Modeling Human and Organizational Behavior Application to Military Simulations (1998) / Chapter Skim
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3 Integrative Architectures for Modeling the Individual Combatant
3 Integrative Architectures for Modeling the Individual Combatant
Pages 51-111
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From page 51... ...
Although such quantitative integrative models have rarely been sought by psychologists in the past, there is now a growing body of relevant literature. In the few cases in which these integrative models have been applied to military simulations, they have focused on specific task domains.
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In particular, most of the models examined are specific instantiations of a modified stage model of human information processing. The modified stage model is based on the classic stage model of human information processing (e.g., Broadbent, 1958~.
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It would be possible to bring a set of specific submodels of human behavior together in an ad hoc manner, with little thought to how they interact and the emergent properties that result. But such a model would not be an integrative architecture.
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MIDAS redesign Neural networks Operator model architecture (OMAR) Situation awareness model for pilot-in-the-loop evaluation (SAMPLE)
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Procedural knowledge is represented in production rules. ACT-A's pattern-matching facility allows partial matches between the conditions of productions and chunks in declarative memory (Anderson et al., 1996~.
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The base-level activation of a declarative knowledge element can also be learned automatically. Associative learning can automatically adjust the strength of association between declarative memory elements.
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If learning is turned on, additional outputs include final parameter settings, new declarative memory elements, and new productions; what the model learns is highly inspectable. Current Implementation The currently supported versions of ACT-R are ACT-R 3.0 and ACT-R 4.0.
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The latter capacity is provided by the ACT-it-on-the-Web server, which can run any number of independent ACT-R models in parallel, allowing even beginners to run ACT-R models over the Web without downloading or installing ACT-R. Validation ACT-R has been evaluated extensively as a cognitive architecture against human behavior and learning in a wide variety of tasks.
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, but the work is too preliminary at this time to be reported in the archival literature. Although ACT-A's applicability to military simulations is as yet underdeveloped, ACT-R is highly applicable to military training in cognitive skill.
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Four types of COGNET operators support the information processing and control behaviors of COGNET models. System environment operators are used to activate a workstation function, select an object in the environment (e.g., on a display)
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Using this method, the attention focus manager starts, interrupts, and resumes tasks on the basis of priorities. As mentioned above, COGNET assumes serial behavior with rapid attention switching, so only the highest-priority task runs at any time.
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Validation A specific COGNET model in an antisubmarine warfare simulation received limited validation against both the simulated information processing problems used to develop it and four other problems created to test it. The investigators collected data on task instance predictions (that is, COGNET predictions that a real human operator would perform a certain task)
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From page 63... ...
Its perceptual models appear to be limited, and it is severely constrained in its ability to model motor behavior, though these limitations may not be significant for many applications. Nevertheless, given its ability to provide a good framework for representing cognitive, multitasking behavior, COGNET merits further scrutiny as a potential tool for representing human behavior in military simulations.
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At this time, EPIC does not completely specify the properties of working memory because clarification of the types of working memory systems used in multiple-task performance is one of the project's research goals. Currently, working memory is assumed to contain all the temporary information tested and manipulated by the cognitive processor's production rules, including task goals, sequencing information, and representations of sensory inputs (Kieras et al., 1998~.
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From page 65... ...
In each cognitive processor cycle, any number of rules can fire and execute their actions; this parallelism is a fundamental feature of the parsimonious production system. Thus, in contrast with some other information processing architectures, the EPIC cognitive processor is not constrained to do only one thing at a time.
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An interface between EPIC's peripherals and Soar's cognitive processor is available on the UNIX platforms. Support Environment EPIC is a relatively new architecture that has not yet been the object of the development time and effort devoted to other architectures, such as ACT-R, Soar, and Micro Saint.
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Assumptions HOS is based on several assumptions that have implications for its validity and its applicability to military simulations. First, HOS assumes that the human has a single channel of attention and time-shares tasks serially through rapid attention switching.
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Those components most closely related to the human operator are described below. The environment of the HOS operator is object-oriented and consists of simulation objects.
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The default micro-models in this category include perception of visual scene features, reading of text, nonattentive visual perception of scene features, listening to speech, and nonattentive listening. Micro-models for cognitive processes determine the time required to calculate, based on the type of calculation to be made, or to decide, based on the information content of the decision problem (e.g., the number of options from which to choose)
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Applicability for Military Simulations HOS has a simple, integrative, flexible architecture that would be useful for its adaptation to a wide variety of military simulation applications. HOS also contains a reasonable set of micro-models for many human-machine system applications, such as modeling a tank commander or a pilot.
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From page 71... ...
