COMPUTER ANIMATION AND VIRTUAL WORLDS
Comp. Anim. Virtual Worlds (2009)
Published online in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/cav.316
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Development of a computational cognitive
architecture for intelligent virtual character
By Pak-San Liew*, Ching-Ling Chin and Zhiyong Huang
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A development of a computational cognitive architecture for the simulation of intelligent
virtual characters is described in this paper. By specializing and adapting from an existing
structure for a situated design agent, we propose three process models—reflexive, reactive
and reflective—which derive behavioural models that underlie intelligent behaviours for
these characters. Various combinations of these process models allow intelligent virtual
characters to reason in a reflexive, reactive and/or reflective manner according to the
retrieval, modification and reconstruction of their memory contents. This paper offers an
infrastructure for combining simple reasoning models, found in crowd simulations, and
highly deliberative processing models or reasoning, found in ‘heavy’ agents with high-level
cognitive abilities. Intelligent virtual characters simulated via this adapted architecture can
exhibit system level intelligence across a broad range of relevant tasks. To demonstrate the
usefulness of the proposed architecture, we describe the effect of the reflexive, reactive and
reflective processes on a virtual character in our virtual tour application. Copyright # 2009
John Wiley & Sons, Ltd.
Received: 25 March 2009; Accepted: 26 March 2009
KEY WORDS: computational cognitive architecture; virtual exploration; intelligent virtual
character; simulation
Introduction
Simulating a believable character in virtual world is one
of the major research areas in computer graphics and
artificial intelligence. There are many applications such
as computer games and virtual reality systems, where a
believable character can greatly enhance the immersive
experience of a user. The current use of artificial
intelligence technology to produce intelligent virtual
characters has yielded interesting results. However,
most of the existing work produced characters that
exhibit a narrow spectrum of intelligence. This has led
researchers to consider a new, holistic and humaninspired approach to inject intelligence into virtual
characters.
In this work, we develop an infrastructure for
intelligent virtual characters to perform a potentially
broad range of tasks—a highly desirable attribute for
*Correspondence to: P. S. Liew, Institute for Infocomm
Research, 1 Fusionopolis Way, 21-01 Connexis, South Tower,
Singapore 138632, Singapore. E-mail: [email protected]
simulating believability in virtual characters within
interactive digital media. Based on an architecture
described by Liew and Gero,1,2 we propose three process
models—reflexive, reactive and reflective—which
derive behavioural models that underlie intelligent
behaviours for these characters. Quick stimulus
responses are considered as ‘reflexive’; stimulusresponse behaviours with slight deliberation are considered as‘reactive’ while highly deliberative responses
are considered as ‘reflective’. Simulating reasonings in
the reflexive, reactive and reflective manners are made
possible by combinations of these process models,
according to the retrieval, modification and reconstruction of their memory contents. Our adapted architecture
provides the capability of combining simple reasoning
models, found in crowd simulations, with highly
deliberative processing models, found in ‘heavy’ agents
with high-level cognitive abilities, within a unified
framework, hence extending the range of intelligent
behaviours that can be exhibited in virtual characters.
An initial implementation of a virtual tour application—
TOWN, based on a behavioural model derived from
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Copyright # 2009 John Wiley & Sons, Ltd.
P. S. LIEW, C. L. CHIN AND Z. HUANG
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this architecture is demonstrated. This is the first step
towards representing and processing an agent’s knowledge and experience to simulate the required humanlevel intelligence for believable virtual characters.
Related Work
This research approaches the development of intelligent
systems by using humans as the best example of humanlevel intelligence through the process of computational
cognitive modelling. Computational cognitive modelling looks at the essence of cognition and various
cognitive functionalities by specifying computational
cognitive models.3 Research in cognitive modelling may
revolve around modelling human cognition to be tested
empirically with human subjects, or it may use the result
as an effective mechanism to build intelligent systems.4
The building of intelligent systems is the focus of this
paper.
A computation cognitive model captures internal
computational processes that generate overt cognitive
behaviour.5 Cognitive models can be created with or
without a cognitive architecture. A cognitive architecture specifies the underlying infrastructure for an
intelligent virtual character that is constant over time
and across different application domains.4 The importance of a cognitive architecture arises from its provision
of a comprehensive initial framework for further
development of cognitive models in many task domains.
