Cognitive Engineering of the Digital Battlefield: A Human-Centered Design Framework

Celestine A. Ntuen

North Carolina A&T State University

Greensboro, NC 27411

Abstract

Cognitive engineering is a new discipline which primarily focuses on a human-centered approach to the design of organizational and technical systems. It draws upon the "human dimensions" of various disciplines. These include cognitive psychology, computation and information sciences, cognitive science and artificial intelligence, and social sciences. The main human dimension derivatives from these disciplines have been studied under the umbrella of "Human Factors". However, Human Factors models deal with design aspects of a system in a non holistic manner, often focusing, for the most part, on the psychological realms of the human as the system user (evaluative approach), rather than the consideration of humans and machines as social entities of the context system. In the past decade, cognitive engineers have attempted to fill this gap.

Within the life cycle of cognitive engineering as a discipline, much emphasis has been placed upon the cognitive dimensions of the human operator. For example, Rasmussen's (1983) taxonomy of skills -, rule - and knowledge-based behaviors attempts to represent the human operator as a three-level psychological mechanism, with each level representing a detachable module of task abstraction. In this same manner, Moran and Carroll (1996) represent the human operator as a three-level computational mechanism for information processing: perception, interpretation, and evaluation. By integrating these models into a computational framework, various ad hoc systems for representing the human operator in complex systems have been developed. Among these are Chris Mitchell's Operator Functional Model (1986) and the U.S. Department of Army's MIDAS (Man-Machine Integrating, Design, and Analysis System) (see, Smith & Tyler, 1997). These systems work well for cognitive task analysis and representation of the human operator's capability in a system design. However, the methods and model representation that address collaboration rules between human operators, software/hardware agents, and other artificial sensors within a system design are yet to be explored in detail. These loopholes limit the application of cognitive engineering as a human-centered design tool.

1.0 Digital Battlefield

Battlefield digitization in an ubiquitous concept for the Army's Force XXI, and its by-product is expected to direct the effectiveness of the Army After Next (AAN) programs. A digitized battlefield can simply be described as a suit of technological apparatus which is fielded either remotely or in situ, and generically tethered around the individual soldier for the purpose of enhancing battlefield situation awareness. The philosophy of the digitized battlefield is for the soldier to "see" and "recognize" the enemy before the latter has the same advantage.

Digitized battlefield concepts carry with them new strategies and tactics for fighting war. These also engender requirements to design new tools to support the war fighter, especially, using "information-on-the move" philosophy to defeat the opponent.

Challenge

A. System Complexity

Generically, a digitized battlefield represents a complex system in all respects. Both the physical and information structures are heterogeneous, uncertain, and adaptive. Each of these characteristics pose individual problems and represent a threat to the traditional rational and linear models for system design. From general systems theory, complexities associated with the physical and information structures of a digitized battlefield can be described in three interaction levels, each with a time dimension. These are:

Based on the level of the above complexities, the cognitive engineer is therefore faced with the grand challenge of designing human-machine systems that can encompass these spatio-temporal characteristics. In particular, in digitized battlefield modeling, many questions must be answered:

B. Degree of System Coupling

Earlier proponents of human-machine systems emphasized "symbiotic" relationships to foster social bonding and cooperation between humans and machines. In a system with multifaceted elements and diverse sensor information, I see a big challenge in terms of cognitive coupling of human mental models with physical objects of the battlefield. This is amplified even further given the dynamicity of the battlefield information.

C. Human -System Interaction

Human-System Interaction paradigm goes beyond the traditional fixation to Human-Computer Interface (HCI). Within a digitized battlefield, the hardware/software configurations, although envisaged as micro gadgets, have design complexities that challenge new thinking about the whole issues of "interaction" and interactive systems in general. Some important challenges of scientific relevance are:

- adaptive to what?

- adaptive to whom?

- adaptive to what extent?

- adaptive when?

These issues of adaptive interface tools seem to be related to multimodal design concepts. However, a system with intelligent agents, having multiple behaviors, should (and must) consider adaptive interface tools corresponding to the system behaviors. Along this line is the issue of system adaptivity in response to anomalies and uncertainties, necessary characteristics of a digitized battlefield system.

3.0 Cognitive Engineering As A Human Centered Design Tool

The philosophy and rationale of digitized battlefields are gravitated around situation awareness. Situation awareness involves at least three levels of the environment: physical, perceptual and cognitive, respectively.

The physical environment is the source of information. An example of a physical environment is the battlefield terrain and weather data. Auxiliary physical environments may be human-made, such as a Head Mounted Display (HMD) or portable (palm) computer. A perceptual environment is the result of collective perceptual gestalts from human and artificial sensors, such as seeing, hearing, touching, feeling, etc. in the physical environment. Perceptual information arouses attention and helps human beings to elaborate certain latencies important to the environment.