Micro Saint is not so much a model of human behavior as a simulation language and a collection of simulation tools that can be used to create human behavior models to meet user needs. Yet many Micro Saint models have been developed for military simulations (e.g., Fineberg et al., 1996; LaVine et al., 1993, 1996)
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Both human operators and the systems with which they interact are modeled by task networks. We focus here on human task networks, with the understanding that system task network concepts form a logical subset.
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The integrated performance package runs on UNIX platforms. Support Environment rl~he Micro Saint environment includes editors for constructing task networks, developing task descriptions, and defining task branching decision logic; an expandable function library; data collection and display modules; and an animation viewer used to visualize simulated behavior.
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Knowledge representation is rudimentary, and, other than a basic branching capability, there is no built-in inferencing mechanism with which to develop detailed models of complex human cognitive processes; such features must be built from scratch. Nevertheless, Micro Saint has already shown merit through at least limited validation and accreditation and has further potential as a good tool for building models of human behavior in constructive simulations.
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Assumptions MIDAS assumes that the human operator can perform multiple, concurrent tasks, subject to available perceptual, cognitive, and motor resources. Architecture and Functionality The overall architecture of MIDAS comprises a user interface, an anthropometric model of the human operator, symbolic operator models, and a world model.
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During the simulation, the updatable world representation is constantly updated by the visual perception agent; it can deviate from ground truth because of limitations in perception and attention. Knowledge representation in the updatable world representation is in the form of a semantic net.
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Operation In a MIDAS simulation, declarative and procedural information about the mission and equipment is held in the updatable world representation. Information from the external world is filtered by perception, and the updatable world representation is updated.
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Support Environment The MIDAS support environment has editors and browsers for creating and changing system and equipment specifications, and operator procedures and tools for viewing and analyzing simulation results. Currently much specialized knowledge is required to use these tools to create models, but it is worth noting that a major thrust of the MIDAS redesign is to develop a more self-evident GUI that will allow nonprogrammers and users other than the MIDAS development staff to create new simulation experiments using MIDAS.
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The approach has been used to model a wide range of cognitive processes and is in widespread use in cognitive science. Yet neural network modeling appears to be quite different from the other architectures reviewed in this section in that it is more of a computational approach than an integrative human behavior architecture.
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Assumptions The two major assumptions behind neural networks for human behavior modeling are that human behavior in general can be well represented by selforganizing networks of very primitive neuronal units and that all complex human behaviors of interest can be learned by neural networks through appropriate training. Through extensive study and use of these systems, neural nets have come to be better understood as a form of statistical inference (Mackey, 1997; White, 1989)
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. Neural networks are also parallel processing systems in the sense that activation spreads and flows through all of the nodes simultaneously over time.
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. Support Environment There are numerous excellent textbooks that can help in designing and programming neural networks (see, e.g., Anderson, 1997; Golden, 1996; Haykin, 1994; Levin, 1991; McClelland and Rumelhart, 1988~.
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We know of no work that models human behavior at the level of reasoning about military strategy and tactics or performance of the tasks to carry out those decisions. Researchers differ in their opinions as to whether it is possible for neural networks to support a general cognitive architecture with high-level reasoning skills involving structured information: see Sharkey and Sharkey (1995)
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Operator Model Architecture The operator model architecture (OMAR) models human operators in complex systems, such as command and control systems, aircraft, and air traffic control systems (Deutsch et al., 1993, 1997; Deutsch and Adams, 1995; MacMillan et al., 1997~.
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At the cognitive level, OMAR consists of agents, which are entities capable of executing goals, plans, and tasks. Generally, a human operator model consists of a single agent, but operator model components (e.g., perception)
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However, OMAR has been used in mixed human-in-the-loop and model simulations, and the OMAR models seem to interact well with the real human operators. Applicability for Military Simulations As noted above, OMAR is an integrative architecture that is well grounded in psychological theory.
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Architecture and Functionality The SAMPLE architecture consists of a system model and one or more human operator models. The system model includes system dynamics (e.g., ownship, the plant, or a target)
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(See Chapter 7 for a more detailed discussion of this material.) Procedures form the core of the SAMPLE human operator model.
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Events in the system or environment are detected by the discrete event detector, and discrete procedures are enabled by event detections. Enabled procedures compete for the operator' s attention on the basis of expected gain, and the individual operator can execute only one procedure at a time.
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From page 90... ...
Applicability for Military Simulations The SAMPLE architecture provides a general framework for constructing models of operators of complex systems, particularly in cases in which the operators are engaged in information processing and control tasks. SAMPLE draws heavily on modern control theory, which has enjoyed considerable success in the modeling of human control behavior.
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From page 91... ...
Although any one Soar program may emphasize the goals of only one of these contingents, both sets of goals could in principle be served by a single Soar model. Assumptions Soar assumes that human behavior can be modeled as the operation of a cognitive processor and a set of perceptual and motor processors, all acting in parallel.
