**Increasing Realism of Human Agents by Modeling Individual Differences:
Methodology, Architecture, and Testbed**

Eva Hudlicka, Greg Zacharias\*, and Joseph Psotka+

Psychometrix Associates, Lincoln, MA; \*Charles River Analytics,
Cambridge, MA;\
+US Army Research Institute, Alexandria, VA

hudlicka@acm.org

Submitted to the AAAI Fall Symposium 00

"Simulating Human Agents"

# Abstract

A key challenge in creating simulated human agents is to produce
sufficiently realistic behavior. A critical component of such realism is
the range of variations in behaviors exhibited by humans. These
variations are due to a variety of factors, including varying levels of
intelligence and skill, differences in cognitive and decision making
styles, personality differences, and different affective states:
collectively termed individual differences. In this paper we describe an
approach for increasing the realism of simulated human agents by
explicitly modeling these individual differences. The core component of
this approach is a generic methodology for modeling individual
differences within symbolic cognitive architectures, via parametric
manipulations of the architectural structures and processes. Individual
differences profiles are first translated into specific architecture
parameters, which then bias the architecture output in corresponding
directions, causing variations in performance. The architecture is
embedded within a modeling and analysis testbed environment, which
supports the simulation of multiple agent interactions within the task
simulation; modeling of a variety of agent types by specifying their
distinct individual differences profiles; and modeling and analysis of
alternative mappings of distinct individual differences onto the
cognitive architecture parameters. Prototype versions of the cognitive
architecture and testbed have been implemented and evaluated in the
context of a military simulation training scenario, representing the
interaction of a number of commanders in the context of a specific
mission. The simulated mission demonstrated distinct differences in
individual commander behavior, as a function of their individual
profile, and consequent significant differences in the final mission
outcome. The testbed environment was effective in facilitating the rapid
selection of alternative individual differences profiles and observation
of resulting individual behavior variations and subsequent mission
outcome. These preliminary results indicate the ability of the modeling
methodology to effectively represent individual differences, and the
ability of the testbed to support flexible exploration in the broad
research area of individual differences modeling.

# 1.0 Introduction

A key challenge in creating simulated agents is to produce sufficiently
realistic behavior. A critical component of such realism is the range of
variations in behaviors exhibited by humans. Whether these be 'leaps of
genius', surprising reactions, specific biases, suboptimal behaviors, or
simply errors, these inconsistencies and idiosyncracies are
quintessential human qualities. These variations are due to a variety of
factors, including varying levels of intelligence and skill, differences
in cognitive and decision making styles, personality differences, and
differences in specific affective states and moods. Collectively, these
factors are termed *individual differences*.

A number of questions must be addressed in representing these factors in
human simulation models. These include the following:

-   How stable are these influences within an individual; across
    individuals; across tasks?

-   Which specific cognitive, perceptual, or motor processes are
    influenced by these factors? What are the exact mechanisms of these
    influences?

-   Which specific individual differences should be represented in a
    particular agent? At what level of resolution should these
    representations exist?

To answer these questions one would ideally turn to psychological
literature for empirical data about the influences of individual
differences on behavior, and to cognitive psychology for details
regarding the mechanisms of these processes. However, the state of the
art in these disciplines does not yet permit this approach; existing
empirical data are sparse and theories of specific cognitive and
perceptual mechanisms often do not provide sufficient level of detail,
or consistency.

This state of affairs therefore necessitates an experimental approach to
modeling individual differences: one that supports rapid definition of
distinct individual difference profiles within a specific agent model,
and a flexible exploration of alternative mappings of the individual
differences factors onto the cognitive architecture structures and
processes, to accommodate rapid integration of newly emerging empirical
data and theories.

In this paper we describe a such an approach and its implementation: the
Methodology for Analysis and Modeling of Individual Differences (MAMID).

> Our approach consists of three components:

• a *generic methodology* for modeling individual differences within
symbolic cognitive architectures, via parametric manipulations of the
architectural structures and processes;

• a *parameterized cognitive architecture*, providing a sufficiently
rich representational and processing environment for modeling the varied
effects of these differences at multiple points of processing; and

• an *exploratory testbed environment* supporting the interaction of
multiple agents within the task simulation; modeling of a variety of
agent types by specifying their distinct individual differences
profiles; and modeling and analysis of alternative mappings of distinct
individual differences onto the cognitive architecture parameters.

