=Paper=
{{Paper
|id=Vol-1419/paper0084
|storemode=property
|title=Interdisciplinary Development and Evaluation of Cognitive Architectures Exemplified with the SiMA Approach
|pdfUrl=https://ceur-ws.org/Vol-1419/paper0084.pdf
|volume=Vol-1419
|dblpUrl=https://dblp.org/rec/conf/eapcogsci/SchaatWKGJ15
}}
==Interdisciplinary Development and Evaluation of Cognitive Architectures Exemplified with the SiMA Approach==
https://ceur-ws.org/Vol-1419/paper0084.pdf
Interdisciplinary Development and Evaluation of Cognitive Architectures
Exemplified with the SiMA Approach
Samer Schaat, Alexander Wendt, Stefan Kollmann, Friedrich Gelbard, Matthias Jakubec
(schaat, wendt, kollmann, gelbard, jakubec@ict.tuwien.ac.at)
Institute for Computer Technology, Vienna University of Technology
1040 Vienna, Austria
Abstract behaviour is more suitable to analyse the basic functions of
the human mind.
In this paper we show how simple simulation scenarios can be
used to develop and test foundational functionalities of
cognitive architectures, exemplified with the SiMA State of the Art
architecture. We present an interdisciplinary methodology A good overview and classification of cognitive
that considers the challenges in capturing and evaluating basic
architectures are elaborated in (Duch, Oentaryo & Pasquier,
functionalities of the human mind. In this regard, we structure
and concretize assumptions from various disciplines and show 2008; Langley, Laird & Rogers, 2009; Vernon, von Hofsten
how we evaluate their plausibility in a consistent model, using & Fadiga, 2010). There, cognitive architectures are
parametrized simulations. classified into three categories: symbolic, emergent, and
hybrid architectures. Symbolic architectures process high-
Keywords: Artificial Intelligence; Cognitive architectures;
Intelligent agents; Computer simulation level symbols like objects or concepts and derive action
plans thereof. In emergent architectures no symbols are
Introduction processed but low-level activation signals in a network, for
example an artificial neural network, are propagated.
The computational approach to examine the human mind Actions emerge out of holistic structures. Emergent
provided a powerful methodology of research. When the architectures are self-organizing and bottom-up structured.
examination of information processing systems (such as the Hybrid architectures combine characteristics of both,
human mind) is at stake, computer scientists are particularly symbolic and emergent architectures.
suitable to contribute their experience. Nevertheless, Prominent examples of symbolic architectures are SOAR,
computer scientists often still approach problems of EPIC, ICARUS and NARS. Examples of emergent
Cognitive Science in a classical AI way. This is especially architectures are IPCA, Cortronics, NuPIC, and NOMAD.
the case regarding interdisciplinary: instead of concretizing ACT-R, CLARION, LIDA, DUAL, Polyscheme, 4CAPS,
models of the human mind from other disciplines into a Shruti, and Novamente can be regarded as hybrid
consistent and testable form, often own models that suffice architectures. For a description of these projects see, for
computational criteria (such as efficiency) are developed. In instance, Duch, Oentaryo & Pasquier (2008).
this regard computer science stays behind its possibilities in ACT-R (Anderson, Bothell, Byrne, Douglass, Lebiere &
contributing to understand the human mind. A counter- Qin, 2004), as a member of hybrid cognitive architectures,
example is to use the computational methodology in an processes its data with the help of different modules, for
approach of synthetic psychology (Braitenberg, 1986). example, a module for visual data, a module for motor data
Similarly, computational models often focus on simulating (actions), a module for goals. In each processing cycle,
high-level cognition without considering their foundations, production rules are matched against facts in short-term
such as motivation and emotion. We propose a more natural memory. The production rule which produces outcome,
approach in considering the foundations of cognition in a which is closest to ACT-R’s goals wins.
