Michael R. Hieb

Using Web services to integrate heterogeneous simulations in a grid environment

The distributed information technologies collectively known as Web services recently have demonstrated powerful capabilities for scalable interoperation of heterogeneous software across a wide variety of networked platforms. This approach supports a rapid integration cycle and shows promise for ultimately supporting automatic composability of services using discovery via registries. This paper presents a rationale for extending Web services to distributed simulation environments, together with a description and examples of the integration methodology used to develop significant prototype implementations, and argues for combining the power of Grid computing with Web services to further expand this demanding computation and database access environment.

Using Web Services to Integrate Heterogeneous Simulations in a Grid Environment

The distributed information technologies collectively known as Web services recently have demonstrated powerful capabilities for scalable interoperation of heterogeneous software across a wide variety of networked platforms. This approach supports a rapid integration cycle and shows promise for ultimately supporting automatic composability of services using discovery via registries. This paper presents a rationale for extending Web services to distributed simulation environments, including the High Level Architecture (HLA), together with a description and examples of the integration methodology used to develop significant prototype implementations. A logical next step is combining the power of Grid computing with Web services to facilitate rapid integration in a demanding computation and database access environment. This combination, which has been called Grid services, is an emerging research area with challenging problems to be faced in bringing Web services and Grid computing together effectively.

Teaching intelligent agents: the disciple approach

The ability to build intelligent agents is significantly constrained by the knowledge acquisition effort required. Many iterations by human experts and knowledge engineers are currently necessary to develop knowledge‐based agents with acceptable performance. We have developed a novel approach, called Disciple, for building intelligent agents that relies on an interactive tutoring paradigm, rather than the traditional knowledge engineering paradigm. In the Disciple approach, an expert teaches an agent through five basic types of interactions. Such rich interaction is rare among machine learning (ML) systems, but is necessary to develop more powerful systems. These interactions, from the point of view of the expert, include specifying knowledge to the agent, giving the agent a concrete problem and its solution that the agent is to learn a general rule for, validating analogical problems and solutions proposed by the agent, explaining to the agent reasons for the validation, and being guided to provide new k...

Operations Intent and Effects Model

Military missions in the 21st century are characterized by combinations of traditional symmetric conventional warfare, irregular warfare, and operations other than war.The inherent uncertainty in an actual mission and the variety of potential organizations (e.g. multi-agency, non-governmental, private volunteer, international, international corporations) from several countries that support the mission makes collaboration and co-ordination a key capability for command and control. The ability to communicate and automatically process intent and effects is vital in order for a commander to cooperate with other organizations and agencies and lead subordinates in such a way that the overall mission is completed in the best possible way, including exploitation of fleeting opportunities, i.e. enable for self-synchronization amongst teams and allow for subordinate initiatives. However, intent and effects are often absent in the current and forthcoming digitalized information models, and if intent and effects are present it is likely to be found that the representations are made as free-text fields based on natural language. However, such messages are very difficult to disambiguate, particularly for automated machine systems. The overall objective for the Operations Intent and Effects Model is to support operational and simulated systems by a conceptual intent and effects model and a formalism that is human and machine interpretable.

Geospatial challenges in a net centric environment: actionable information technology, design, and implementation

Terrain and weather effects represent fundamental battlefield information supporting situation awareness and the decision-making processes for Net Centric operations. Sensor information can have a greater impact when placed within a terrain and weather contextual framework. Realizing the promised potential of Net Centric operations is challenging with respect to these effects, since these effects can both enhance or constrain force tactics and behaviors, platform performance (ground and air), system performance (e.g. sensors) and the soldier. We have defined a methodology that starts with military objectives and determines the most useful terrain products to support these missions, taking into account weather effects and sensors. From this methodology we have designed a number of technical standards and components. A key standard is geospatial Battle Management Language (geoBML) to represent Mission input to Geospatial and Sensor Products. An example of components for creating these products are those in the Battlespace Terrain Reasoning and Awareness (BTRA) system. These standards and components enable interoperability between force elements that address not only syntactic consistency, but consistency of both a lexical and semantic representation to realize shared, coherent awareness. This paper presents a systemic approach for successful resolution of these challenges and describes an Actionable Geo-environmental Information Framework (AGeIF).

