Course Of Action Research Articles

Real-Time Prediction of Observed Action Requires Integrity of the Dorsal Premotor Cortex: Evidence From Repetitive Transcranial Magnetic Stimulation.

Studying brain mechanisms underlying the prediction of observed action, the dorsal premotor cortex (PMd) has been suggested a key area. The present study probed this notion using repetitive transcranial magnetic stimulation (rTMS) to test whether interference in this area would affect the accuracy in predicting the time course of object directed actions performed with the right hand. Young and healthy participants observed actions in short videos. These were briefly occluded from view for 600 ms and resumed immediately afterwards. The task was to continue the action mentally and to indicate after each occlusion, whether the action was resumed at the right moment (condition in-time) or shifted. In a first run, single-pulse transcranial magnetic stimulation (sTMS) was delivered over the left primary hand-area during occlusion. In the second run, rTMS over the left PMd was applied during occlusion in half of the participants [experimental group (EG)]. The control group (CG) received sham-rTMS over the same area. Under rTMS, the EG predicted less trials correctly than in the sTMS run. Sham-rTMS in the CG had no effects on prediction. The interference in PMd interacted with the type of manipulation applied to the action’s time course occasionally during occlusion. The performance decrease of the EG was most pronounced in conditions in which the continuations after occlusions were too late in the action’s course. The present results extend earlier findings suggesting that real-time action prediction requires the integrity of the PMd. Different functional roles of this area are discussed. Alternative interpretations consider either simulation of specific motor programming functions or the involvement of a feature-unspecific predictor.

A Semantic Architecture for Expression Fighting Method

In military C2 domain, due to the complexity of war, precise transmission of headquarters’ order is a dilemma, especially when coping with the fighting method. As it composes of tricks or strategies, transmitting and understanding the fighting method by oral words in personal eyesight may cause a lot of semantic vagueness and divergence, hence, a lot of difficulties for understanding and automation are occurring naturally. Aiming at solve the problem, semantic technologies are utilized to express knowledge behind fighting method. After analyzing the implicit meaning of fighting method, COA (Course of Action) and core norm like “event, sequence, action, process, mutual relation, cognition and rule” are well defined at semantic level by OWL and RuleML, as an ascend from traditional grammatical level. Semantic expression is modelled and consequently embodied into the CGF (Computer Generated Force), so as to make CGF comprehending human’s order, and analyzing and reasoning easily.

The modeling and solution of course of action generation based on IN-DEA

The course of action (COA) generation is the course of designing the task planning flow, which is one of the key supporting techniques while the military organization executing aerial operation. Due to the outstanding advantage of influence nets (IN), describing the causal logic between random variable, this proposal will introduce IN into the fast building mechanism of COA model. The COA generation model utilize influence nets is established. The probability propagation method is designed, which is based on influence intensity. An improved differential evolution algorithm (IDEA) is designed to solve the action policy optimized selected model. At last, the simulated result of aerial arrack campaign case show the action policy optimized selected in variety influence constants can improve the capability of cause-effect modeling, and the improved differential evolution algorithm has fine convergent and optimized capability.

Comparison of similarity measures to differentiate players' actions and decision-making profiles in serious games analytics

Three Gameplay Action-Decision (GAD) profiles: Explorer, Fulfiller, and Quitter, have been identified based on individual's decision-making actions and navigational behaviors in situ serious games. The ability to profile trainees using serious games can yield new analytics and insights towards training and learning performance improvement, including the identification of weaknesses or potential training needs in the players towards adaptive training, and the creation of new diagnostics for prescriptive training, retraining, and remediation. Similarity measures of players' in-game course of actions (COAs) have been shown to be a viable approach in differentiating novices from experts in serious games.In this study, we examined and compared several popular similarity measures to see if any measure, or combination of measures, would be viable in differentiating players based on their GAD profiles in serious games. Our findings revealed that similarity measures, while significant in their predicting abilities individually, could gain more strength from one another in combination. More research is needed to create or develop new metrics and methods for players’ action and behavioral profiling in Serious Games Analytics.

The Evaluation of COAs Based on Stochastic Interval Weighted Sum Method

An important element of military operations is the evaluation of courses of action (COAs) in order to overcome emergency situations. In this kind of decision-making situation, it is important to consider several criteria to evaluate the various COAs, and to compare and rank the COAs according to their ability to meet the selected criteria. It is often necessary to balance the importance of these criteria accordingly to commander’s viewpoints. A new method has been proposed for evaluating the COAs that is based on the possibility calculus known as stochastic interval weighted average operator. The method first presents the attributes and its weights by stochastic interval numbers, so empirical and statistical information, as well as for opinions and judgments made by commanders and analysts, all can be integrated, second, the random numbers of sums of independent variables can be generated by Monte Carlo method, lastly, the comparing and sorting of COAs are realized by random variable Comparison. An example is given to demonstrate the feasibility and practicability of the proposed method.