unified cognitive architecture that harnesses the possibilities In SOAR (Laird, Congdon, Coulter, Derbinsky & Xu,
given by computational simulations and is able to provide a 2011), a member of symbolic cognitive architectures, the
unified tool to test assumptions and their relationships to processing cycle selects operators which fit the current
each other. We will use superficially simple simulation problem and lead to a state which is closer to a desired goal.
scenarios to guide our development and test the resulting Furthermore, LIDA (Faghihi & Franklin, 2012) is a
model. On the one hand this considers that most of humans’ member of hybrid cognitive architectures. In LIDA the
behaviour is covered by every-day capabilities (what Bargh cognitive cycle activates modules to filter input data, to
& Chartrand (1999) called the unbearable automaticity of select actions, and to process the actions. Additionally,
being). On the other hand our experience with the cognitive LIDA has several built in learning mechanisms.
architecture SiMA1 (Simulation of the Mental Apparatus & In ICARUS (Langley, Choi & Trivedi, 2011), facts about
Applications) (Schaat, Wendt, Jakubec, et al., 2014; the environment and objects are called percepts and beliefs,
Dietrich, et. al., 2014) showed that – especially when the and rules are called skills. Skills are applied to percepts and
foundations of the human mind are at stake – every-day beliefs in order to reach ICARUS’s goals.
1 ARS (Artificial Recognition System) was renamed to SiMA.
515
SiMA Approach Ego, the demands from being a social creature, and the ego,
which has to mediate between the other two. This abstract
Rated according to the scheme sketched above the SiMA
theory is concertized in the SiMA project as a basis, which
architecture is a hybrid one. It defines three layers, where
is extended by contemporary theories from various
the lowest one comprises the neural activities, i.e. the sensor
disciplines, such as Damasio’s (2003) theory of emotions.
and actuator activities (see Figure 1, the leftmost block).
The second layer has to build neurosymbols from the neural
input and in the other direction neural actuator signals from Case-driven Agent-based Simulation
the symbolic results of the topmost layer, the psyche, which The challenges in capturing the functionality of the
is understood as a symbol processing machine. human mind in an interdisciplinary collaboration using
computational simulations pose special requirements on the
sexual drives
drive track/
unconscious
methodology in developing and evaluating the SiMA model.
self-preservation drives
Super-Ego The question here is, how to translate assumptions about
rules
external perception
perception track/ defense human mind’s functioning from other disciplines in a
unconscious mechanisms
body perception deterministic and testable simulation model? In the SiMA
sensors
and neuro- project case-driven agent-based simulation (Schaat &
neural (de-)
nets, symboli- Dietrich, 2014; Bruckner, Gelbard, Schaat et al., 2013) is
zation
actuators developed. This methodology guides interdisciplinary
selection of action/ selection of need/
action
conscious conscious collaboration in finding the required functions and data for a
simulation model of the human mind. A combination and
Figure 1: Overview of the SiMA model. adaption of casuistry, agent-based simulation and use-case
driven requirements engineering proved suitable to cope
Some specific key features define the SiMA approach. with the challenges of interdisciplinary collaboration and
First and foremost it is a functional model, i.e. it follows a the evaluation of models of the human mind. Amongst
generative approach with the focus on describing functions others these challenges are the restricted accessibility of the
that generate behavior instead of building a behavior model. human mind, interdisciplinary knowledge translation, the
This enables a generic and flexible model. Another feature complexity in explaining and evaluating models of the
is the layered description of human information processing. human mind.
The principle here is to use appropriate means of description The first step in case-driven agent-based simulation is the
for different aspects of a systems, e.g. the neuronal layer analysis of the model requirements. Here, we use a casuistic
should be described with other means than the psychic approach, where the behavior and underlying assumed
layer. In developing such a model we use a holistic and psychic processes in a concrete case, e.g. a hungry agent
unified approach, which considers a consistent and coherent perceiving a food source and another agent, are described in
description of all key aspects of human information a narrative way. But our experience in interdisciplinary
processing. The consideration of these key features is only collaboration showed that a textual concretization and
possible by following a bionic and hence interdisciplinary structuration is needed to use such exemplary case as a basis
approach. for further development of a causal and deterministic model.