A cognitive environment is the citadel of situation awareness and it operates from a collection of experience repertoire using previous results of lessons learned (Ntuen, Mountjoy, Barnes, & Yarborough, 1986). Between the cognitive and perceptual environments is the dimension known to consist of internalized knowledge. Internalized knowledge is a function of memory attributes: capacity, information storage, format of information storage, etc.

If human-centered design rationales are to be applied to cognitive engineering of the digital battlefield, then it is imperative to recognize these abstract environmental levels in which the human operates. Thus, the cognitive engineering design can be postulated based on these abstractions.

(a) Physical

At this level, engineers and computer scientists must interact very closely with human factors specialists and psychologists in designing the digitization peripherals.

(b) Perceptual Level

At this level, battle information characteristics should be portrayed in a realistic manner (the near realism approach). The nature, form, and level of abstractions of information affect the design of a graphical display. I have been working on a cognitive display as an alternative to a configural display design. The cognitive display combines the perceptual dimensions and information contents of the world and the human system to represent the level of compensatory aid required to minimize human workload in searching for salient data. A possible extension (an idea I am working on with my graduate students) is the concept of a Reciprocal Display Interface (RDI). Under the RDI philosophy, cognitive, associative, and separable displays are combined into a unit metric reciprocal to the system complexity.

(c) Cognitive Level

The above tasks are germane to a commander or a soldier in a battlefield. Context familiarity and recall of information already known about the context belong to information retrieval and recall from the human memory. Situation analysis varies with context and the level of military decision making process. For example, a commander is involved more in modeling courses of action and communication of intents to battlestaffs. To the commander, a decision aiding tool must be able to compensate for, and support, the modeling needs with respect to such issues as:

In battlefield digitization modeling (as is true of most human modeling problems), a general model capable of duplicating the commander's decision making ability in a multifaceted, dynamic, and unstructured environment is not yet available. Therefore, it is important to pay attention to the process rather than the function of modeling: for example, the commander's intent and courses of action for Battle Command Systems. This aspect of cognitive engineering of the commander requires an innovation in the collaborative planning and distributive decision making process. Part of this work is actively being pursued by the Army Research Laboratory.

4.0 Summary

Cognitive engineering of a digitized battlefield deals significantly with building a symbiotic relationship between humans (soldiers) and the battle environment (mission, enemy, terrain, troops, and time available). Contrary to most classical large-scale systems, the digitized battlefield system requires the soldiers to perform on-the-move tasks requiring real-time perceptual control of cognitive actions. Cognitive engineering is suitable for this kind of system because of the ability to abstract the system information into generic levels of human-machine interaction (the so-called abstraction hierarchy space), while recognizing the levels of synergy and symbiosis existing between the system agents, and, for the most part, allowing the representation of the system information for modeling in a manner that captures the interacting dimensions of the system and the associated task abstractions.

Generically, one may approach the design aspect on three levels:

One can further elaborate on the above three design levels by projecting the environment into the meta-levels of human factors. I refer to this as "Human-Centered Factors" in cognitive engineering (see Figure 1). Each of the factors of Figure 1 is shown by the arrows pointing toward the center of the wheel. The wheel contains particular tasks which are normally performed by the human operator.

References to my research agenda

J. Rasmussen(1983). Skills, rules, knowledge: Signals, signs, and symbols and other distinctions in human performance models. IEE Transactions on Systems, Man, and Cybernetics, SMC-13(3), 257-267.

T.P Moran & J. M. Carroll (1996). Design Rationale: Concepts, Techniques, and Use. Mahwah, N.J: Lawrence Erlbaum Associates.

C.M. Mitchell (1986). GT-MSSOC: Research domain for modeling human-computer interaction and aiding decision making in supervisory control systems. Georgia Institute of Tech: Center for Man-Machine Research, Report # 86-1.

B.R. Smith & S. W. Tyler (1997). The design and application of MIDAS: A constructive simulation of human-system analysis. Proc. 2nd Simulation Technology & Training (SIMTECT) Conference, Camberra, Australia.

C. A. Ntuen, D. N. Mountjoy, M.J. Barnes, & L.P Yarbrough (1996). Representation of the commander's heuristic knowledge in a decision support display. Proc. First Annual Symposium. Army Research Lab: Advanced Displays and Interactive Displays Federated Consortium.

BIOGRAPHY

Celestine A. Ntuen is a Professor and Director of the Institute for Human-Machine Studies at North Carolina A&T State University. He started a program in Human-Machine Systems Engineering at North Carolina A&T State University (the first such program in any HBCU/MI). He started (and has since served as the program chair of) the Annual Symposium on Human Interaction with Complex Systems.

Dr. Ntuen received his Ph.D in Industrial Engineering from West Virginia University (with a concentration in Systems Modeling and Simulation). In the past eight years, his research interests have focused on modeling and analysis of human performance interacting with complex automated systems (CAS). He is interested in defining, modeling and designing frameworks for cognitive engineering as a discipline. He has published more than 100 papers in his field of interest.