Children's Imitation with Varied Demonstration and Reproduction Delays

Imitation is both a simple (Fadiga et al., 1995) and a complex matching (Wohlschläger et al., 2003) for transforming visual inputs into motor outputs. These matching types determine what and how observed behaviors would be matched and imitated. The current study tests the children's ability to imitate transitive and intransitive action's course demonstrated by an adult model involving necessary and unnecessary sequences for performing goal-directed actions immediately or after varied delays. The goal-directed actions imitation is depended on imitation form (Labiadh et al., 2013) and calling (Meltzoff & Moore, 1998) process. For instance, in immediate imitation, the ability to recall is easy and requests a low memory load (Wohlschläger et al., 2003). In short-term deferred imitation, the delay between demonstration and reproduction is short and the retention of actions is limited and requests a short-term memory (Rumiati & Tessari, 2002). While, in long-term deferred imitation, the demonstration and reproduction delay is long and requests a long-term memory (McDonough et al., 1995).

Modeling and solution of COA development based on timed influence net and game theory

The development of a course of action (COA) is one of key steps in operation planning. Considering the conflict game, resource restriction, and the influence of execution time, this paper establishes a COA development model based on the timed influence net and game theory. The given problem is solved by transforming it into a standard matrix game model. An example is provided to illustrate this model and its solution.

CGF-SDP: Development of Simulation Scenario and Scenario Editor

Military Scenario Definition Language (MSDL) is intended to provide a standard mechanism for loading Military Scenarios independent of the applications. Some customized scenario specifications lack scalability and interoperability with Command and Control (C2) applications. And how to integrate MSDL with simulation models still needs further implementation. This paper extended and modified MSDL to propose a feasible simulation scenario specification based on our existed CGF (Computer Generated Force) model system. A CGF-SDP (Scenario Development tool) was also developed to facilitate generating course of action (COA) of a military scenario graphically based on a GIS system. A simulation scenario was generated by this editor and it was effectively used in a combat simulation system’s execution process.

On using Multiple Interoperating Models to Address Complex Problems

Models, created using different modeling techniques, usually serve different purposes and provide unique insights. While each modeling technique might be capable of answering specific questions, complex problems require multiple models interoperating to complement/supplement each other; we call this Multi-Modeling. This Multi- Modeling approach for solving complex problems is full of syntactic and semantic challenges. In this paper, a systematic methodology for addressing Multi-Modeling problems is presented. The approach is domain specific: Identification of the domain and the supporting modeling techniques is the first step. Then a new Domain Specific Multi-Modeling Workflow Language supported with a Domain Ontology is used to construct the workflow that defines the interoperation of the selected models. The Domain Ontology provides semantic guidance to effect valid model interoperation. The approach is illustrated using a case study from the Drug Interdiction and Intelligence domain. The Joint Inter-Agency Task Force (JIATF) -South, an agency well known for interagency cooperation and intelligence fusion, receives huge amounts of disparate data regarding drug smuggling efforts. Analysis of such data using various modeling techniques is essential in identifying best Courses of Action (COAs). The proposed methodology is illustrated by creating workflows of model interoperations involving Social Networks, Timed Influence Nets, Organization Structures and Geospatial models.

Analysis and evaluation of net‐centric command and control decision processes

PurposeThe purpose of this paper is to present an approach which can evaluate the ability that successfully achieves command and control, both in qualitative and quantitative modes, to improve decision accuracy and speed, as well as construct an executable architecture for analyzing and verifying different decision projects.Design/methodology/approachBy defining command and control (C2) decision architecture and decomposing C2 decision processes into measurable subfunctions, measures and metrics will be associated with each of the lowest level decomposed functions, and will be used to provide support for performance evaluation. Both Markov decision process analysis and conditional probability (CP) logic are used for modeling the decision‐making process of course of action (COA). Meanwhile, an executable architecture constructed by Petri net is applied to logic structural verification and performance evaluation.FindingsThe paper presents an idea and methodology for net‐centric command and control decision‐making process analysis.Research limitations/implicationsThe paper describes and decomposes C2 decision processes for complex missions in uncertain environments.Practical implicationsThe paper could be an important reference of analysis and application in net‐centric command and control of decision making.Originality/valueThe paper combines methodology with qualitative methods (decision process decomposition), quantitative method (Markov decision process analysis and CP logic), as well as structural verification and performance evaluation.