Prototype versions of the cognitive architecture and testbed have been
implemented and evaluated in the context of a military simulation
training scenario, representing the interaction of a number of
commanders in the context of a specific mission (Hudlicka & Billingsley,
1999). Each unit commander is represented by an instance of the
cognitive architecture, whose behavior is controlled by the individual
differences parameters representing that commander's individual profile.
The prototype enabled the simulation of a variety of agent types (e.g.,
high / low anxious, high / low aggressive, high / low obsessive, etc.).
The simulated mission demonstrated distinct differences in individual
commander behavior, as a function of their affective / personality
profile, and consequent differences in the final mission outcome.

This paper is organized as follows. We first outline categories of
individual differences factors and provide a brief summary of their
influence on cognition, perception, and behavior (section 2). We then
describe a generic method for modeling the effects of individual
differences within cognitive architectures (section 3), an experimental
parameterized cognitive architecture demonstrating this modeling method
(section 4), and the associated testbed and simulation environment
within which this architecture is embedded (section 5). We briefly
summarize the implementation status (section 6). The paper concludes
with a summary, conclusions, and discussion of future work (section 7).

# 2.0 Effects of Individual Differences Factors on Behavior

The past decade has witnessed a renewed interest in the exploration of
the effects of individual differences on performance (Revelle, 1995).
However, no single psychological or neuroscience theory that provides
*the* definitive, orthogonal set of cognitive, affective, personality,
and individual differences. Rather, each subdiscipline within the
psychological sciences defines its own set of factors, as a function of
a particular theoretical perspective, methodologies, and specific
application of interest. Thus personality theorists in academic
psychology speak of "Big 5" personality traits (extraversion, stability,
openness, agreeableness, and conscientiousness) (Costa & accrue, 1992)
and "Giant 3" (approach, avoidance, fight / flight) personality traits
(Eysenck, 1991), emotion researchers speak of basic emotions[^1] (the
exact number varies but generally joy, sadness, fear, anger, surprise,
and disgust are considered basic). Cognitive psychologists speak of
general cognitive abilities (e.g., attention and working memory
capacity, speed, and accuracy), cognitive biases (e.g., availability,
consistency, confirmation bias, order effects, etc.) (Tversky &
Kahneman, 1981). and specific skills and cognitive styles, clinicians
speak of distinct personality styles (e.g., avoidant,
passive-aggressive, narcissistic, etc.), and political psychologists
consider high-level traits relevant to leadership and interpersonal
decision-making style (e.g., authoritarianism, dogmatism, ease of
persuasion, locus of control, need for power, need for affiliation,
etc.) (Shaw and Post, 1999). Furthermore, these factors are defined at
varying levels of abstraction (e.g., negative emotionality vs.
anxiety-prone vs. suffering from agoraphobia), and varying levels of
detail with respect to their specific mechanisms (e.g., high
intelligence vs. high attentional speed and working memory capacity).

Not all of the identified factors have been studied extensively, and the
available data do not always provide unequivocal results. Nevertheless,
certain regularities emerge from the existing evidence regarding the
influences of some of these factors on perceptual, cognitive,
decision-making, and motor mechanisms mediating behavior (LeDoux, 1989;
Williams et al., 1997; Mineka and Sutton, 1992; MacLeod and Hagan,
1992). For example, fear and anxiety influence attentional focusing
processes and perceptual categorization processes to enhance the
detection of threatening stimuli; increased levels of obsessiveness
correlate with increased checking behaviors and lead to delays in
decision selection (Broadbent et al., 1986); mood influences type of
material recalled from memory (Blaney, 1986); low level of emotional
stability predisposes towards anxiety; etc. Empirical evidence also
suggests that the extent of influence varies with context; that is, by
the individual and the task. The exact manifestations of these factors
also vary within specific individual based on other contextual factors
(e.g., skill level, general cognitive abilities, individual history,
prior experience with the task, current affective state, current
interpersonal environment, etc.). Given the complexity of the
phenomenon, how then do we select the specific factors to include in a
particular model / agent simulation?

We have identified a number of criteria for this selection process: 1)
availability of empirical evidence demonstrating the existence of the
individual differences factor and an associated performance effect; 2)
sufficient effect size to warrant the inclusion of the factor within the
model; 3) sufficient robustness of existing empirical findings to allow
generalizability to the context of interest (e.g., military simulation,
medical decision making, gaming); 4) relevance of particular factor
within the context of interest (i.e., does the influence of the
particular factor emerge within the task of interest or does the task
environment provide performance constraints which limit the
manifestations of individual differences); and 5) theoretical and
empirical basis for mapping the factor's influence onto a cognitive
architecture. Again, given the lack of definitive empirical data, a
critical component of the final selection process is an extensive
cognitive task analysis and knowledge elicitation with a subject matter
expert.

