple policies specified by independent parties. The scenarios implemented concern information sharing via XMPP (“instant messaging”). We then outline how situations can be characterized as policy-compliant or policy-violating. In some cases, situations are policy compliant or violating because of events and actions that they contain. We show that our formalism supports this analysis.

Accurate seasonal estimates of fiber are needed to maximize profits whether producing sugarcane (Saccharum spp.) for sucrose or ethanol. The main purpose of this study was to determine the effects of sample date and crop cycle on fiber content of three sugarcane cultivars growing on sand and organic (muck) soils, and secondarily to determine if fiber could be reliably estimated 1 mo before the beginning of the harvest season. From September through February, from 2007-2009, fiber content was estimated from monthly sampled stripped stalks of cultivars CP 72-2086, CP 78-1628, and CP 89-2143 growing in three replications of field plots in south Florida in the plant-cane, first-ratoon, and second-ratoon crop cycles on Pompano fine sand (siliceous, hyperthermic Typic Psammaquent) or Margate sand (Siliceous, hyperthermic Mollic Psammaquent), and Torry muck (euic, hyperthermic Typic Haplosaprist) soils. Linear increases in fiber content ranged from 0.07 to 0.28 g kg(-1) d(-1). Quadratic models usually predicted maximum fiber content from December through early January. On sand soils, the cultivar rankings were oft en similar to expectations, with fiber content of CP 78-1628 > CP 89-2143 > CP 72-2086. On the muck soil, CP 78-1628 fiber content was high, but differences between CP 72-2086 and CP 89-2143 were not consistent. For all soils, overall means were oft en not indicative of fiber status due to significant, but inconsistent interactions. Researchers should analyze fiber content whenever they analyze sucrose content, and mills should monitor fiber content daily of unique cultivar x crop cycle x soil deliveries.

We say that a computer program augments the analyst if it can infer facts that are implicit in existing information, but that may be relatively difficult for a human to infer. Among a multitude of reasons, the analyst's task is difficult because (1) reported information to be analyzed and reasoned about often cannot be completely trusted (requiring verification attempts via further collection of information, corroboration where verification is not possible, and/or assumption-based reasoning) and (2) evaluation of trust (reliability, credibility) is context (or situation) dependent. These two sources of difficulty, and the possibility of augmentation-generated false alarms, imply that if one wants to employ the capabilities of an automatic reasoner, the reasoner must be able to deal with these kinds of complexities.

Naturalistic decision-making studies of intelligence analysis have generally focused on information search, collection, and synthesis processes, deemphasizing the initial "problem formulation" phase, in which analysts interpret and contextualize the information request to determine which information to collect. We present the results of two studies focusing on this phase. In the first study, we performed a cognitive task analysis via semistructured interviews with 22 active-duty U.S. Army intelligence analysts to uncover factors that arise in operational environments that complicate problem formulation. The factors discovered (e.g., vague and/or overly narrow intelligence requests) led to a second study probing 6 active-duty U.S. Army intelligence analysts' cognitive strategies with a "think-aloud" protocol as they interpreted and evaluated representative information requests. The study revealed that analysts actively interpret and contextualize an information request. The analysts reframed and broadened the request so that they could respond meaningfully to the underlying intent, then used contextual cues and metainformation to determine the most useful collectors and how effectively the request could be answered in the time allotted. We discuss these results and their implications for both the cognitive modeling of intelligence analysis and the development of training and decision aids for more effective framing and contextualization of information requests.

Foliar nutrient analysis is a useful diagnostic tool to complement soil testing as a best‐management practice with sugarcane (Saccharum spp.). This study was conducted to determine sugarcane production limits at leaf nutrient concentrations less than optimum. Eight Florida sugarcane growers participated in a survey of leaf nutrient values in 2004, 2005, and 2006. A total of 412 leaf samples were collected from individual commercial sugarcane fields, from which there were 389 harvest data/leaf data combinations. Fields were selected to be representative of plant cane, first ratoon, and second ratoon crops; mineral and organic soils of the area; and major commercial sugarcane cultivars. Leaf silicon (Si), magnesium (Mg), and manganese (Mn) concentrations had the strongest correlations with tons sugarcane ha−1 on organic soils, and leaf nitrogen (N), Mg, and Si concentrations had the strongest correlations with tons sugarcane ha−1 on mineral soils. Boundary lines were used to define practical limits of tons sugarcane ha−1 for leaf nutrient concentrations less than optimum. A table was developed that provides approximate leaf concentrations of nine nutrients at which 10 and 25% losses in relative tons sugarcane ha−1 were estimated. Boundary‐line analysis indicated that sugarcane production was most limited nutritionally in survey fields by insufficient Mg, iron, N, and Si on mineral soils and by insufficient Si and Mn on organic soils.