The Enhancement of Mental Models and its Impact on Teamwork

The aim of the present study was to investigate whether enhancing team mental models (TMM), more specifically task models and team interaction models, improved teamwork in dynamic situations. Measures of performance, coordination, and communication were collected during a forest firefighting simulation task (C3Fire) and compared across three learning conditions. The purpose of these learning conditions was to either enhance task TMM by providing additional information on environmental dynamics or team TMM by providing additional information on the roles of each team member and possible interaction strategies. In the control condition, no additional information was provided. Also, task complexity was varied through transparency of courses of action (COA). The results showed better team performance and coordination in conditions with a more obvious COA. However, there was no significant effect of learning condition on team effectiveness. A trend in the data suggests that teams given additional information on team interaction or task factors spent more time communicating. These findings are discussed in the context of previous research and potential avenues for future investigations.

Visualizations and Interaction Methods for Resilient Submarine Decision Support

Submarine commanders must make decisions rapidly to carry out increasingly complex missions. However, the rate of information delivery has outpaced the capacity of the command and control systems that prioritize and filter it. Technology could help commanders filter through data to make decisions, but this decision support must be carefully engineered to support the development of resilient courses of action (COAs). This paper details our experiences applying resilience engineering to submarine decision support. It focuses on designing two features that are essential for a resilient decision support system: (1) user interaction with a decision support system, which blends the user’s operational insights with the technical aspects of the decision support system; and (2) visual representations of trade-offs. The paper ends with a discussion of the lessons learned from this work and a set of recommendations for designing decision support systems.

LG-PACKAGE: New Frontier

Abstract —This paper describes “new frontier” reached in the development of LG-PACKAGE, a set of the Linguistic Geometry (LG) tools, introduced first in 2004. LG is a type of game theory that generates best strategies for all sides in a conflict in real time. The paper describes the main advanced features of the versions (through Version 3.7) of LG-PACKAGE released gradually from 2004 through 2010. These releases converted LG-PACKAGE into the software of industrial strength applicable to the wide scope of defense systems. The US and British defense agencies and the world major defense contractors utilize these tools. Index Terms —Linguistic Geometry, Game Theory, Modeling and Simulation, Search Problems I. I NTRODUCTION INGUISTIC Geometry (LG), a type of game theory, was first introduced in [4] in 1992. A much deeper account in LG is provided in [5]. Future directions of development of LG are considered in [10]. LG-based tools automatically generate winning strategies, tactics, and courses of action (COA) and permit the warfighter to take advantage thereof for mission planning and execution. LG looks far into the future – it is “predictive”. With unmatched scalability, LG provides a faithful model of an intelligent enemy and a unified conceptual model of joint military operations. The LG tools are based on the concept of the LG

A framework to support qualitative reasoning about COAs in a dynamic spatial environment

We propose a framework to reason qualitatively about courses of action (COAs) which need to be executed in a realistic geographic space that may change. Particularly, the framework aims to support human mental ‘What-If’ analysis by simulating the execution of COAs in a virtual geographic environment, which can change during the simulation, and by allowing the user to explore various scenarios (different COAs) and to analyse their outcomes using causal reasoning techniques. In this article, we first present a framework which is based on a conceptual model of spatio-temporal situations, a multi-agent geosimulation platform and qualitative spatio-temporal causal reasoning techniques. Then, we illustrate the framework using a case study.

Management by chance

PurposeThe purpose of this paper is to provide an outline for conceptualizing an unprecedented factor responsible for awesome success or awful failure of every managerial course of action (COA). This typical factor is coined as “the unforeseen or unknown chance cause” (z) in this paper which aims at focusing on the constant interaction between known causes and z that resultantly shape failure or success of a COA.Design/methodology/approachThe approach to this critical topic is primarily based on the method of simple demonstration of z along with its main contender, knowledge factor, following the method of elementary geometric configurations for lucid representation. Finally, this study advocates a regulatory method which may help chief executive officers (CEOs) in their dealings with z in a proactive manner.FindingsThe outcomes of this course of research are: first, there are some unique features of z, namely, omnipresence, variability and regularity. Second, it is the tolerance limit factor (TLF) which, if properly empowered by knowledge quality, can regulate z. Third, management by chance (MBC), philosophically, is an eternal journey to the unforeseeable land of z.Originality/valueThis study enlightens the following new and original thoughts which are equally important for academics and for practitioners. First, z is no longer an exogenous factor as commonly believed, rather it dwells in the core of a firm's survival. Next, the pivotal point is identifying z promptly without any excuse. Finally, only z can provide freedom of thoughts otherwise impossible in this computerized internet bound world.