For the initial prototype implementation, we divided the individual
differences factors into three broad categories: cognitive, affective,
and personality. Several factors from each category were then selected
to provide adequate complexity capable of validating the modeling
approach and the cognitive architecture, evaluating the testbed
functionality, and providing interesting and meaningful variations in
performance. These factors are briefly described below.

*Cognitive*

-   Skill level was selected because it determines the knowledge
    structure and contents mediating task performance (e.g., perceptual
    categories for situation assessment, situation to action mapping,
    action repertoire).

-   General intelligence was selected because defines baseline limiting
    capabilities of the fundamental processes within the perceptual --
    cognitive apparatus (e.g., attention and working memory accuracy and
    capacity).

-   Individual history factors were selected as critical components in
    defining specific affective connotations for particular situations
    (e.g., success or failure associated with a specific recent
    situation influences level of anxiety in a similar situation, etc.).

*Affective*

-   Anxiety was selected as the primary affective factor because of its
    relevance to the task (military simulation), and the extensive
    empirical data documenting consistent influence on attention and
    perception (e.g., narrowing of attention, biasing towards threats).

*Personality*

-   Emotional stability was selected as a factor influencing anxiety
    manifestation and rate of decay.

-   Aggressiveness was selected as a critical personality factor
    relevant to the simulation task domain.

-   Obsessiveness, a state resulting from a combination of high anxiety
    and high conscientiousness, was selected as an important factor
    influencing speed and accuracy of decision-making.

# Methodology for Modeling Individual Differences in Agent Architectures

The core component of our approach is a *generic methodology* for
modeling individual differences within symbolic cognitive architectures,
via parametric manipulations of the architectural processes and
structures. A key objective of the methodology is to provide flexibility
regarding the *types of factors* selected for inclusion in a model, the
*nature of their influence* on cognitive, perceptual, and
decision-making processing, and the *degree of this influence*. Such
flexibility then supports an experimental approach to model development,
by providing the following features: 1) flexible selection of parameter
combinations to represent in the model (i.e., distinct individual
profiles); 2) rapid incorporation of emerging empirical data; 3) varying
the level of representational abstraction of a particular factor,
depending on the needs of the particular application; 4) modification of
the specific effects of a particular factor depending on the theoretical
perspective represented and knowledge of the underlying mechanisms.

The modeling approach achieves this flexibility through a
parameterization of individual cognitive architecture processes and
structures (Hudlicka, 1997; Pew & Mavor,, 1998). The distinct individual
differences factors are then mapped onto these cognitive architecture
parameters. Figure 3-1 illustrates this approach. The effects of
individual differences factors can be modeled in two primary ways: 1) by
manipulating the model *processing* parameters (e.g., attention capacity
& accuracy), to reflect a particular cognitive and decision making
style, or a particular affective personality factor (e.g.,
susceptibility to anxiety), thereby biasing the model behavior in a
particular direction (e.g., detection of threatening cues in the
environment); and 2) by manipulating the model *content* (e.g.,
structure and contents of rules, semantic nets, belief nets, etc.) to
reflect the desired characteristics (e.g., varying levels of skill and
training, stable perceptual biases, decision making preferences,
different individual histories, etc.). Table 3-1 illustrates an example
of the individual differences-to-cognitive architecture mapping
specification (equation calculating the level of anxiety from
contributing factors).

**Table 3-1: Equation for Calculating the Level of Anxiety from the**

**Contributing Individual Differences Factors**

######### ![](media/image1.wmf){width="3.91875in" height="2.807638888888889in"}

######### Figure 3-1: Summary of Proposed Methodology for Representing

#### Individual Differences in Human Performance Models

# 4.0 Parameterized Experimental Cognitive Architecture 

The *parameterized cognitive architecture* implements the modeling
methodology described above, by providing a rich representational and
processing environment for modeling the varied effects of the individual
differences factors. Several objectives motivated the design of the
MAMID experimental cognitive architecture: 1) consistency with existing
theories and empirical data; 2) ability to generate the desired task
performance; 3) ability to encode the influence of the selected
individual differences factors via parameterized processing, as required
to implement the modeling methodology discussed in section 3 above; 4)
ability to explicitly model distinct stages of processing, to enable the
encoding of parallel effects of a particular factor on distinct
components of the perceptual -- cognitive apparatus (e.g., anxiety
effects on attention AND perception, etc.).