The impetus for SiMA was the challenge, to design a Overall, the procedure of case-driven agent-based
control unit able to cope with ambiguous situations, such as simulation consists of following steps (also see Figure 2).
the security monitoring of an airport or the cooperation of a
robot with human co-workers. The artificial system should
have the same “feeling” for a situation as a human would
have. The only way to gain this could be the bionic
approach. So a holistic theory of the human mental
apparatus as the control unit of the human body (Solms,
2009) was needed to work as the blueprint for the SiMA
model. The basis was found in Freud’s metapsychology.
Freud came up with two major structuring concepts for the
psyche, the first and the second topographical model. In the
first model the distinction is made between the primary
process, where data are handled totally unconsciously
according to the pleasure principle, and the secondary
process with preconscious and conscious, also rational, data
treatment, where additionally the reality principle gets Figure 2: Case-driven agent-based simulation.
observed. From the point of view of computer science it
clearly is a data model, while Freud’s second topographical Exemplary Case
model is a function model. It distinguishes between the
The exemplary case is a narrative description of a
functions of the Id, the treatment of bodily needs, the Super-
concrete case that demonstrates assumptions in an
516
exemplary form, e.g. regarding motivations and decision Overall, this structuration enables a fine-grained
making in a concrete internal and external situation. requirements analysis, the development of a causal model
The exemplary case primarily serves as a platform for and its evaluation.
interdisciplinary collaboration that facilitate the discussion
between researchers, which often use different approaches
and vocabularies. Hence, the usage of a concrete case
supports bridging the disciplines and enhances the
understanding.
The exemplary case at hand (called “Adam is hungry”)
describes a simplified gent-object interaction. The initial
situation is given by hungry Adam, the agent with the SiMA
architecture, Bodo, a passive agent, and a Viennese
Schnitzel as a food source. The exemplary case describes
abstractly how Adam’s motivations, represented by drives,
get in conflict with perception and social norms. And how
mediating psychic processes finally decide his actions. In
short, Adam is confronted with choosing to eat, share, give
the Schnitzel or even beat Bodo. But under which external Figure 3: Simulation case “Adam is hungry”.
and internal conditions does he choose the respective
alternative actions? A deterministic description is needed. Evaluation
Generally, to use the exemplary case as a point of departure After developing the model (see sections below for an
for model development, some criteria must be considered. overview) it is tested using the simulation case as a test
These are especially the explication of assumptions and plan. In particular we parameterize the simulation according
requirements, and the consideration of a consistent and to the scenario’s data determinants and observe if the
deterministic description with a concrete focus (e.g. functions generate and data determine behavior as expected
motivation and decision making). Therefore the exemplary (see Chapter Calibration and Simulation). We do not only
case is transformed into, structured simulation case. validate the behavior, but also how the behavior is generated
and determined, e.g. how emotions and drives evolve and
Simulation Case influence the agent’s decision. If the agent behaves
A focus in analyzing and transforming the exemplary case unexpected or the data visualization indicate wrong
into a simulation case is an analysis of the data that assumptions, based on an analysis on different levels, we
determine the agent’s behavior. Following a functional have to conduct another iteration of the procedure (see
approach we also focus on how a change in these data feedback cycles a, b, and c in Figure 2). Possibility a and b
determinants would lead to a behavioral change. We indicate that the inputs form other disciplines
distinguish four groups of determinants: the agent’s (psychoanalysis, neuroscience) are interpreted and
experience, personality factors (as simplifications of transformed wrongly or implicit requirements emerge
memories and body functionalities), the environmental state, during implementation (implementing a model helps to
and the agent’s initial internal state (given by drives and understand and specify it). Possibility c may be caused by
emotions). inconsistencies in an underlying theory or between different
The simulation case for the described exemplary case is theories. This feedback helps to sharpen theories from other
sketched in Figure 3, with the standard scenario of eating disciplines precisely.
