intelligent simulation of the battlefield (isb)

P e r g a m o n Expert Systems With Applications, Vol. 11, No. 2, pp. 227-236, 1996

Published by Elsevier Science Ltd Printed in Great Britain

0957-4174/96 $15.00 + 0.00

S0957-4174(96)00035-8

Intelligent Simulation of the Battlefield (ISB)

GEORGE STONE t

University of Central Florida, Orlando, Florida, U.S.A.

EUGENE RESSLER AND EDWARD LAVELLE

United States Military Academy, West Point, New York, U.S.A.

Abstract--This paper discusses the development of a meta-expert system, the Intelligent Simulation of the BattlefieM (ISB), for assisting military commanders with managing battlefieM information and decision making. The three main components of the ISB are the Standard Army Training System-Training Exercise Development System (SATS-TREDS), the Janus combat simulation model and the Command Support System ( ComSS).

Integrating simulation with artificial intelligence, the three main components of the 1SB merge to enhance the command and control process. ISB creates an environment to measure the effectiveness of battle commanders to focus and operate in a simulated, yet realistic, dynamic, information-driven, knowledge-assisted environment. The front-end training preparation component of the ISB structures the exercise based on mission requirements and tasks. Once the trahffng scenario is specified through SATS- TREDS, the ISB utilizes a command interface designed to build information templates for displaying information based on a commander's profile and the particular mission. The ISB system's network is configured to accommodate the flow of information generated by the Janus simulation program. With assistance by ISRA intelligent agents and associates, the information is then pushed to the ComSS decision support system. Due to the enormous flow of information, only certain, preselected data are queried and reported during the battle. Through the SATS-TREDS program, the remainder of the information is stored for later reference and review for feedback on future training needs.

The ISB system promises to be a beneficial tool for classroom, training and operational environments as it conforms to the demanding requirements of realistic, dynamic and flexible simulation users.

THE ISB CONCEPT

THtS PAPER DESCRIBES a meta-expert system called the Intelligent Simulation of the Battlefield (ISB), which is designed to support military commanders in training and operations. It is a response to the rapid changes in mission and force structure amid shrinking resources that characterize current military environments (McGinnis & Stone, 1994). Units no longer train for set-piece missions on well-known terrain with certain timetables. Instead, they respond to contingencies within extremely broad categories of operations. In this setting, units train to react to whatever mission might come along rather than training to perform specific tasks. Hence training the unit

*To whom all correspondence should be addressed at: 1389 Blue Spruce Court, Winter Springs, FL 32708, U.S.A.

to proficiency in the mission at hand is part o f the mission itself. Rapid design and execution of training are an integral part of success. Manual methods for creating training scenarios, then designing, executing and evaluating training exercises are too slow to be effective. The purpose of the ISB is to assist commanders by providing an artificially intelligent framework to speed the above processes and improve the quality of results.

The three main components of the ISB (shown in Fig. 1) are the Standard Army Training System-Training Exercise Development System (SATS-TREDS), the Janus combat simulation model and the Command Support System (ComSS).

The strategy of the ISB's design is to integrate other systems, existing and planned, as subsystems with artificial intelligence (AI) serving to extend their capabilities and "glue" them into a cohesive whole with the

227

228

Intelligent Si the Battlefield G. Stone et aL

Evaluation

After

t l

FIGURE !. The

following capabilities:

(1) design training scenarios, (2) generate exercise data, (3) administer exercises using the same, modem com-

mand and control systems as for operations and (4) evaluate exercise results.

In this paper, we describe the subsystems and the AI "glue" that binds them into a framework where these capabilities are provided in a useful form. Requirement (3) is difficult because there is as yet no command and control system standard for operations. Hence the ISB includes an experimental command and control system with features likely to be present in any future standard. It serves as a test bed for decision support techniques, also M-based, which are also covered in this paper. Finally, we step through the process of formulating and executing a training exercise with the ISB, concluding with a summary of what has been accomplished and what is left to do.

COMPONENTS OF ISB

The three main components of the ISB are depicted in Fig. 2. At the "front end" is a training planning system,

FIGURE 2. The ISB network configuration.

During

- CIDS - ITIDS

ISB process.

the Standard Army Training System-Training Exercise Development System (SATS-TREDS). SATS-TREDS accepts input that describes the desired training and produces a variety of reports describing how to conduct an appropriate exercise. More importantly, the system emits "seed data" for a simulation of the exercise scenario.