Doctrinally, Priority Intelligence Requirements (PIRs) represent information that the commander needs to know in order to make a decision or achieve a desired effect. Networked warfare provides the intelligence officer with access to multitudes of sensor outputs and reports, often from unfamiliar sources. Counterinsurgency requires evaluating information across all PMESII-PT categories: Political, Military, Economic, Social, Infrastructure Information, Physical Environment and Time. How should analysts evaluate this information? NATO's STANAG (Standard Agreement) 2022 requires that every piece of information in intelligence reports used to answer PIRs should be evaluated along two independent dimensions: the reliability of its source and the credibility of the information. Recent developments in information retrieval technologies, including social search technologies, incorporate metrics of information evaluation, reliability and credibility, such as Google's PageRank. In this paper, we survey various current approaches to automatic information evaluation and explore their applicability to the information evaluation and PIR answering tasks.

During joint operations among multi-national forces it is imperative that the planned courses of action (COA) of coalition units be accurately and precisely communicated between battlefield operating systems, particularly when dealing with highly coordinated maneuvers. A similar need arises in being able to communicate intelligence concerning hypothesized or anticipated enemy courses of action (ECOA). In each case there is the need for a shared representational language for describing ECOAs that can be used with C4 systems. The standard exchange language for sharing such information among NATO forces today is the Joint Command, Control, and Consultation Information Exchange Data Mode (JC3IEDM). In this paper we explore the formal representational requirements for describing ECOAs and evaluate the effectiveness of JC3IEDM for this purpose.

Enemy or Threat Courses of Action are produced during Intelligence Preparation of the Battlefield, during the Military Decision Making Process, and as part of the process of Situation Development. Due to the overwhelming amount of information involved in these processes and the limited time available to intelligence analysts, significant efforts are underway to develop computer based tools to assist in these processes. For these to be successful there needs to be a way for formally representing Enemy/Threat Courses of Action. This paper investigates the requirements for and potential solutions to this problem using OWL, elements of JC3IEDM and the OWL Time ontology.

During joint operations among multi-national forces it is imperative that the planned courses of action (COA) of coalition units be accurately and precisely communicated between battlefield operating systems, particularly when dealing with highly coordinated maneuvers. A similar need arises in being able to communicate intelligence concerning hypothesized or anticipated enemy courses of action (ECOA). In each case there is the need for a shared representational language for describing ECOAs that can be used with C4 systems. The standard exchange language for sharing such information among NATO forces today is the Joint Command, Control, and Consultation Information Exchange Data Mode (JC3IEDM). In this paper we explore the formal representational requirements for describing ECOAs and evaluate the effectiveness of JC3IEDM for this purpose.

: Data fusion systems are being increasingly used to support military planning, decision making, and command and control functions in general. Typically, these systems are designed around the current capabilities of particular data collectors (e.g., sensors) and available processing algorithms. These algorithms incorporate an ontology that reflects the designer's perception of key concepts in the world (e.g., types of threats, classes of vehicles to be tracked) and how these can be parsed by the data fusion systems. As a consequence, these algorithms are limited in their ability to adapt to the dynamic changes that inevitably arise in the operational environment (e.g., new sensors, weapons, and enemy tactics). This frailty is representative of a more generic problem with current approaches to system design that result in rigid systems that are unable to evolve to keep pace with changing operational conditions. In this paper, we present the results of an analysis, design, and development effort intended to move towards robust C2 through evolvable human-in-the-loop data fusion systems. We discuss an evolvable semantic interface we have designed that enables the creation of new concepts within the fusion system, and provide an overview of the prototype evolvable data fusion system architecture we are developing.

Enemy Courses of Action (ECOAs) play a central role in the process of situation development in military decision-making. In order to reason about ECOAs, it would be necessary to adequately represent them in a formalism that allows for automatic reasoning. In this paper, we examine the benefits and drawbacks of representing ECOAs within several frameworks that have been encoded as OWL ontologies.