Battle Management Language (BML) As an Enabler

According to its definition, a Battle Management Language (BML) is an unambiguous language used to command and control forces and equipment conducting military operations and to provide for situational awareness and a shared, common operational picture. In this paper, we will argue that the use of a BML enables military units to utilize state-of-the-art IT techniques for todays operations. In particular, we will discuss the benefits of using BML for military communications in multinational endeavors, for the use of simulation systems in staff training, for decision support, and for automatic information fusion.

An integrated mission and cyber simulation for Air Traffic Control

Worldwide statistics show that air traffic has been growing significantly in recent years. In order to maintain safety, new technologies and architectures are constantly being proposed and deployed. However, several of those technologies contain design flaws and security vulnerabilities which, if properly exploited, can affect the Air Traffic Control (ATC) system in such a way that its effects could range from simple flight delays (economic loss) to air disasters (loss of life). In order to better assess the new generation of air traffic services, this paper presents a simulation/emulation framework based on open source tools to able to evaluate the effects of cyber-attacks and network/communication failures on Air Traffic Control. It presents a case study on Automatic Dependent Surveillance - Broadcast (ADS-B) technology, with real implementations of cyber-attacks at the link layer. Furthermore, mitigation/defense mechanisms are also evaluated from a mission-assurance perspective.

Teaching an automated agent to monitor the electrical power system of an orbital satellite

Abstract This paper illustrates an application of the Disciple knowledge acquisition methodology to build an intelligent adaptive agent for monitoring the electrical power system of an orbital satellite. This methodology is used by an expert to build and train an agent in much the same way that the expert would teach a human apprentice—by giving the agent specific examples of problems and solutions, explanations of these solutions, and supervising the agent as it solves new problems. During these interactions, the agent acquires general rules and concepts, continuously extending and improving its knowledge base. The agent learns by synergistically integrating the basic learning strategies: explanation-based learning, learning by analogy, and empirical inductive learning from examples. An important feature of Disciple-based agents is their ability to reason with incomplete and even partially incorrect information. The agent distinguishes between routine problems (problems it knows that it can solve correctly), innovative problems (problems to which it can recommend solutions, but for which it is not certain of the result), and creative problems (problems it is not able to solve). This allows the agent to solve a problem independently (for a routine problem), to ask confirmation of its solution (for an innovative problem), or simply to ask the expert to solve the problem (for a creative problem).

The changing paradigm for integrated simulation in support of Command and Control (C2)

Modern software and network technologies are on the verge of enabling what has eluded the simulation and operational communities for more than two decades, truly integrating simulation functionality into operational Command and Control (C2) capabilities. This deep integration will benefit multiple stakeholder communities from experimentation and test to training by providing predictive and advanced analytics. There is a new opportunity to support operations with simulation once a deep integration is achieved. While it is true that doctrinal and acquisition issues remain to be addressed, nonetheless it is increasingly obvious that few technical barriers persist. How will this change the way in which common simulation and operational data is stored and accessed? As the Services move towards single networks, will there be technical and policy issues associated with sharing those operational networks with simulation data, even if the simulation data is operational in nature (e.g., associated with planning)? How will data models that have traditionally been simulation only be merged in with operational data models? How will the issues of trust be addressed?

Formalizing Operations Intent and Effects for Network-Centric Applications

A Network-Centric approach enables systems to be interconnected in a dynamic and flexible architecture to support multi-lateral, civilian and military missions. Constantly changing environments require commanders to plan for more flexible missions that allow organizations from various nations and agencies to join or separate from the teams performing the missions, depending on the situation. The uncertainty inherent in an actual mission, and the variety of potential organizations that support the mission after it is underway, makes Command Intent (CI) a critical concept for the mission team. Both humans and computerized decision support services need to have the ability to communicate and interpret a shared CI. This paper presents the Operations Intent and Effects Model (OIEM) – a model that relates CI to Effects, and supports both traditional military planning and Effects Based Operation. In the provided example the suggested Command and Control Language is used to express Operations Intent and Effects.