Seed data are passed to the second main component of the system--the Janus constructive combat simulation. Janus serves as a very detailed "world model" of exercise action. In traditional simulation-based training, the simulation would be started and a panel of human observer-controllers would use this world model to synthesize realistic intelligence reports, orders from higher headquarters and other events serving as input to the people being trained. In turn, actions of the trainees would be used to reprogram the simulation, altering the world model to fit those actions. The accuracy of these processes depends completely on the expertise and objectivity of the observer-controllers. ISB replaces some observer-controller functions with intelligent simulation reporting agents, which use expert system techniques to distill realistic exercise events from Janus simulation states.

These events are passed to the third main component of the ISB, the Command Support System (ComSS). McGinnis and Stone (1994) introduced ComSS as an experimental system implementing many features expected in fielded command and control systems of the future. In addition, it has some novel subsystems, developed as part of the ISB, that distinguish it among current efforts. Two, the Intelligent Tailoring Information Delivery System (ITIDS) and the Commander's Informa- tion Display System (CIDS), exploit current theories of human learning to adapt ComSS information displays to the personal style and aptitudes of the using commander.

Intelligent Simulation of the Battlefield 229

Standard Army Training System-Training Exercise Development System (SATS-TREDS)

The U.S. Army's Simulation, Training and Instrumenta- tion Command (STRICOM) has been collaborating with several other Army agencies over the last few years to develop a prototype training planning development system. Soon to be integrated under the Standard Army Training System (SATS), the Training Exercise Develop- ment System, or SATS-TREDS, has focused on the development of an intuitive, user-friendly training software support system to reduce training planning time and effort by automating some of the tedious, time-consum- ing tasks associated with developing training scenarios (Crissey et al., 1994). The training scenarios were developed using a cognitive analysis tool and subject matter expertise. The resulting training plan development model is depicted in Fig. 3.

The key to the SATS-TREDS process is the orienta- tion on developing simulation-based training exercises with focus on the essential training tasks. In SATS- TREDS testing of 104 subjects at two U.S. Army installations, the user's mental models were measured to evaluate their ability to remember the training planning development process after using the manual or the SATS-TREDS computer-based planning tools. Results showed the mental models of the SATS-TREDS group improved while the manual group's mental models did not (Stone, 1996).

Other measures for effectiveness and efficiency used in the SATS-TREDS study can be applied to the ISB process. These measures include task load index scores, performance times, simulation measures of effectiveness and quality ratings. The SATS-TREDS process and its measures of effectiveness and efficiency will be used for developing constructive simulation scenarios and training commanders on command and control (C2) in the ISB environment. This C2 training concept was first

suggested by McGinnis and Stone (1995) for conducting training of planning and decision making in simulations such as the Janus constructive simulation being used for the ISB.

The Janus Constructive Simulation

Estvanik (1994) declares that wargame simulations are used to test the participants on their strategic and tactical decision-making given partial information distorted by time, The development of time-constrained, decision- making situations is the main objective for using simulations in the military. Advances in computer simulations permit the military "to train and equip forces far more effectively and efficiently than ever before" (Krepinevich, 1994, p. 23). By 1997, the U.S. Army intends to spend "about $750 million on the acquisition of simulators and another $400 million on research and development" (U.S. GAO, 1993, p. 2).

Often used in conjunction with learning exercises, simulations assist as problem-solving decision tools and evaluation devices (Keiser & Seeler, 1987). With simulations, problem-solving and decision-making skills can be learned in a sterile, yet dynamic, complex and realistic setting (Keiser & Seeler, 1987). The manager for the simulation must ensure that the key events have realistic meaning to the participants and will stimulate the correct responses for training, which can then be related to operator reliability.

The Janus simulation is one of the constructive training simulations used by the U.S. Army and the U.S. Marine Corps. Written in FORTRAN 77, Janus is a combat simulation program executed on a UNIX operat- ing system. The Janus program is a next-event, stochastic, Monte Carlo simulation that allows exercise participants to train on the command and control of their respective units in an artificial combat environment.

The Janus simulation depicts a military ground and air

Events Tasks Scenarios

Record Mission Assess Training Select Training Conditions Essential Task List Proficiency

Build Calendars and Prioritize Tasks Select Scenarios Schedules

Allocate Tasks to Events Edit Scenarios

Check Training Aids Devices, Simulators and Simulations Constraintsi

Build Input Files and Exercise Products

Capture Assessments

FIGURE 3. The training planning process.

230 G. Stone et aL

battle environment from the perspective of friendly, enemy and neutral forces, while its fidelity causes players to visualize battlefield concepts in dynamic and realistic training scenarios. Since the Janus simulation is prevalent at many military installations as a training system for command and control during mission execution, Janus is an ideal simulation for the ISB model.

