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New Software Platform Capabilities and Experimentation Campaign for ELICIT
ELICIT is the Experimental Labora tory for Investigating Information-sharing Coll aboration and Trust. A project of the Command and Control Research Prog ram (CCRP) within the Office of the Assistant Secretary of Defense (NII), the ELICIT project deve loped an online multi-user software platform for conducting experiments in information-sharing and tr ust. The initial version of the software allowed researchers to model and experiment with a limited set of Command and Cont rol (C2) organizations, processes and approaches in a computer-instrumented environment. The ELICIT software has since been enhanced to allow organization type to be configurable , thereby allowing researchers to experiment with a wider variety of C2 organizations, processes and appr oaches. In addition, the software has been further enhanced to support software agents as well as hu man participants, greatly expanding the avenues for research. Although the introduction and use of ELIC IT remain relatively recent events, considerable research has been conducted already using this experimentation platform , and the C2 Research Community is gaining commensurate experience and insight into sound research design. Building upon such research and experience, we develop a multidimensional campaign for continued experimentation using the ELICIT platform. The campaign is populated with recent studies and guides future researchers toward high-payoff research areas that can be addressed using ELICIT
The Failed Thermostat: The Illusion of Control in an Information-Rich Age The concept of command and control is central to modern warfare. Command is a legal and behavioral term referring to a designated individual leader’s responsibility and accountability for everything the leader’s unit of command does and does not do. Contro l is a regulatory and scientific term denoting the ability to manage that which is commanded. This paper investigates the use of certain types of control with operating environments that overwhelm commanders’ abilities to do their job – lead and succeed in battle.
Researchers in the Lockheed Martin Advanced Technology Laboratories (ATL) in Cherry Hill, N.J., will work with EW experts at the Raytheon Co. Space and Airborne Systems segment in McKinney, Texas, to field test EW machine learning software written to detect and jam enemy adaptive communications automatically.
DARPA officials have announced their intention to award a sole-source contract to Lockheed Martin ATL for the third phase of the Behavioral Learning for Adaptive Electronic Warfare (BLADE) program, which is developing technology to defeat adaptive wireless communications threats.
Lockheed Martin has developed software algorithms and techniques that could enable U.S. EW systems to learn how to jam new radio frequency threats automatically. Now Lockheed Martin will work with Raytheon to test this software in a tactical EW system under realistic conditions in the field. Lockheed Martin has been working on the DARPA BLADE program since its inception in 2010.
Pattern recognition hardware and software has made it possible for computers to make dramatic progress in computer vision and speech understanding. In contrast, Dr. Pratt said, little headway has been made in “cognition,” the higher-level humanlike processes required for robot planning and true autonomy. As a result, both in the Darpa contest and in the field of robotics more broadly, there has been a re-emphasis on the idea of human-machine partnerships.
“It is extremely important to remember that the Darpa Robotics Challenge is about a team of humans and machines working together,” he said. “Without the person, these machines could hardly do anything at all.”
In fact, the steep challenge in making progress toward mobile robots that can mimic human capabilities is causing robotics researchers worldwide to rethink their goals. Now, instead of trying to build completely autonomous robots, many researchers have begun to think instead of creating ensembles of humans and robots, an approach they describe as co-robots or “cloud robotics.”
Using a Command and Control Language to Simulate Operations in a Multi-Agent Environment
One of the most effective tools in planning complex operations between different organizations is a simulation of what is to be done. Given a common intent, a simulation provides a basis for understanding the different elements of an operation, and thus enables flexibility as a plan is developed and implemented. The state of the art in simulation is with multi-agent environments. Our work is in developing the abilities of agents so that they reason and act correctly in the simulation. We describe an agent engine called 2APL and its communication protocols. One of the most critical problems in simulating military operations is communicating the intent of what is to be achieved to an agent. This intent can be transmitted effectively between humans, but is problematic when working with agent implementations, due to the large amount of interpretation a human performs. We use a language called the Command and Control Lexical Grammar (C2LG), derived from a body of work called Battle Management Language for its precision and C2 semantics. In this paper, we present our experience in using the C2LG and assess the language for use with simulation agents for developing more effective simulations for Complex Endeavors.
Sharing awareness and problem solving: introducing the concepts of embodied knowledge, epistemic and pragmatic action
This contribution will elaborate on the interpretation of shared awareness on the basis of the asymmetry between information and knowledge. It will sketch the main elements associated to the implementation of knowledge computation in the C2 cycle on the basis of the aspects associated to knowledge assets codification and contextualization. Even though the concepts of situation awareness and shared awareness have become now a commonplace in the scientific literature, the concept of awareness remains often considered as a “buzzword” labeling a range of cognitive processes. Relevant definitions may be divided into two classes corresponding to State vs. Process duality. Endsley’s conceptual contribution focuses on individual appreciations and confronts “situation aw areness” to “situation assessment”. Defining and modeling SA has recently evolved to take into account team cognition specificities and to consequently build up team situation awareness (TSA). Teamwork requires information gathering, information sharing, knowledge mobilization and team convergence toward a concrete action. Networking and computational capabilities available at all levels of the military systems on a battlefield and in the C2 chain have come to a turning point where creation, formalization, and distribution of information may be assessed as problem solving features and integrated into a broader investigation of decision making.