As a result of initial assumptions made in designing the
architecture, further development of models is constrained by the pre-determined modular structure
within unified framework to produce general intelligence.6
There are research works that develop intelligent
systems independent of a cognitive architecture.7–11
However, this paper takes the position of the development of an architecture of intelligent behaviour through
a system-centric approach. This architecture will have
the potential to unify various findings in artificial
intelligence, such as production rules in expert systems,
and cognitive science, such as information processing
models of the human mind, into a single framework. We
aim to fulfil the increasing requirements of integrated
systems that wish to support the required behaviour
across a broad range of relevant tasks.4 An existing
structure for a situated design agent1,2 is specialized and
its related key concepts are adapted for simulating
believable virtual characters.
Development of a
Computational Cognitive
Architecture
The approach taken in this paper, to develop the
required computational cognitive architecture for an
intelligent virtual character, is based on creating those
aspects of the character that are constant over time and
across different application domains.4 These include
a memory system that contains knowledge and
experience processed by the character,
representations of elements that are contained in this
memory system and their organization into largerscale mental structures and
functional processes operating on these structures,
performance mechanisms utilizing them and learning
mechanisms that change them.
An existing structure1,2 for a situated design agent
(Figure 1) is specialized and related key concepts are
adapted for developing the computational cognitive
architecture in this paper. This new architecture is
illustrated in Figure 2. Various components of the
intelligent virtual character are represented as boxes in
the diagram, with paths for information flow labelled
with numbers.
Processing within the architecture is organized
around different computational cognitive systems
labelled as conceptor, perceptor, sensor and memory
system in Figure 2. Each of these systems contributes
Figure 1. An existing structure for a design agent used for the
development of the cognitive architecture in this paper (from
Reference1,2).
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Copyright # 2009 John Wiley & Sons, Ltd.
Comp. Anim. Virtual Worlds (2009)
DOI: 10.1002/cav
A COGNITIVE ARCHITECTURE FOR VIRTUAL CHARACTER
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Figure 2. Computational cognitive architecture developed in this paper for intelligent virtual character (numbered paths are
indicated as (#) in the text where # is the number as shown in the diagram) (expanded and specialized from Reference1,2).
towards the operations of reflexive, reactive and
reflective process models to create behavioural models
(Figure 3). Details of these cognitive systems, process
models and behavioural models are described in the
following sub-sections.
Computational Cognitive Systems:
Sensor, Perceptor and Conceptor
Sensor, perceptor and conceptor1,2 are different subsystems within the architecture that provide functionalities for sensation, perception and conception.
Sensation is an information extraction process. Information from the environment is extracted into the agent
according to the current configuration of the sensor. This
configuration is dictated by perception that biases the
way the sensor works to limit the type and amount of
information sensed. Perception, after creating the bias
for sensation, organizes the sensory data into coherent
structures as required by conception. It operates on
existing knowledge and experiences to form concepts
relevant for the current situation. Each of these
processes, sensation, perception and conception, draws
upon information from the memory system to produce
actions through the effector.
Memory System
Figure 3. Creation of different behavioural models from the
cognitive architecture by various configurations of process
models.
The purpose of the memory system is to provide an
environment for the storage, retrieval, modification and
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Copyright # 2009 John Wiley & Sons, Ltd.
Comp. Anim. Virtual Worlds (2009)
DOI: 10.1002/cav
P. S. LIEW, C. L. CHIN AND Z. HUANG
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construction of memory out of knowledge and experience1. The right-hand side of Figure 2 illustrates details
of the memory system expanded from Liew and
Gero’s1,2 work on situated design agents. A brief
description of each memory sub-system follows.
Working memory is a workspace for deliberative
processes, where explicit reasoning and executive
control functions are performed with caching (11)2.
These processes selectively alter and/or add information to the working memory based on the content of
long-term memory (LTM) (14) or short-term memory
(STM) (12). Information, within the working memory, is
combined with the stored knowledge and experiences,
manipulated, interpreted and recombined to develop
new knowledge, assist learning, form goals and support
interactions with the external environment. Knowledge
and experiences accumulated are available for use in the
immediate task and once activated, they can then be
manipulated for the extraction of novel information that
had not been stored explicitly in the past. The
behaviours of these operations are influenced by the
external environment, the interactions between agent
and environment, and the internal state of the agent. All
the above inputs are subjected to biases (13) before they
are processed within the working memory system. The
bi-directional path (13) between the priming component
and the working memory models the effect of using the
default bias or the setting of a pre-defined bias.