A Sketch Recognition System for Recognizing Free-Hand Course of Action Diagrams

Military course-of-action (COA) diagrams are used to depict battle scenarios and include thousands of unique symbols, complete with additional textual and designator modifiers. We have created a real-time sketch recognition interface that recognizes 485 freely-drawn military course-of-action sym- bols. When the variations (not allowable by other systems) are factored in, our system is several orders of magnitude larger than the next biggest system. On 5,900 hand-drawn symbols, the system achieves an accuracy of 90% when con- sidering the top 3 interpretations and requiring every aspect of the shape (variations, text, symbol, location, orientation) to be correct.

Effective Course-of-Action Determination to Achieve Desired Effects

An evolutionary algorithm-based approach to identify effective courses of action (COAs) in dynamic uncertain situations is presented. The uncertain situation is modeled using timed influence nets, an instance of dynamic Bayesian networks. The approach makes significant enhancements to the current trial-and-error-based manual technique, which is not only labor intensive but also not capable of modeling constraints among actionable events. The proposed approach is an attempt to overcome these limitations. It automates the process of COA identification. It also allows a system analyst to capture certain types of constraints among actionable events. Because of its parallel search nature, the approach produces multiple COAs that have a similar fitness value. This feature not only gives more flexibility to a decision maker during mission planning, but it can also be used to generalize the COAs if there exists a pattern among them. This paper also discusses a heuristic that further enhances the performance of the approach.

On Simulation-based Wargaming: Comparison of Two Different Methodological Approaches

Wargaming has been neglected within the German Forces for almost fifty years. Only within the last two years, two so-called ―Leitfaden‖ (codes of best practice) have been developed. The first one, elaborated by members of the General Staff Academy, is intended for operational wargaming and fully rests on the traditional analytical comparison of different Courses of Action (CoA). Own and enemy CoAs are played against each other on the foundation of a game between two staff groups. Within this paradigm, simulations are used as evaluation tools. The reasoning is based on the premise that simulation systems are valid representations of reality. A successful simulation run is considered a corroboration of a particular course of action. The second ―Leitfaden,‖ developed at the University of the Federal Armed Forces, is intended for so-called ―tactical wargaming.‖ The two central concepts of this approach are intuition-based decision-making and assumptionbased planning. The core rationale of this method is that all military planning and decision making is liable to what is called the ―fog of war.‖ The irresolvable unpredictability of future developments in all military operations is considered an insurmountable obstacle to all analytical approaches. This approach rests on the claim that pattern recognition and mental simulation of these patterns are the most valuable cognitive functions of expert decision makers. Within this paradigm, simulations are used to support creativity, enhance imagination and trigger self-critical thinking. Simulation systems of complex systems are not necessarily regarded to be valid. This article discusses the pros and cons of the two approaches (―Leitfaden‖) from both theoretical and practical perspective.

Automating Terrain Analysis: Algorithms for Intelligence Preparation of the Battlefield

Terrain information supplies an important context for ground operations. The layout of terrain is a determining factor in arraying of forces, both friendly and enemy, and the structuring of Courses of Action (COAs). For example, key terrain, such as a bridge over an unfordable river, or terrain that allows observation of the opposing forces line of advance, is likely to give a big military advantage to the force that holds it. Combining information about terrain features with hypotheses about enemy assets can lead to inferences about possible avenues of approach, areas that provide cover and concealment, areas that are vulnerable to enemy observation, or choke points. Currently, intelligence officers manually combine terrain-based information, information about the tactical significance of certain terrain features as well as information regarding enemy assets and doctrine to form hypotheses about the disposition of enemy forces and enemy intent. In this paper, we present a set of algorithms and tools for automating terrain analysis and compare their results with those of experienced intelligence analysts.

Manoeuvre theory: Business mission analysis process for high intensity conflict

The third paper in the manoeuvre warfare series, describes a prescriptive approach for the organization's operationalisation of its strategic intent. It discusses the practical planning stages adapted from the military appreciation process. Termed the mission analysis process (MAP) this framework guides planners through the initial mission analysis, the development of a course of action (CoA), followed by a landscape analysis, execution, review, reorientation, and further action if deemed necessary. This paper argues from a military paradigm using both military and business as examples. This paper emphasises the need for porous, flexible and rapid planning processes, devolved decision‐making, and high levels of organizational capability, initiative, trust, and employee competence. Argues that the MAP framework facilitates organizational learning, a heightened sense of organizational participation and commitment, as well as improving business development between management levels and across the organization.