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(Note that the desired states do not have to be completely specified; that is, open-ended problems can be represented by desired states that simply contain more knowledge than the initial state, without the particular type or content of that knowledge being specified.) When sufficient knowledge is available in the problem space for a single operator to be selected and applied to the current state, the behavior of a Soar model is strongly directed and smooth, as in skilled human behavior.
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In all models, initial knowledge is encoded by the human modeler in production rules. In many models, the initial state of the model is coded manually, but other models start with no knowledge of the problem state and must acquire that knowledge from the external environment.
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html> . Validation Soar has been evaluated extensively as a cognitive architecture against human behavior in a wide variety of tasks.
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In addition, other work that compares Soar models with human behavior include models of covert visual search (Weismeyer, 1992) , abductive reasoning (Krems and Johnson, 1995)
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COMPARISON OF ARCHITECTURES Table 3.1 presents a summary of the integrative architectures reviewed in this chapter, comparing them across several dimensions that should help the reader assess their relative potential in meeting the needs of intended applications. The following sections describe each of these dimensions in turn, attempting to point out features that distinguish some architectures from others.
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Working/Short-Term Memory As discussed in Chapter 5, working or short-term memory plays a major role in human information processing. Applications in which attention and decision making are important considerations are likely to benefit from veridical representations of working memory.
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98 TABLE 3.1 Integrative Architectures MODELING HUMAN AND ORGANIZATIONAL BEHAVIOR Submodels Working/ Architecture Original purpose Sensing and Perception Short-Term ~ ACT-R Model problem solving Perceptual processors: Activation-ba and learning visual, auditory, tactile long-term me COGNET Develop user models in Abstract perceptual Extended won intelligent interfaces, daemons, with provision memory throw surrogate users and of user-defined models multipanel hi adversaries EPIC Develop and test theories Perceptual processors: Unlimited cad of multiple task visual, auditory, tactile duration performance HOS Generate timelines for Visual and auditory Micro-model HMS analysis and sensing and perception limited capac evaluation micro-models and decay Micro Saint based Evaluate systems and Detection/identification Not explicitly network tools procedures probabilities, times, and accuracies MIDAS Evaluate interfaces and Visual perception agent Not explicitly procedures MIDAS Redesign Evaluate interfaces and Visual and auditory Subset of not procedures perception term memory capacity and Neural network Multiple constraint, Visual and auditory Activation-ba based tools satisfaction in memory, perception capacity language, thought, pattern recognition OMAR Evaluate procedures and Default perceptor models Emergent pro interfaces explicitly mo SAMPLE Evaluate crew procedures, Visual, auditory channels Not explicitly equipment (optimal control model based) Soar Model problem solving Perceptual processors: Unlimited cad and learning visual, auditory duration tied
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MODELING THE INDIVIDUAL COMBATANT 99 Working/ 'erception Short-Term Memory Long-Term Memory Motor Outputs cessors: Activation-based part of Network of schema-like Motor processors: Behaviors y, tactile long-term memory structures plus manual, vocal, productions oculomotor ceptual Extended working Multipanel blackboard Abstract, with Behaviors ~ provision memory through provision for d models multipanel blackboard user-defined models cessors: Unlimited capacity and Propositions and Motor processors: Behaviors y, tactile duration productions manual, vocal, oculomotor ditory Micro-model with Not explicitly modeled Eye, hand, trunk, Metrics rception limited capacity foot, etc., and decay micro-models ratification Not explicitly modeled Not explicitly modeled Times and accuracies Metrics times, and plus micro-models Lion agent Not explicitly modeled Semantic net, updatable Jack-animated Behaviors world representation mannequin ditory Subset of nodes in long- Frames Jack-animated Behaviors term memory, limited mannequin capacity and decay ditory Activation-based limited Connection weights Sensor/motor Behaviors capacity integration, occularmotor tutor models Emergent property not Not explicitly modeled Default effecter Behaviors explicitly modeled network of frames models ry channels Not explicitly modeled Procedural knowledge Time-delayed procedural Behaviors ol model- base actions cessors: Unlimited capacity, Productions Motor processors: Behaviors y duration tied to goal slack manual, vocal, oculo motor continued on pages 100-105
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(built on Common Lisp) SAMPLE Objects Production rules No learning Soar Productions Productions Learning by ~ (flexible set
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MODELING THE INDIVIDUAL COMBATANT 10 Higher-Level CognitiveFunctions Situation Learning Planning Decision Making Assessment Weight adjustment, production strength adjustments, new productions, new schemes Creates new plans Knowledge-based, Bayesian Overt and inferred ~es, No learning Instantiates general Knowledge-based Overt and inferred plans luggage language, mans, ules, ferns age mon Les No learning No learning No learning No learning No learning Weight updating by gradient ascent on objective function No learning No learning Learning by chunking (flexible see text) Instantiates general plans No planning No planning Instantiates general plans Instantiates general plans Motor planning Instantiates general plans Instantiates general plans Creates new plans Knowledge-based None Knowledge-based Knowledge-based, Bayesian Knowledge-based, Bayesian Competitive systems of activation Knowledge-based, Bayesian Knowledge-based, Bayesian Knowledge-based Overt and inferred Overt Overt Overt Overt Primarily inferred Overt Overt Overt and inferred continued
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From page 102... ...