Short-Term Memory is modelled as a short-term, fixedsize buffer that captures the limited-capacity characteristics of STM. The content of the buffer is constructed
from the memory trace that enters the STM (16). The
path (17) from the buffer to the LTM indicates the buildup of a long-term trace due to the memory trace’s levelof-processing. The slots in STM are used to indicate the
notion of the relative level-of-processing of the various
traces within the buffer.
1
Knowledge refers to the general facts related to that domain
that are given to the agent when it was first created. The agent is
not involved in any way with these facts. Experience of an agent
entails the agent’s involvement as the ‘first person’ in dealing
with the substance of that experience. Memory is a ‘thing’ that
involves a construction process for its formation. Memory
construction is the process that produces a memory by manipulating knowledge and experience. It requires a total reorganization of what the system knows. Memory retrieval is a
process that treats the memory system as a static store, where
required knowledge is retrieved according to simple matching
between the stimulus from the environment and the preconditions stored within the memory system. Memory modification is a process that entails minor adaptation of retrieved
knowledge for the current situation.
2
Numbers in parentheses refer to the paths in Figure 2.
Both STM and LTM are used in the construction of
memories within the working memory in the reflective
and reactive mode of operations ((12) and (14)). The
differences between memory traces residing in STM and
LTM lie in the fact that memories in STM are not as
readily recalled in the memory construction process as
those in LTM.
The STM serves as a gateway into and out of the LTM.
The path into LTM occurs when memory traces are
transferred from the working memory to the LTM
through STM. As soon as a memory of an experience has
been constructed and served its purpose within the
working memory, it is transferred into STM as a new
memory trace (12). Memory traces in STM are integrated
into LTM gradually through their level-of-processing
(17). The STM acting as a gateway out of the LTM occurs
when memory traces in LTM that are not grounded
frequently are transferred into STM (16) and out of the
memory system. These two actions model the phenomenon of long-term learning and the phenomenon of ‘use
it or lose it’ in the human memory system.
The amount and form of memory traces that are
transferred from STM to LTM (17) are primarily a
function of their level-of-processing. For a memory trace
to be transferred to LTM, it must go through a series of
encoding levels. Active processing of a memory trace
through its grounding, according to the current
situation, produces durable STMs. As the durability of
a memory trace increases, its level of encoding increases.
The increase in level-of-processing of a specific memory
trace facilitates its inclusion into LTM. This simple
formulation captures the important characteristic of
long-term learning in which a process of encoding
material, in terms of prior experiences and grounded
usage, produces comparatively durable and readily
retrievable memory traces.
Long-Term Memory is a relatively permanent repository for memory traces. Memory traces stored here do
not disappear as readily as those in STM. New memory
traces about experiences are encoded into LTM after
they have been processed in the working memory and
STM. To influence subsequent explicit behaviour, these
memory traces within LTM and STM are brought back
into working memory, where they become part of the
reasoning process.
Long-term memory refers to all effects of prior
experiences and knowledge on subsequent behaviours
of the situated agent. These behaviours are instantiated
through the various reflective, reactive or reflexive
processing models (Figure 3). Explicit memory subsystems provide memories for reasoning involving the
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Copyright # 2009 John Wiley & Sons, Ltd.
Comp. Anim. Virtual Worlds (2009)
DOI: 10.1002/cav
A COGNITIVE ARCHITECTURE FOR VIRTUAL CHARACTER
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working memory (for reflective and reactive process
models) while implicit memory sub-systems deal with
memories that affect the reflexive behaviour of the agent
(reflexive process models).
Explicit memory provides the required information
for reflective and reactive processing models. Requests
for explicit memories and transfer of constructed
memories occur through process (14). These operations
are subjected to the bias activated at the current point in
time (13). Explicit memories that are not subsequently
grounded are transferred to STM before they are purged
from system (16). On the other hand, memories in STM
that have passed through a series of processing (levelsof-processing) are transferred to explicit memory (17) for
longer retention.
Episodic memory deals with the storage and construction of memory traces related to experiences. Once
the memory of an experience has been constructed and
used within the working memory, it is stored in STM as
a new experience where it is subjected to several levels of
processing before it is transferred into the episodic
memory (17). The content of the episodic memory is also
subjected to generalization (20) where a common set of
experiences is compiled into a general rule and the
original experiences may be removed from the system.