The Command Support System (ComSS)

The Command Support System (ComSS) is designed to tailor battlefield information for the commander. The commander needs information quickly to make critical decisions in stressful situations. Information is processed and delivered to the commander at such a high rate of exchange that, all too often, the final benefits of the simulation training exercise are negligible.

In addition to information overload, commanders frequently confront the prospect of making inaccurate and untimely decisions based on partial information synthesis. The problem is not that the information is missing, but that the information was not presented in a logical, concise and meaningful manner to be understood by the commander. Considering the aforementioned problems in information management, the commander must have a computer-based system that enables filtering, templating, visualizing, interpreting and synthesizing information. ComSS assists in this management process.

The efficient use of time and resources is crucial to the commander's success in stressful combat situations. The commander cannot afford to expend time and efforts conducting routine tasks or gleaning irrelevant information. Expert systems can reduce the routine tasks and prompt the system user for information (Hood & Mason, 1987). The development of an extensive knowledge and rule-based system that supports the decision making process of the commander is a key aspect of ComSS to enhance the effectiveness and efficiency of battlefield decision making. Coupled with multimedia, the ability to define and represent the commander's information requirements can be enhanced through artificial intelligence.

The ComSS research program has designed and is developing the following components for this purpose: (1) the Intelligent Tailored Information Delivery System (ITIDS), (2) the Commander's Information Display System (CIDS) and (3) Intelligent Simulation Reporting Agents (ISRA). The first two components address methods for filtering, templating and visualizing information, while the third component provides a way to interpret and synthesize information.

Intelligent Tailoring Information Delivery System (IT1DS). The ComSS research project has explored and prototyped an intelligent multimedia capability to enable flexibility in building user interfaces for command and

control with a decision support system. There is a growing need within the Army for a quick efficient method to distribute information. Information such as briefings, reports, operation orders and much more serve as the conventional methods for parceling information. These conventional methods are fast becoming dated, and what is needed is a new method to present various material in a manner tailored to the listener. The commander may need to ask the computer-based decision support system to provide information in a certain format based on learning style, mission, preferences or personal experiences. In other words, a system is needed to create a better learning environment. Ideally, this type of system enables the commander to understand and retain a higher volume of information than previously possible in a shorter amount of time. The next component of ComSS is being developed as a prototype system to increase the commander's ability to learn and perform in dynamic combat environmentswthe Intelligent Tai- lored Information Display System (ITIDS).

Since the basis of the research is the tailoring of information to various learning styles, the United States Military Academy (USMA) is an immediate source for research in enhancing learning. At West Point, cadets face not only a rigorous academic schedule, but military responsibilities and physical fitness activities place inordinate demands on their time. As a result, cadets must plan and organize to accomplish all that is required within demanding time constraints. Cadets need effective and efficient ways to save time. One possible method is being tested in a USMA computer course, CS383 Information Systems, which teaches the fundamentals of information systems and the technology and software involved.

What distinguishes the CS383 course from a typical class is the multimedia hypertext presentation of the lesson material, slideshows, pictures, sound files, lesson and notes, course available on-line 24 hours a day. The lessons are available on the World Wide Web (WWW) browser for student access to each lesson in the cadet rooms. The individual hypertext markup language (HTML) pages allow the cadet to locate the appropriate lesson page and downloads whatever media type he/she wishes to study. This is a great resource for the student in that it provides an organized collection of the material that is taught. Once the student has access to such a wealth of information, the next step is to present the information in a manner that optimizes learning. There are many researchers who have proposed methods and models for the way people learn. Some of these authors and their theories are now examined.

The discussion begins by inspecting where computer- based information systems are distinctively viewed by everyone as an individual preference. Schneiderman (1992) alleges that

"Even people who enjoy using computers may

Intelligent Simulation of the BattlefieM 231

have very different preferences for interaction styles, pace of interaction, graphics versus tabular presentations, dense versus sparse data presentation, step-by-step work versus all-at-once work, and so on. These differences are important. A clear understanding of personality and cognitive styles can be helpful in designing systems for a specific community of users (p. 25)."

In this case, the specific communi~ o f users are commanders who need clear and concise battlefield information. "Unfortunately, there is no simple tax- onomy of user personality types. An increasingly popular technique to use is the Myers-Briggs type indicator (MBTI) based on Carl Jung's theories of personality type" (Schneiderman, 1992, p. 25). In his theory, Jung conjectured that there were four dichotomies:

• Extroversion versus introversion: Extroverts focus on external stimuli and like variety and action, whereas introverts prefer familiar patterns, rely on their inner ideas and work alone contentedly.