Machine Interpretable Representa tion of Commander's Intent
The Network-Centric approach envisioned in the Glob al Information Grid enables the interconnection of systems in a dynamic and flexible architecture to support multi-lateral, civilian and military missions. Constantly changing environments re quire commanders to plan for missions that allow organizations from various nations and agencies to join or separate from the teams performing the missions, depending on the situation, as missions unfold. The uncertainty with 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. With new and innovative information technologies, CI can now be made available to the team of organizations in a coalition environment. Using a flexible and linguistically based approach for representing CI allows Intent to be interpreted and processed by all participants – both humans and machines. CI representations need to be able to express mission team’s purpose, the anticipated End-State of the mission and desired key tasks. In this work, the expression of CI is developed to enable the structure and dynamics of collaboration support
Mission Plan Recognition: Developing Smart Automated Opposing Forces for Battlefield Simulations an d Intelligence Analyses
A key challenge for battlefield simulation is the estimation of enemy courses of action (COAs). Current adversarial COA development is a manual time-consuming process prone to errors due to limited knowledge about the adversary and it s ability to adapt. Development of decision aids that can predict adversary’s intent and range of possible behaviors, as well as automation of such technologies within battlefield simulations, would greatly enhance the efficacy of training and mission rehearsal solutions. In this paper, we describe the development of OPFOR agents that can intelligently learn BLUEFOR’s mission plan. This knowledge will allow OPFOR agent to reason about the intent of BLUE and counteract accordingly to prevent/influence the future BLUEFOR’s operations by affecting current operations, challenging BLUE’s resources, and preparing OPFOR for future battles.
Potential Benefits & Implications of Priv acy Protection and Anonymity for Command & Control through “Hidden Communications Services”
As the style of warfare has changed to support sudden regional conflicts and ad hoc humanitarian missions for disaster relief (e.g., Hurricane Katrina), so has the style of Command & Control (C2) needed to incorporate civilian intelligence sources (non-government organizations-NGOs) and embrace government authorities. It is difficult to predict in advance what sources of intelligence will be used, and if one is communicating with “small civilian cells”; the Internet might be the only available channel. However, the need still exists to protect the sources & methods employed for intelligence gathering from disclosure. Likewise the deployment of military resources, such as naval vessels, needs to be protected even if serving civilian aid. One possible method of protecting intelligence and C2 communications would be through the creation of a “Hidden Communications Web Service” in which the source and destination of IP messaging was kept hidden/anonymous, but authentication and authorization for access could be maintained as needed. The concept of “Onion Routing” (Tor) was developed several years ago by Goldschlag, Reed, and Syverson at the Naval Research Laboratory to provide anonymity on the Internet and has led to many “civilian” implementations world-wide through open-source software (e.g., Tor)
Semantical Machine Understanding
Semantical Machine Understanding is the foundation for automatic sense and decision making of multinational, multicultural, and coalition applications. We show an innovative semantical machine understanding system that can be installed on each node of a network and used as a semantic search engine. Innovations of such a system include 1) text mining: extract concepts and meaning clusters based on contexts using pattern recognition and machine learning; 2) meaning learning: extract knowledge patterns that link human labeled meaning to raw data . The knowledge patterns can be applied to predict future data; and 3) collaborative meaning search : incorporate humans and machines to form a collaborative network to search and enhance the meaning iteratively. In this paper, we also show the feasibility of using a semantic search architecture and discuss the two ways it is drastically different from current search engines: 1) indexes embedded in agents are distributed and customized to the learning and knowledge patterns of their own environment and culture. This allows data providers to maintain their own data in their own environment, but still share indexes across peers; 2) Semantic machine understanding enables discovery of new information rather than popular information.
Enabling Organizational Innovation: Scie ntific Process and Military Experience
Increasing information richness and the changing socio-political environment in recent years have resulted in changes in corporate structure and organization. The growing challenges of organizational and technological complexities require the development of new organizational concepts. The effects of a combination of high complexity and hi gh uncertainty have been recognized before in military settings. To take advantage of new technologies and manage in formation complexity, a theory of Network Centric Operations was developed. Mission Command and Network Centric Operations formulate organizational structure across functional domains (physical, informational, change this cognitive and social), in a way that is also applicable in a business setting. In response to an increase in decision complexity and regulations, academia has developed risk assessment and multi criteria decision analysis tools for use in military and industrial settings. We believe that the combination of military science with multi-criteria decision analysis and risk assessment has the potential to dramatically improve the credibility, efficiency and transparency of strategic and tactical decisions in industrial settings. This paper summarizes the military concepts of MC and NCO, and links them with mental modeling, risk assessment and decision analysis tools. Application of the combined framework for the pharmaceutical industry is also discussed.
Multi-agent System for Rapid TST Decision Support
Our adversaries understand the need to limit their ex posure time to preying USAF eyes. The enemy hides, exposes only during the briefest of time and then hides again. As the enemy adapts to our technology and finds methods to minimize their exposure, the F2T2EA “kill chain” had to be compressed from hours, to seconds. Obvious impediments to fa st Time Sensitive Targets (TST) engagement are aircraft strike time and administrative delay in the kill chain. 21st Century Systems, Incorporated is developing RPAT Technicians to compress the F2T2EA timeline for support to near-instantaneous planning assessment and airborne weapons assignment for a team distributed in a ground control cent er and airborne control platform. Reactive planning against TSTs requires prior lists of “what-if” and “what-to-do” which will naturally accelerate the manual decision process. RP AT Technicians work inside the human decision loop to support the need for immediate answers. The key component of the system is the Decision Agent for Rapid TST, or DART. DARTs encapsulate human and pre-calculated knowledge in order to provide rapid, low-cost recommendations during emerging events. Supporting Technician agents interface with information systems and gather the data needed by DARTs for generating recommendations.
originally posted by: Aqualung2012
a reply to: admirethedistance
This is outdated, publicly available information. No, "this" isn't it... but if this is any indication of what they are capable of... Well, we can only imagine what "it" really is.