These considerations led to the design of a symbolic, integrated
cognitive architecture consisting of five modules: *sensory
pre-processing*, providing the external cues representing the current
state of the world; *attention*, filtering the incoming cues and
selecting a subset for further processing; *situation assessment*,
mediating information integration by combining individual cues into an
overall situation assessment; *decision selection*, selecting an
appropriate action based on the current situation assessment; and
*procedure execution / monitor*, closing the loop by integrating
feedback from actions from the environment (see figure 4-1). The
individual modules are assumed to have access to the memory structures
containing the domain and problem-solving knowledge (long term memory
(LTM) and working memory (WM)), and consisting of rules (decision
selection module) and belief nets (situation assessment module). The
architecture modules thus use multiple representational formalisms to
implement a variety of inferencing types.

![](media/image2.wmf){width="3.176388888888889in" height="2.43125in"}

**Figure 4-1: MAMID Cognitive Architecture and Associated testbed
Environment**

Processing within the MAMID architecture is determined in part by the
various parameters controlling their individual module processing, and
in part by the structure and contents of the memory structures (e.g.,
nature and content of rule-bases, topology of belief nets).

The architecture and module processing details are described in detail
elsewhere (Hudlicka and Billingsley, 1999). Here we briefly describe one
of the architecture modules, Situation Assessment, to illustrate the
general approach. The situation assessment module is implemented in
terms of belief nets (Pearl, 1986), which map the incoming cues,
selected by the attention module, onto high-level situations, using the
belief net probabilistic inferencing engine. Incoming cues, representing
low-level features and events (e.g., enemy unit approaching), are
combined into intermediate results (e.g., enemy unit within weapons
range), and finally derive the overall situation types (e.g., unit under
attack). A number of factors influence the situation assessment process,
including the current situation, expectations, and goals, current
affective state, variety of cognitive individual differences factors
(general, stylistic, and specific skill), and personality traits. The
specific skill and individual history factors determine the overall
network contents, topology, and conditional probabilities linking the
individual nodes. The general cognitive capacity factors determine the
processing speed and accuracy. Working memory capacity is reflected in
the number of incoming cues that can be processed by the network.
Anxiety is represented by a distinct node within the belief net, and
linked to all nodes representing propositions (i.e., specific
perceptions) influenced by anxiety. This explicit representation thus
allows the representation of the biasing effects of anxiety on
perceptual categorization; that is, the increased likelihood of assuming
the existence of a more threatening situation. Figure 4-2 shows an
example of a belief net deriving the probability of attack.

![](media/image3.png){width="4.7444444444444445in"
height="2.7194444444444446in"}

**Figure 4-2: Example of a Situation Assessment Belief Net, Showing an
Explicit**

**Representation of the Anxiety Level**

Distinct agent types are modeled by defining an *individual profile*,
consisting of specific values for the individual differences factors
(e.g., an anxiety-prone, intelligent, but novice commander would have
high general cognitive abilities, low skill level, low emotional
stability, and high anxiety; a obsessiveness-prone, highly skilled
commander would have high skill level, high conscientiousness level,
high anxiety level, etc.). These profiles are then translated into
specific cognitive architecture parameter values and structural memory
(knowledge base) modifications, which determine the exact model
behavior. This cognitive architecture is loosely based on the Charles
River Analytics SAMPLE architecture (Zacharias et al., 1996), which was
augmented with additional structures and processes, as necessary, to
enable the modeling of the selected individual affective and
personality.

# 5.0 Modeling and Analysis Testbed Environment

The MAMID *testbed* provides an experimental environment supporting the
modeling of a variety of agent types in terms of the architecture
described above, and their interaction within the task simulation
environment. Specifically, the MAMID testbed supports the definition of
specific task scripts, and individual commander profiles in terms of
distinct individual differences factors, and the simulation of the
resulting commander behavior, to demonstrate both the modeling
methodology and the effects of the individual commander's behaviors on
the overall mission outcome. A testbed approach is necessitated in part
by the fact that existing empirical evidence does not always support the
definitive determination of the factors influencing particular task
performance, and in part because the state-of-the-art in empirical
psychological literature, cognitive science, and AI, does not yet allow
the specification of *the* definitive set of mappings among individual
differences factors and cognitive architecture parameters.

![](media/image4.png){width="4.79375in" height="3.5972222222222223in"}

**Figure 5-1: MAMID Testbed GUI Showing Simulation Output (left) and**

**a Variety of Monitoring Displays (right)**

The MAMID testbed consists of the following components (refer to figure
4-1): *the Analyst Interaction Management* module, supporting scenario
selection and definition, specification of the simulated commander's
individual differences profiles used during a simulation run,
specification of MAMID run-time parameters to control system performance
monitoring, facilitating the user / analyst control of the simulation,
and displaying an animated version of the scenario (i.e., a military
simulation situation sketch), where the visible effects of distinct
agent profiles can be observed as the simulated mission unfolds (e.g.,
aggressive commander, anxious commander, etc.); a *Cognitive
Architecture Parameter Calculation* module, mapping the individual
differences factors onto the cognitive architecture parameters
controlling processing; the *parameterized Cognitive Architecture*,
implementing the distinct modules discussed above; and a *Task Scenario
Simulation module*, implementing the simulation functionality required
for the demonstration scenario.