• Sensing versus intuition: Sensing types are attracted to established routines, are good at precise work and enjoy applying known skills, whereas intuitive types like solving new problems and discovering new relations, but dislikes taking time for precision.

• Perceptive versus judging: Perceptive types like to learn about new situations, but may have trouble making decisions, whereas judging types like to make a careful plan, and will seek to carry through the plan even if new facts change the goal.

• Feeling versus thinking: Feeling types are aware of other people's feelings, seek to please others and relate well to most people, whereas thinking types are unemotional, may treat people impersonally and like to put things in logical order (Schneiderman, 1992, pp. 25 and 26).

Studies by Richard Felder, David Kolb and others have shown that students have learning styles or specific ways to learn best (Montgomery, 1995). Richard Felder and Barbara Solomon developed a test of 28 questions to determine a student's learning style (Felder & Solomon, 1988). The Felder Learning Style Model (see Fig. 4) consists of four sliding scales with two learning dimen-

Active Reflective I o I

Sensing Intuitive t o I

Visual Verbal t o I

Global Sequential t o I FIGURE 4. The Felder Learning Style Model.

sions for each scale. For the Felder model, the pairs of dimensions are on

relative opposite extremes. The student is on the continuum between these dimensions. The top scale has the active and reflective dimensions. An active learner tends to act first and question or consider later. The active learner wants to do it, not to think about it. The reflective learner takes time to think and consider before jumping into action. The next scale down has the dimensions sensing and intuitive. The sensing student wants to be taught facts and real world applications. An intuitive person likes concepts, theories and broad ideas. The third scale is the visual and verbal scale. Visual learners want pictures, movies and diagrams. A verbal student learns best with sound, text and speech. The scale on the bottom has the sequential and global dimensions. The sequential learner desires ordered and step-by-step instruction. The global learner wants the big picture before trying to understand the smaller parts.

The ITIDS component will combine adaptive hypermedia with the Felder Learning Style Model. To do this the student takes an on-line test, the Felder test, to determine their learning style. This test is implemented using HTML forms over the WWW. The forms are written in a Visual Basic program that accepts the information from the test, generates a file containing the results and returns a display to the user. The displayed results identify the individual's personal learning style. The user's learning style will be used to tailor what and how the lesson is displayed to the user. A media sorter, also built in Visual Basic, reviews the user's learning style, finds the media for the lesson that the student wishes to study and tailors the instruction to the student. The student enters in his or her name (or codename), selects the lesson to study and observes the HTML page with links to media on the lesson which are sorted in order of utility to learning style. The on-line learning styles test and media sorter allow the user to study in the style where he/she can learn best.

From some of the initial information gained through the study at West Point, there appears to be countless opportunities for application in the 21 st Century Armed Forces. For example, a briefing is presented to two different battalion commanders. However, each commander's unique learning style predicates the use of different briefing formats. Perhaps one commander's briefing needs to be more graphical while the other commander's briefing requires more textual content. The presentations are tailored so that both commanders can take full advantage of the information being briefed, decreasing the communication barrier. Other examples could include a company operations order given to a group of platoon leaders that have visual dominant learning styles. Oversight errors in the execution of the mission might be completely eliminated while the junior leaders retain a majority of the information required to conduct the mission.

232 G. Stone et al.

As the ITIDS is developed, research continues to find the optimal method to present information based on learning styles. ComSS is approaching this problem in phases with the first phase being the automated templating of the displays for the commander. To assist this process, a rule-based expert system will encapsulate learning style theories and construct the commander's template for learning and understanding the battlefield environment. The next component provides an example of displaying the information filtered and templated by the ITIDS module.

Commander's Information Display System (CIDS). The Commander's Information Display System (CIDS) was developed to provide an efficient method for unit commanders to visualize or see important battle information. The CIDS concept seeks to provide the commander with a direct link to battlefield information.

The CIDS developers focused on how the commander receives the information vice what information the commander receives. The current prototype is a remote- audio voice activation device (head-mounted microphone) with a remote video graphics transfer device (televideo glasses). Essentially, the microphone and televideo glasses allow the commander to interface with a decision support system. The commander controls the display input and output using voice-activated software.

Currently, the system interfaces with a computer prototype demonstration of a decision support system. Video input can also be overlaid on the display's background in order for the commander to view remote sensing and detection video from unmanned aerial and ground systems, For ease of use, the CIDS hardware can be linked via a wireless local area network (LAN) to simulations and decision support systems.