Semantic memory deals with general principles and
refers to the acquisition, storage and usage of knowledge. This knowledge is grounded according to the
agent’s current internal state in relation to the current
environment and is subjected to addition, modification,
updating, replacement and removal as the agent learns
new knowledge.
Implicit memory captures knowledge expressed
through performance rather than through recall and
adaptation (semantic memory) or construction (episodic
memory). Experiences are accumulated in behavioural
changes. The reflexive behaviour of the agent is based on
the implicit memory system. Implicit memories are
taken as memories that are not used explicitly within the
working memory during reasoning. These memories
are previously acquired information that influences
the agent’s behaviour or reasoning process either
directly or indirectly. Implicit memory involves a whole
range of implicit learning systems that can act as the
basis for analysing perceptual and motor processing
(10), and they typically involve relatively automatic
retrieval processes that are normally not carried out
during reflective and reactive processes. Feedbacks from
the sensor (10) form the basis of these implicit learning
processes. With practice, implicit skills can be improved.
The improvement is retained (remembered) from one
practice session to the next even though the information
remembered occurs outside the working memory.
In other cases, a pattern of behaviour that was learned
through the working memory can gradually become
automatic or habitual with repetition, causing memories
that were once explicit to become implicit. Thus implicit
memory can be used to carry out tasks in a reflexive
manner, leaving the reflective and reactive processes
free for other tasks (18).
The contents of implicit memory are also subjected to
modification by the working memory through a series of
explicit learning processes. These processes add, remove
and alter the contents of implicit memory through
normal machine learning processes (18).
Implicit memory has a constraining characteristic of
extreme dependence on the immediate situation. A
specific behaviour is automatically called upon by a
specific configuration of stimuli. The same behaviour is
not involved in another set of stimuli. Explicit
memories, on the other hand, are used in contexts
other than those in which they were originally acquired
and are therefore used not only to guide repetitive
behaviour but also to plan future behaviour and to
modify existing routines.
Procedural memory contains process knowledge on
how to perform a specific task. This knowledge about
processes can be taken as the skill that an agent
possesses. With practice, these skills can be improved.
The improvement is retained (remembered) from one
practice session to the next using feedback from the
working memory (18), even though the information
remembered is used in a reflexive manner later ((3) via
(10)). If necessary, the knowledge learned is used
together with that in the semantic memory if its
application into the current situation needs to be
considered together with other knowledge.
Priming is the process that activates (biases) one or
more existing memory traces by a stimulus (the priming
stimulus) presented to the working memory system (13),
and it influences the subsequent perception (9) and
reasoning processes (8). The priming stimulus originates
from memory traces contained within LTM or STM ((15)
and (19)) and it allows activated memories in STM or
LTM to be brought into working memory more easily
after the priming experience than before ((12) and (14)).
Priming plays an important role in the explicit
reasoning process. When an object, idea (concept) or
event is seen (through perception) or thought about
(within the reflection and reactive processes), those
elements of the semantic memory, that are relevant to
that particular perception or thought, are activated
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Copyright # 2009 John Wiley & Sons, Ltd.
Comp. Anim. Virtual Worlds (2009)
DOI: 10.1002/cav
P. S. LIEW, C. L. CHIN AND Z. HUANG
*******************************************************************************************************************
(primed) for a period of time and made more available
than usual for retrieval to the working memory for
deliberation. This makes the reflective and reactive
processes operate logically rather than jumping randomly from one idea or image to another.
Perceptual (form) priming ((9) and (13)) can improve
the facility for detecting or processing perceptual objects
based on recent experience. It can involve the acquisition
of new information or the modification of existing
memory representation that not only improves the
ability to identify stimuli but also alters judgments and
preferences that are involved in the same stimuli.
Conceptual (meaning) priming ((8) and (13)) affects
the agent’s behaviour indirectly by influencing the
reflection and reaction processes. It influences the
processes that are carried out within working memory
and involves the activation of concepts stored in the
semantic memory system.
Conditioning provides reflexive behaviour to the
agent by matching the pre-conditions of the rules it
contains with the conditions of the environment and
applying those rules when applicable. The agent’s
behaviour is affected directly by this reflexive action
in the reflexive operation. Common reactive operations
that are frequently used are transferred to the conditioning component (18) so that they can behave in a
reflexive manner without explicit reasoning in the
working memory.