activation COGNET Serial with switching and Limited focus on attention, Pandemor interruptions parallel motor/perceptual based on processors EPIC Parallel Limited perceptual and motor Priority-b processors, unlimited cognitive processor HOS Serial with switching, plus Speech, hand, foot, and Priority-b parallel movement possible cognitive channels switching Micro Saint based Parallel with switching to Visual, auditory, cognitive, Simple dy network tools serial resources limited psychomotor workload MIDAS Resource-limited parallel Visual, auditory, cognitive, Z-Schedu~ motor resources constraint MIDAS Redesign Resource-limited parallel Visual, auditory, cognitive, Agenda ~ motor resources resource c priorities Neural network Contention scheduling Units and connections Competiti based tools via competition among allocated to a task activation components OMAR Serial with some parallelism Perceptor, cognitive, effecter Tasks con for automatic tasks resources SAMPLE Serial with interruptions Sensory, cognition, and action Priority b, channels "situation Soar Serial with switching and Serial cognitive processor, Preference interruptions limited perceptual and motor allocation resources
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MODELING THE INDIVIDUAL COMBATANT 103 Implementation tion Goal/Task Management Multiple Human Modeling Platform e memory None (one goal at a time) Potential through multiple Mac, PC (limited)
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From page 104... ...
models) Micro Saint based C, C++ Editors, debuggers Some mic network tools least one implemen MIDAS Lisp, C, C++ Graphical editors, Full mode graphical data displays MIDAS Redesign C++ Similar to original None MIDAS Neural network C, NETLAB Commercial products, Extensive based tools GUIS componen OMAR Lisp Compilers, editors, Component browsers, online animation tools, post run analysis tools SAMPLE C++ Editors Control ta Soar C Editors, debuggers Extensive
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From page 105... ...
Extensive at multiple levels ACT-R models focus on single, specific information processing tasks; has not yet been scaled up to complex multitasking situations or high-knowledge domains Used in low-fidelity submarine training simulations and high-fidelity AEGIS CIC training simulations to provide surrogate operators and adversaries EPIC models focus on simple, dual-task situations; has not yet been scaled up to complex multitasking situations or high-knowledge domains Currently capable of scripted behaviors only Used extensively in military simulations Currently capable of scripted behaviors only In development Focus on sensory/motor integration, have not yet been scaled up to complex multitasking situations or high-knowledge domains Has been used in small-scale military simulations Has been used in military simulations (e.g., synthetic theater of war-Europe [STOW-E]
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From page 106... ...
Knowledge Representation Declarative Representation of declarative knowledge ranges from simple variables to complex frames and schemes. Those applications in which complex factual knowledge structures must be represented explicitly would be better served by the more sophisticated techniques of such architectures as ACT-R and OMAR.
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From page 107... ...
Some form of decision-making capability seems essential for most military simulations, and architectures such as ACT-R, OMAR, and Soar seem most capable in this regard. Multitasking All the architectures permit multitasking in some sense, though ACT-R enforces strict serial execution of tasks with no interruptions allowed.
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From page 108... ...
Unfortunately, most of the validation of "full models" has been based on subjective assessments of subject matter experts, not real human performance data. This last consideration dictates considerable caution on the part of users.
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From page 109... ...
Another research and development path might prove more fruitful: combine the strengths of two or more architectures to produce a hybrid that better encompasses human phenomena. A simple example, the combination of Soar's cognitive processor with EPIC's perceptual and motor processors, has already been mentioned.
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From page 110... ...
Through extensive use of the reviewed architectures, researchers and developers in the field now know enough about their relative strengths and weaknesses to judiciously explore plausible combinations for achieving the greatest impact of human behavior models. CONCLUSIONS AND GOALS None of the architectures described in this chapter is precisely what is needed for military simulations.
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From page 111... ...
Although it is unlikely that such architectures would be entirely satisfactory, they would give modelers more experience in developing and validating human behavior representations. Intermediate-Term Goals · Continue validation into the intermediate term as more sophisticated integrative architectures are developed.
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