The CIDS prototype allows the commander to have hands-free access to a decision support system, and allows the ability to maintain visual contact with the local environment. The prototype is a predecessor to a virtual battlefield presence device that could provide immersive sensations of being in the battle to understand and appreciate the intense situation at hand.

CIDS directly supports ComSS by providing the hardware necessary for a unit commander to gain quick access to vital battlefield information to assess combat situations, and enhance tactical decision making. How- ever, information overload often occurs with the deluge of multiple resources providing data and information for the commander to synthesize.

The CIDS consists of the following hardware:

(1) the display system consists of a visor (similar to an oversized pair of sunglasses) with a projection system and a mirror on the side of the dominant eye;

(2) the voice interface is a headset with a microphone

and earphones to support two-way audio; and (3) the display and voice interfaces connect to a wireless

transceiver worn on the commander's belt.

Three features of CIDS are that it:

(I) displays information (text, graphics, video) on demand with transparent-sunglasses,

(2) has a voice interface to allow hands-free operation and

(3) enables freedom of movement.

All of the CIDS features enable the commander to visualize information that arrives at such a fast rate. CIDS will provide an effective information delivery to avoid overwhelming the commander in viewing the battlefield environment.

Intelligent Simulation Reporting Agent. The Intelligent Simulation Reporting Agent (ISRA) is a model for tools that automate the synthesis of reports from complex simulations producing a surfeit of data. Understanding the large outputs from simulations is a recurrent problem that has traditionally been confronted head-on with computer-assisted visualization (CAV) systems. ISRA is an important generalization of that work. CAV concen- trates on clever filtering, formatting and presentation of data, usually in graphical form, so that patterns and structures become obvious to a human observer. Con- versely, ISRA filters a trace of simulation state so that a desired set of patterns and structures "become obvious" to an expert system employed as an observer.

Just as the human observer of a CAV can act in a prescribed manner upon detecting a pattern or structure, an ISRA can take any programmable action when a condition encoded in the expert system's rule base becomes true. The action of interest here is the formulation of a report based on the accumulated simulation history. The ISB currently implements ISRA in only one formRa generator for the messages that an Army Operations Center would see based on the world model provided by a running Janus simulation. It filters the simulation state to obtain input for an expert system that encodes the knowledge of an expert human observer-controller. The expert system emulates first the process of the human detecting a simulated situation where a message might be sent, then the process of intentionally perturbing the message to account for the "fog of war", the confusion and disarray of battle which degrades communications of units in combat situations.

THE ISB PROCESS

Preparation and Planning

SATS-TREDS to Janus. The SATS-TREDS process focuses on enabling commanders to orient required tasks


for training on the simulation's capabilities and limitations. Once the training tasks have been identified, they must be applied to the simulation scenario. In building the Janus simulation scenarios, two areas for AI are the organization of military task forces by commanders and the initial deployment of the commander's forces. One of the needs in the transition from SATS-TREDS to the Janus simulation is the structure and composition of the task organization of forces which will participate in a combat environment. The list of units, weapons systems, vehicles and personnel who will be in the simulation must be identified at the outset of the simulation. Due to the inordinate amount of information required, there is a need to prioritize and categorize this procedure through a case-based reasoning (CBR) approach. The cases from previous exercises and scenarios can be used as templates for future simulation force organization and compositions. Instead of starting the process with unknown scenario results for a task organizational structure, the use of artificial intelligence, specifically, case-based reasoning permits pre-existent knowledge of the scenario outcome at the onset of the simulation execution. Once the force organization is developed and certified, the next step is the positioning or deployment of the individual combat vehicles and personnel. Initial positions for Janus deployment files must also be identified at the start of integration of the training plan into the simulation. A generic deployment file is checked prior to implementation in the Janus simulation and then converted into the format readable by the simulation. Once the Janus simulation deployment file is ready, it provides the initial locations and routes of the forces for the simulated battle. Normally, the effectiveness or results of the deployment files are not known until after the simulation scenario is executed. Case-based reasoning promises to alleviate the unknowns for this procedure. Using CBR, the commander can evaluate the positions and initial force planning subsequent to the exercise's execution. The overall objective is the complete confirmation that the units are placed in the most effective arrangement for the accomplishment of the mission at hand. The commander uses the CBR capability as an intelligent assistant or tutor in order to learn how and when to use the forces effectively in a task force. After establishing the composition and locations of the forces, the simulation battlefield preparation is completed. The training tasks will be performed in the simulation as applied to the scenario, but the information for all of the actions and tasks involved must still be delivered effectively and efficiently.