The if-then rules, in the conditioning component, are
extremely dependent on the immediate situation. An
implicit memory is retrieved and used to guide
behaviour only at the moment when a specific set of
pre-conditions is satisfied.
The learning of new rules either by creating entirely
new rules or by modifying/replacing existing rules will
result in the creation of an artificial grammar. This
occurs through an implicit learning process such as
classical conditioning or operant conditioning.
Effector and Environment
The effector output actions that are performed within
the environment to produce effects that can be sensed
(left-hand side of Figure 2). Structural design of this
system in relation to the cognitive architecture is based
on Blumberg and Galyean’s12 layered architecture for
behaviour, motor system and geometry direction of
autonomous creatures and Reynolds’s13 action selection-steering-locomotion hierarchy of motion behaviours.
The effector operates at two levels: a controller layer and
an actuator layer.
At the controller level, motion commands resulting
from the reflective, reactive and/or reflexive processing
models are integrated and converted to control signals
to be sent to the actuator for execution of actions (21).
The actuator is modelled after Reynolds’s13 locomotion
level. It represents the agent’s embodiment within the
simulated world. Within the actuator, control signals
from the controller are converted into motions according
to the agent’s embodiment. The two-layered design of
the effector allows different actuators to be used without
affecting the rest of the architecture. If an appropriate
convention for communicating control signals has been
established, the entire architecture can be completely
independent of the specific actuator of similar functionalities. When a new actuator is used, only the
mechanism for mapping control signals to motions that
is unique to the agent’s new embodiment needs to be
altered.
Actions performed by the actuator are subjected to
simulated physical constraints imposed by the environment on the agent’s embodiment. In order to compensate this interaction between the agent’s body and the
environment, the control commands can be tuned
according to the actuator used through self-calibration.
Knowledge of such self-calibration skills can be called
upon reactively (9) or reflectively (8).
Reflexive, Reactive and Reflective
Process Models
There are three basic process models that can be derived
from the cognitive architecture of agency: reflexive,
reactive and reflective process models. These are
extensions of processes of the same name the operate
within virtual agents.14
Reflexive process model represents processes that
are very fast and operate in a parallel fashion. They
are either innate abilities of the agent or are
learned environmental competencies and behaviours.
Operations of reflexive processes are based on a direct
response to stimulus sensed by the agent (7, 10, 3, 1)3.
Sensory data about the environment and about the
internal state of the agent are acquired through the
sensor and propagate without perception to effectors. A
reflexive process cannot devise, evaluate and choose
alternative possible actions in advance of performing
3
Sequence of numbers in parentheses refers to the information
flow paths in Figure 2.
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Copyright # 2009 John Wiley & Sons, Ltd.
Comp. Anim. Virtual Worlds (2009)
DOI: 10.1002/cav
A COGNITIVE ARCHITECTURE FOR VIRTUAL CHARACTER
*******************************************************************************************************************
them. Various forms of implicit learning/training can
extend the capabilities of the reflexive sub-mechanism.
A major characteristic of the reflexive process is that all
responses, internal within the agent or external to the
environment, are activated as soon as their triggering
conditions are met. This process can break down if the
agent is presented with a condition that was not predefined for the system.
The main purpose of reactive processes is to tune
parameters for correct behaviours by devising new
combinations of actions to cope with novel contexts.
During the operation of a reactive process within the
working memory, a model is created to evaluate
alternative actions before they are carried out (7, 6, 9, 4,
1). A number of different temporary structures such as
alternative plans may be created and compared in some
way prior to selection. The operation of a reactive process
does not involve additional processes that create new
functional capabilities; rather it uses a general sub-system
to create and evaluate different configurations of the
current capabilities. Operations in the reactive process
that are called upon frequently can be made to behave in a
reflexive mode (bypassing the reasoning process) and run
in parallel with other reactive process. The concurrency of
these operations allows routine tasks to be carried out
automatically, leaving the reasoning process to deal with
tasks that are more complicated and thus facilitating
better overall performance of the system.