Figure 5-1 illustrates a specific configuration of the MAMID testbed
Analyst GUI, showing the simulation output (map with overlaid military
unit symbols representing a particular mission situation) and a variety
of system monitoring windows and individual profile specification
displays.

# 6.0 MAMID Implementation and Status

To demonstrate and evaluate the MAMID modeling methodology, we
implemented the cognitive architecture and testbed described above, and
evaluated its performance in the context of a simulated military
engagement. The behavior of each unit commander was modeled by an
instantiation of the MAMID cognitive architecture, whose exact behavior
was determined by the individual differences profile associated with
that commander. The different individual profiles specified distinct
commander personalities and backgrounds, resulting in differences in
information processing and observable behavior. A variety of distinct
profiles were specified (e.g., high / low anxious, high / low
aggressive, high / low obsessive, etc.), resulting in observable
differences in the overall mission development and outcome (see Hudlicka
and Billingsley (1999) for a more detailed discussion).

# 7.0 Summary, Conclusions, and Future Work

**Summary** We described a research approach aimed at producing more
realistic behaviors in simulated human agents. The approach consists of
three components: a generic methodology for modeling the effects of a
variety of individual differences factors on performance, by
manipulating the processing and structural parameters of a symbolic
cognitive architecture; a parameterized cognitive architecture, which
controls the agent behavior; and an experimental testbed. The high
degree of architecture parameterization allows the flexible exploration
of the effects of a variety of distinct individual differences profiles
on performance, thereby enabling the modeling of a variety of agent
types, and their interaction, within the testbed environment. A
prototype cognitive architecture was implemented within an integrated
simulation and analysis testbed environment (Windows/ NT; Java; JESS,
JavaBayes). The initial domain was a military simulation training
scenario, representing the interaction of a number of commanders in the
context of a specific force-on-force mission. Each commander's behavior
was controlled by an instance of the cognitive architecture, whose
behavior in turn was determined by the individual differences profile
representing the commander's cognitive, affective, and personality
individual differences factors.

**Conclusions** The simulated mission demonstrated distinct differences
in individual commander behavior, as a function of their individual
profile, and consequent significant differences in the final mission
outcome. The testbed environment was effective in facilitating the rapid
selection of alternative individual differences profiles and observation
of resulting individual behavior variations and subsequent mission
outcome. These preliminary results indicate the ability of the modeling
methodology to effectively represent individual differences, and the
ability of the testbed to support flexible exploration in the broad
research area of individual differences modeling. Together, they
indicate a general feasibility of the modeling approach. Although the
initial prototype was developed within the military domain, the modeling
methodology and architecture are applicable across a broad variety of
non-military application areas, as outlined below. In addition to
enabling the modeling of more realistic behavior in simulated human
agents, the methodology and testbed environment also provide a research
tool for investigating broader issues in computational cognitive
modeling, by supporting the rapid testing of theoretical hypotheses
regarding the exact mechanisms of influence of a variety of individual
differences factors on perceptual and cognitive processing.

**Future Work** Future work falls into several categories of effort: 1)
Expansion of the set of individual differences factors modeled (e.g.,
broader set of cognitive styles, emotions, and personality traits); 2)
Enhancements of the cognitive architecture to include learning and more
complex planning, as well as modifications in the overall structure of
the architecture (e.g., non-linear, opportunistic processing); 3)
Enhancements of the testbed functionalities to support more extensive
architecture parameter modifications; 4) Application of the approach in
different domains (e.g., virtual reality training and treatment
environments in mental health; team training environments; and
interactive education and gaming environments).

## 

## Acknowledgements

The research described in this paper was supported in part by US Army
Contract DASW01-98-M-1985. We would like to acknowledge the
contributions of the following individuals: John Billingsley and John
Broglio implemented the MAMID prototype, Prof. William Revelle of
Northwestern University made helpful suggestions regarding the
individual differences factor selection, and Mr. Ted Fichtl of The
Compass Foundation was the primary subject matter expert.

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[^1]: The notion of basic emotions remains somewhat controversial,with a
    number of researchers disputing the idea that a small set of basic
    emotions exists.