ITIDS. Due to the enormous amount of information presented to a commander in a battlefield environment, the commander's intelligent display of information is essential for effective and efficient dissemination of information. The ITIDS process will examine the tasks to develop the initial templates based on the mission. Next

the template will be refined using the results of the commander's learning style determined by adaptive hypermedia and the Felder model in the ITIDS process. Once the initial conditions of the simulation and the tailoring of the information displays are defined, the commander will conduct the ISB training exercise using the Janus simulation and the ComSS components.

Execution

Janus to ComSS. Hood and Mason (1987) note a key difficulty of using Janus to provide information for an exercise conducted with ComSS as the conduit for information provided to the trained unit "an information processing system for real-time problems must, by definition, be able to process incoming information sufficiently and rapidly to meet the time constraints imposed by the function of the system in the external world" (p. 105). Janus provides an extremely rich world model, one whose full details would overwhelm the ComSS data processing facilities. This is not a limitation of the ComSS. It is designed to handle the actual information flow into units during operations. This flow arrives through limited communication channels. It consists largely of intelligence and situation reports from widely dispersed sources that are subject to mispercep- tions and errors. Hence the volume of information is limited by imperfections of its sources and the process of providing it. Indeed, the design focus of ComSS is to improve the command staffs ability to sort and evaluate information to develop an accurate world model from incomplete and potentially inaccurate input information. Traditional simulation-based training employs observer- controller personnel to interpret the rich simulation world model and generate a realistic information flow for the trained unit, then to reflect the trained unit actions (orders, real unit movements, etc.) back into the simulated world. The ISB can accomplish many of the same functions with Intelligent Simulation Reporting Agents.

The ISRA model is shown conceptually in Fig. 5. Simulation-state recordings form a simulation history. A filtered version of the complete history is presented to an expert system structured to generate reports as output. The history filter is a "dumb" preprocessing step that avoids another pitfall summarized well by Hood and Mason (1987, p. 105) "Given fixed hardware, the processing speed of a system, in terms of throughput, will be inversely proportional to its intelligence." Since the ISRA expert must keep up with the full rate of change in the simulation state, we simplify, hence speed its task by doing as much work as possible in advance. This framework is sufficiently general to support three broad functions:

(1) Intelligent Report Generator: Without the expert system, ISRA is equivalent to a visualization system.

234

Hence an alternate view of an ISRA is a visualization system with added intelligence.

(2) New Simulation Layer: If ISRA reports are treated as part of the simulation state (the path depicted as a dotted line in Fig. 5), then ISRA becomes a part of the simulation itself.

(3) Intersystem Data Link: If ISRA reports are meaningful to another system, then ISRA can serve as a programmable intelligent link between those systems.

The initial ISRA implementation is an instance of (3). The simulation in progress is a Janus battlefield simulation generating so-called post-processing files. These describe the state of the simulation over time. Hence they constitute an accumulated history of the simulation. The ISRA monitors their growth over time.

The expert system implementation consists of a database of rules of the form

if antecedent then consequent

The antecedent is a Boolean or fuzzy logic predicate on the universe of simulation histories plus the assertions contained in the consequent parts of other rules. A rule is said tofire when its antecedent becomes true, whereupon the consequent is asserted. The consequent of a rule may contain simple Boolean or fuzzy assertions that affect the firing of other rules. It may also contain report assertions that describe how to construct a report from the simulation state.

The intervening history filter is chosen to abstract essential information from the accumulated history so that the expert system can be made reasonably efficient. In the initial system, the filter is very simple. For each rule, a snapshot of the simulation state is maintained for the last time the rule fired. Rules antecedents operate these recorded states plus the present state of the simulation. Hence they are memory-limited in that a rule firing is based on history only back to the previous firing of the same rule.

In this system, report assertions are themselves complex rule-based functions. The simulation state represents perfect knowledge of the simulated world. Yet the purpose of the system is to synthesize messages that might flow from military units engaged in the simulated battle. Messages are of course produced by people with imperfect knowledge and abilities. Moreover, the "fog of war", the flood of activity and confusion that permeate

G. Stone et al.

battle, exacerbates imperfection in a way also heavily dependent on the simulation state.

For example, an antecedent is true if two simulated units pass within a certain distance of each other with a clear line of sight. To emulate a perfect world, the consequent would assert two observation reports to the unit headquarters, each providing perfect facts on other (observed) units. To account for the fog of war however, the system makes further stochastic decisions on whether each of the reports is sent at all. If a message is sent, the data it contains are perturbed randomly. Coordinates of unit locations are displaced. Size information is changed. Typographical errors are injected. The frequency and magnitude of these errors are modified based on the simulation state: fresh, unchallenged units are less likely to make mistakes, so their reports are closer to perfect fact than battle-weary units under enemy pressure.