Data fusion systems are increasingly being used to support military planning and decision making. Typically these systems are designed around the current capabilities of particular data collectors (e.g., sensors) and processing algorithms. They incorporate an ‘ontology’ that reflects the designer's perception of the key features of the world (e.g., types of threats, classes of vehicles to be tracked) and how these can be parsed by the data fusion systems. As a consequence they are limited in their ability to adapt to the dynamic changes that inevitably arise in the operational environment (e.g., new sensors, weapons, tactics). This is representative of a more generic problem with current approaches to system design that result in rigid systems that are unable to evolve to keep pace with changing operational conditions. In this paper we present the results of an analysis, design, and development effort intended to move away from traditional data fusion systems towards evolvable human-in-the-loop data fusion systems. We discuss the analysis we conducted in support of an evolvable system design and provide an overview of the prototype evolvable data fusion system architecture we are developing.

Abstract : Commanders require relevant information about background information in order to exercise effective command and control (C2). METT-TC factors (Mission, Enemy, Terrain & Weather, Troops, Time Available and Civil Considerations) represent the canonical, militarily significant background against which information is evaluated and military decisions are made. If this background is to be encoded, shared, and, ultimately, processed and reasoned about by computers or computer-assisted C2 systems, the METT-TC background must be represented in some standard format with a shared computer-processable semantics. The JC3IEDM (Joint Command, Control, and Consultation Information Exchange Data Model) represents several years of effort by NATO's Multinational Interoperability Programme at developing a representation of military situations in order to support communication and interoperability among NATO forces. All information to be shared by participants must, therefore, be representable within JC3IEDM. In this paper, we point out aspects of METT-TC that are not currently or not completely representable in JC3IEDM. These include aspects such as cover and concealment, fields of fire, and mission purpose. We end by suggesting ways in which JC3IEDM can be extended to represent these aspects of METT-TC factors.

: Situation assessment (SA) involves deriving relations among entities, e.g., the aggregation of object states (i.e., classification and location). While SA has been recognized in the information fusion and human factors literature, there still exist open questions regarding knowledge representation and reasoning methods to afford SA. For instance, while lots of data is collected over a region of interest, how does this information get presented to an attention constrained user? The information overload can deteriorate cognitive reasoning so a pragmatic solution to knowledge representation is needed for effective and efficient situation understanding. In this paper, we present issues associated with Level 2 Information Fusion (Situation Assessment) including: (1) user perception and perceptual reasoning representation, (2) knowledge discovery process models, (3) procedural versus logical reasoning about relationships, (4) userfusion interaction through performance metrics, and (5) syntactic and semantic representations. While a definitive conclusion is not the aim of the paper, many critical issues are proposed in order to characterize future successful strategies for knowledge representation, presentation, and reasoning for situation assessment.

Situation assessment (SA) involves deriving relations among entities, e.g., the aggregation of object states (i.e. classification and location). While SA has been recognized in the information fusion and human factors literature, there still exist open questions regarding knowledge representation and reasoning methods to afford SA. For instance, while lots of data is collected over a region of interest, how does this information get presented to an attention constrained user? The information overload can deteriorate cognitive reasoning so a pragmatic solution to knowledge representation is needed for effective and efficient situation understanding. In this paper, we present issues associated with Level 2 (Situation Assessment) including: (1) user perception and perceptual reasoning representation, (2) knowledge discovery process models, (3) procedural versus logical reasoning about relationships, (4) user-fusion interaction through performance metrics, and (5) syntactic and semantic representations. While a definitive conclusion is not the aim of the paper, many critical issues are proposed in order to characterize future successful strategies to knowledge representation and reasoning strategies for situation assessment.

The primary goal of this effort was to understand the problems faced by military intelligence analysis personnel as well as how, and to what degree, the identification of these problems could guide the development of computational support systems. To develop this understanding, we performed a literature review, knowledge elicitation interviews and a cognitive task analysis (CTA) in the domain of Army Intelligence Analysis at the Brigade Combat Team. This effort consisted of identifying: (1) the major functions or cognitive tasks entailed in Army Intelligence Analysis; and (2) the complexities in the domain that pose challenges to performance of these cognitive tasks. Identifying the cognitive tasks and the challenges faced in performing those tasks provided a basis for determining opportunities for more effective support of human information processing and decision-making. In this paper, we document selected results of this analysis effort.