Reflective process model represents processes that
interact with other internal processes, within the working memory, to provide an overall behaviour of the
agent (7, 6, 5, 8, 2, 1) by behavioural parameter reparameterization and expansion. Agents operating in
the reflective mode monitor, evaluate and modify other
processes occurring within the agent to regulate and
direct the internal and external behaviour of the agent.
The reflective processes within the agent give it the
abilities to reason about past events, make predictions
about future events, form concepts, and act in a proactive manner according to some goals.
that span from instant stimulus-response (found in
crowd simulations) to high deliberation (found in
‘heavy’ agents with high-level cognitive abilities).
To illustrate the concept of the behavioural model, we
demonstrate the change in behaviour of a virtual character
under the influence of reflective, reactive and reflexive
processes in an in-house application, named ‘TOWN’, in
the coming sections. The next section also draws the
parallel between the behavioural model concepts and the
algorithm that drive the virtual character.
Initial Implementation
TOWN (pronounced dot-town) is a virtual town
exploration application. It introduces Singapore landmarks, representing them in this virtual world by
caricatured models with low polygon count. A virtual
character, a bear, roams around in the virtual world and
brings its audience to the different places of interest.
Interaction from users is minimal—users can stop/run
the bear, look at the surrounding of the bear using
different camera views and ‘teleport’ the bear to another
location.
In the application, steering behaviours13,15 are used to
control the movement of the bear. Movements such as
seek, arrive, obstacle avoidance and wander are modelled as
forces acting on the bear that influence its movement.
These forces are scaled (Figure 4) according to their
importance before combining as a resultant force that
steer the bear in a particular direction with a specific
velocity. Figure 4 illustrates an example of four steering
forces (F1, F2, F3 and F4) calculated from a behavioural
model acting on an agent. Each of these forces is scaled
by a factor (a, b, g, d) that represents its influence on the
motion of the agent. The behavioural model used here
Behavioural Model
Behavioural models represent the lowest level of
abstraction in the architecture. These models are used
directly to control a virtual character’s behaviour. A
behavioural model can be created out of a combination
of different reflective, reactive and reflexive processes
(Figure 3). This gives the character reasoning capabilities
Figure 4. Steering forces scaled by their influence on the
motion of the agent.
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Copyright # 2009 John Wiley & Sons, Ltd.
Comp. Anim. Virtual Worlds (2009)
DOI: 10.1002/cav
P. S. LIEW, C. L. CHIN AND Z. HUANG
*******************************************************************************************************************
utilizes the reflective, reactive and reactive process
models.
At the fundamental operational level, all steering
forces are calculated by algorithms stored within the
procedural memory according to sensory data extracted by
the sensor from the virtual environment (italic text refers
to different modules illustrated in Figure 2). These forces
are sent to the controller (10, 3) as motion commands
together with default scaling from the sensor and sum
together in the default order as determined by the
controller. A control signal modelled as a resultant force
acting on the bear is sent to the actuator (21) for the bear
to execute its movement. This process utilizes simply the
effector, sensor and procedural memory (7, 10, 3, 1) in a
reflexive manner.
There are two fundamental ways to control the
movement of the agent through steering forces. The first
is by changing the influence of each steering force
(Figure 5). The second is by adding new steering forces
(Figure 6).
Changing the influence of each steering force can be
seen as correcting errors in movement such as the bear
crashing into walls or the bear bouncing between two
parallel obstacles. The behavioural model operates in a
reactive mood when this happens (7, 6, 9, 4, 21, 1). The
perceptor can be set to look out for signs of error
movement by interpreting the sensor data (6) and trigger
an event to extract relevant knowledge from semantic
memory (9, 14) to modify the influence of each steering
force coming from Procedural Memory (19, 14, 9). Forces
that are subsequently sent to the controller (4) as motion
commands are modified with new scaling from perceptor
and summed in an order specified by the controller to
create the necessary controller signal for the actuator (21)
to correct the movement of the bear.
Figure 6. Adding new steering forces to control the agent’s
movement.
Adding new steering force to control the bear’s
movement can model achieving of high-level goals
through reflective reasoning (7, 6, 5, 8, 2, 21, 1). In
this reflective mode, the conceptor can create high-level
goals by processing the information from the perceptor
according its knowledge stored in semantic memory (6, 5,
8, 14). For example, the bear may have an energy level
attribute (computed from selected sensory data based on
knowledge) that starts to deplete as the bear moves in
the environment. As soon as a certain level has been
reached, a ‘hunger’ emotion may be triggered to create a
‘find food’ goal. This goal can create additional forces in
the algorithms within procedural memory (19) that steer
the bear towards specific location in order to find food.