Renew

In order to complete the cycle of the ISB process, the information that is gleaned from a training exercise must be captured and reported to the trainer for future retraining and assessment of the training audience. The integration of the ComSS-reported elements and the simulation results lead to the conclusion of the training exercise.

ComSS to SATS-TREDS. As part of the ISB, the Intelligent Status Reporting Agent (ISRA) seeks to integrate the simulation information in an intelligent manner to the decision support system. ComSS enables the commander to visualize this information through the Commander's Information Delivery System (CIDS) and the ISRA model. In this review process, the commander decides whether corrective action should be taken during or after the completion of the simulated battle. Under the ISB environment, the commander has the choice to make decisions or relegate those decisions to preselected intelligent agents who wait for certain conditions to take appropriate action. Since the commander or the intelligent agents partially synthesize all of the information in the ISB, most of the information is routed to an expert system which resides within the SATS-TREDS component and assists in developing the post-simulation-based training results and conclusions. The expert system assists the commander in evaluating success in the simulated battlefield environment. The review process generates feedback that will develop the final proficiency

S t a ¢ ~ -J S i r , ~ . histo~ histmy - ~ A~ims

FIGURE 5. Structure of the ISRA.


of tasks trained through the ISB process. From this point, the cycle of planning, preparation, execution and review is complete. Future training plans will seek to improve any deficiencies noted in the ISB training exercise.

Limitations

The ISB process has several limitations which are both hardware and software dependent. Hardware for the ISB process must be configured to enable communication between devices at various levels and protocols. A router is essential to ensure adequate communication. Since the communication may only be possible over local area networks, there is a limitation on the distance which the ISB will cover. For tactical operations, this may not be realistic and thus may not demonstrate the actual effects of communications in an operational environment, the battlefield. With future technologies which enable longer distances between nodes of communication, this limitation should be overcome.

Another limitation in the ISB process is the software. As developers seek to improve versions of existing legacy simulation and decision support systems, the software interfaces must maintain compatibility for seamless integration of data and information. The robustness of the interfaces to conform to future software versions of the simulations are being considered in the programming of the ISRA interface and the other components of the ISB process interaction files.

CONCLUSION

This paper has outlined the development of the Intelli- gent Simulation of the Battlefield (ISB). As a system of expert systems, ISB will assist commanders in the training integral to mission accomplishment in modern military environments. The three main components of the ISB work together to maximize the training realism and effectiveness. SATS-TREDS and Janus support the creation and execution of realistic training scenarios. ComSS is a command and control system representative of those being developed for future fielding. The ComSS serves as a conduit for exercise information into the trained unit. Moreover, the unique user-adaptive features of the ComSS interface are important in their own right. The components of the ISB have been integrated into a cohesive system using the ISRA as intelligent "glue" to achieve the four desired system capabilities given at the outset:

(1) Design training scenarios. • SATS-TREDS supports exercise planning from

commander's intent. • SATS-TREDS produces Janus seed data.

(2) Generate exercise data. • Janus creates a world

scenario. model of the exercise

• ISRA produces simulated intelligence and situation reports.

(3) Administer exercises using a modem command and control system. • ISRA message traffic is provided to ComSS. • Messages realistically emulate information

received in real operations.

(4) Evaluate exercise results. • The same features of ComSS that support the

commander's assessment of success during operations assist his/her evaluation of training.

Work remaining falls in three areas:

(I) Field test the system in real training and operational environments.

(2) Develop an ISRA that intelligently reflects exercise activity back to the Janus simulation. In the current system, this is still performed by human observer- controllers.

(3) Enrich the ComSS environment for assessing exercise results.

It appears that the usefulness of results in areas 2 and 3 may be highly dependent on the nature of the trained unit and its mission. The authors look forward to field tests within the next few months to further explore these issues with the people who must live with them daily.

Future Directions of ISB

The ISB process has many implications for future Army and Department of Defense models. First of all, the ISB allows researchers to rapidly prototype and test concepts for before full-scale development by users. The first demonstration of concepts work to show developers how the military wants software coded to model certain features and characteristics of systems and their behav- iors. The ISB process will accommodate experiments using the newest technologies on current systems. Sometimes the legacy system is all there is to work with. The researcher must use what is available today to test concepts for tomorrow.