When the time comes, all of these forces are sent to the
controller (19, 14, 8, 2) as motion commands together with
the required scaling from the conceptor and summed
together in the order as determined by the controller. A
control signal modelling the resultant force that pulls the
bear towards the landmark with food is sent to the
actuator (21) for the bear to execute its movement.
Results
Figure 5. Changing the influences of different existing steering forces to control the agent’s movement.
Up to this point, much has been said of the reflexive,
reactive and reflective processes. In our visual simulation using the TOWN application, the influence of
these three processes on the bear is demonstrated. The
effect of reactive adjustment is best seen when the bear is
in a confined space. We placed the bear in two locations
in the virtual world (Figures Figure 7(a) and 7(b)) where
its running space is small. Without reactive adjustment
(and hence in reflexive mode), the bear is easily trapped
*******************************************************************************************************************
Copyright # 2009 John Wiley & Sons, Ltd.
Comp. Anim. Virtual Worlds (2009)
DOI: 10.1002/cav
A COGNITIVE ARCHITECTURE FOR VIRTUAL CHARACTER
*******************************************************************************************************************
Figure 7. (a) (b) The confined spaces in the virtual world.
in the confined area, bouncing from one wall to another.
In the reactive processing, the scales of the steering
forces are adjusted, correcting errors so that the bear can
manoeuvre itself out of the confined space.
The visual simulation also demonstrates the influence
of reflective process, which is adjusted by creating
additional forces that will steer the bear towards a
designated destination. In the visual simulation, the bear
wanders aimlessly when there is no reflective adjustment. When the reflective process is turned on, the bear
moves towards its target, manoeuvring itself out of any
obstacles that come in its path.
We see from the visual simulation that with the
adjustment of the reactive and reflective processes, the
virtual character shows intermediate and goal-directed
form of reasoning. The concept of the behavioural model
of the proposed architecture enables the bear to exhibit
more human-like behaviour, hence increasing the
appeal and usefulness of the application.
Conclusion and Future Work
In this paper, our development of a computation
cognitive architecture for intelligent virtual characters
has been described. To demonstrate the effectiveness of
the development, a behavioural model that utilizes the
reflexive, reactive and reflective processes was used to
modify a common steering mechanism in our virtual
tour application.
The proposed architecture has the potential to
simulate human-like behaviours in intelligent virtual
characters. Future work will be on the representations
for items stored in each memory sub-system, and
processes that operate on these representations of the
proposed architecture. More realistic human-like behaviours can be simulated in this way.
ACKNOWLEDGEMENTS
The work is supported under the Personal 3D Entertainment
Systems Programme of the Institute for Infocomm Research,
ASTAR. The TOWN came into existence with contributions
from Bryan Chong, Chua Gim Guan, Loke Mei Hwan, Christina
Tang, Camellia Zakaria, Azmi Karim and Loh Jin Biao.The
authors are very grateful to these colleagues.
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Copyright # 2009 John Wiley & Sons, Ltd.
Comp. Anim. Virtual Worlds (2009)
DOI: 10.1002/cav
P. S. LIEW, C. L. CHIN AND Z. HUANG
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Authors’ biographies:
Pak-San Liew received his PhD in Design Computation
from the Key Centre of Design Computing and Cognition, University of Sydney in 2004. He is currently a
Senior Research Fellow in Institute for Infocomm
Research, ASTAR. His research interest is in artificial
intelligence and computer graphics.
Chin-Ling Chin received her Masters in Electrical and
Electronic Engineering from Imperial College, UK in
1997. She worked in DSO National Laboratories, Singapore, in speech processing before joining Institute for
Infocomm Research, ASTAR, as a Senior Research
Officer. Her research interest is in computer graphics.
Zhiyong Huang received his PhD in Computer Science
from EPFL, Switzerland in 1997. He worked in School of
Computing, NUS, Singapore, before joining Institute for
Infocomm Research, ASTAR, as a Senior Scientist. His
research interest is in computer graphics.
*******************************************************************************************************************
Copyright # 2009 John Wiley & Sons, Ltd.
Comp. Anim. Virtual Worlds (2009)
DOI: 10.1002/cav