Getting the information is not the only thing; the next requirement is the creative presentation of the information. The hypermedia adaptive interface being developed for the ISB still has the potential for increasing the users' ability to use the system to the fullest extent. By tailoring the system to the user's learning, the system becomes personalized and more effective to the user.

Classrooms of the future must seek to employ the ISB concept. As technology matures and develops in versatil- ity and scaleability, classroom instruction will seek to

236 G. Stone et al.

encompass all available educational media for realism on dangerous situations. Simulation enhances learning since it enables student leaders to practice tasks and err in real- life situations where one mistake is often unacceptable. With identical systems in the classroom and in the operational environment, learning is enhanced in its application to the tasks required for success. The leader/ student must be able to train as he/she will fight. The tools used in war must be as familiar to the commanders as those provided for training.

Lastly, although the ISB is a conceptual system for the military, there arc related applications in the civilian sector. Emergency disaster relief for natural and man- made situations requires command and control of situations in a stressful, life-threatening environment. More practice using emergency management information support systems and simulations is conceivable, namely an "Intelligent simulation of emergency disaster relief operations". Nuclear power plants use simulators and simulations to practice tasks which involve nuclear incidents. Although sports is not as life-threatening an environment as other areas mentioned, there arc financial risks which could be explored through the use of a simulation and associated decision support systems. Likewise, information technology (IT) insertion using future communication networks could be modeled as an "Intelligent Simulation of IT".

All of the applications and future use of simulations merged with an integrated decision support system environment point to the essential need for incorporating users and designers into a new paradigm. This paradigm is an integrated, artificially intelligent model of the target domain, whether it is the battlefield, emergency disasters, nuclear power plants, sports or information technology.

Acknowledgements--We would like to acknowledge those who helped us in formulating the ideas and thoughts which went into this paper. Lieutenant Colonel Michael McGinnis formulated in a white paper the Command Support System which is the impetus and foundation of the Intelligent Simulation of the Battlefield. Major Curtis Carver assisted with technical multimedia knowledge, while Lieutenant

Colonel Chuck Powell shared operational guidance on tactical command and control systems. Also, we thank Mr Michael Banman, the TRADOC Analysis Center Director, who supported our initial work with his inspiration and resources.

The Army Artificial Intelligence Center also funded the overall structure of the ISB, while the Simulation, Training and Instrumenta- tion Command (STRICOM), FORCE XXI Program Office (Fort Knox) and Dr Kirstie Bellman at the Advanced Research Projects Agency (ARPA) have funded the development of ISB components and their linkages. Without such cooperation and faith in our abilities to do the work, the ISB concept would never have materialized as a viable precursor of future simulation environments.

REFERENCES

Crissey, M., Stone, G., Briggs, D. & Mollaghasemi, M. (1994). Training and exercise planning: Leveraging technologies and data. In 16th interservice/lndustry Training Systems and Education Conference Proceedings, pp. 6-I 2. Orlando, FL.

Estvanik, S. (1994). Artificial intelligence in wargames. AI Expert, 9(5), 22-29.

Felder, R. & Solomon, L. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674-681.

Hood, S. & Mason, K. (1987). Knowledge-based systems for real-time applications. In J. R. Quinlan (Ed.), Applications of expert systems, pp. 102-119. Sydney: Addison-Wesley.

Keiser, T. & Seeler, J. (1987). Games and simulations. In R. Craig (Ed.), Training and development hapdbook, 3rd edn, pp. 456--469. New York: McGraw-Hill.

Krepinevich, A. (1994). Keeping the pace with the military-techno- logical revolution. Issues in Science and Technology, Summer, 21-27.

McGinnis, M. L. & Stone, G. (1994). Decision support technology. Military Review, 74(11), 68-75.

McGinnis, M. L. & Stone, G. (1995). Recent army initiatives to develop decision support systems (DSS) for command and control and training planning. In 1995 International Conference on Industry, Engineering, and Management Systems (IEMS) Proceed- ings. University of Central Florida, Orlando, FL.

Montgomery, S. (1995). Addressing diverse learning styles in a large classroom. In 1995 ASEE National Conference Proceedings, 24-28 June 1995, pp. 344-349. Anaheim, CA.

Schneiderman, B. (1992). Designing the user interface. Reading, MA: Addison-Wesley.

Stone, G. (1996). The use of computer-based planning to enhance the effectiveness and efficiency of simulation-based team training. Unpublished doctoral dissertation, University of Central Florida, Orlando, FL.

U.S. General Accounting Office (1993). Army training: Commanders lack guidance and training for effective use of simulations. Gaithersburg, MD: General Accounting Office.

intelligent simulation of the battlefield (isb)

Documents