the Schnitzel, and the alternative scenarios of beating Bodo, This evaluation methodology enables us to test our
and sharing or giving the Schnitzel to Bodo. As sketched, model’s predictability and plausibility; in particular, the
the personality factor “neutralized intensity”, which validity of the case’s assumptions and if the specified data
indicates the strength of the defense and secondary process determine the expected behavior (change).
(see below), plays a key role in the selection of the scenario.
The transformation into a structured simulation case Primary Process
follows use-case-based requirements analysis in software In SiMA, the primary process represents unconscious data
engineering. Data determinants represent pre-conditions, the processing. It is characterized through fast and immediate
description of an agent’s final internal state and selected processing of data that is close to sensor values. Its logic can
action represent post-conditions. For the standard scenario, be well described by the rules that apply on associations
eat, the inner processes that generate the post-conditions between data structures. There are two rules of the forming
from these pre-conditions are described step-by-step. We of associations: similarity and simultaneousness. This
also have to track and justify every possible behavior of the means that things that are similar are likely to form
exemplary case (e.g. share, beat). That is, for the alternative associations as well as objects that occur at the same time or
scenarios we only describe how the change of data within a short time frame.
determinants would lead to an alternative behavior.
517
The inputs of the primary process are defined by the (severeness of a conflict) and the stage of development of
homeostasis of the body, the body perception and the the personality of the software agent.
external perception. Homeostatic values are symbolized into
drive tensions, which are a mean for intensity of bodily Secondary Process
need. In the Drive Track in Figure 1, drives are created from The secondary process is responsible for the
the drive tension and extended with a drive object and a preconscious/conscious processing of data. Its main task is
drive aim. The drive object is the external object, which is to take a decision about an action based on the inputs from
able to satisfy a drive and the drive aim is the action that has the primary process. However, different to the primary
to be taken to satisfy it. process, more extensive associations of data structures are
possible. Data structures are extended with a word, making
it possible to communicate the information to a received
outside of the system. Also, temporal and hierarchical
associations may be used, making it possible to order things.
At the beginning of the secondary process, activated stored
images, which were independent in the primary process are
formed into sequences called acts. Acts define events and
the actions necessary to be taken to get from an event to
another.
Figure 4: Drive representation.
External perception and body perception are symbolized selection of need/
conscious
selection of action/
conscious
and define the input of the Perception Track in Figure 1.
Here, based on perceived features, internal representation First decision process Second decision process
(so-called percepts) of objects are inferred. Through the Propose
options
Evaluate
options
Select option
Propose
options
Evaluate
options
Select option
property of simultaneousness, these objects form a
perceived image that represents the current situation. Figure 6: Decision making in the secondary process.
Through the property of similarity, similar stored situations
as stored images are then activated. In the stored images, Decision making in SiMA can be divided into two stages
memorized emotions are associated that reminds the system as seen in Figure 6. In each stage, similar process steps are
of a certain emotional state. Together with the drives, they taken: first to limit the number of options, considering all
generate the current emotions (Schaat, 2013) of the system available options. Second, the options passing the first stage
that will be used later on in decision making. have access to more system resources and one of them is
finally selected.
The first step in the decision making process is to extract
the possible options that the system can develop and act on.
It is the start of the Selection of Need track of Figure 1 and
Internalized
Internalized rules
rules Figure 6. This is done through the creation of possible goals
(“propose options” in Figure 6) from the acts or from
Defense mechanisms: perception. Drives from the primary process become drive
- reduce input data
Primary process
- detect and resolve
Secondary process wishes, which are one of the motivations to do something in
conflicts in input data
the system. They define the desired external object, the
Figure 5: Overview of defense mechanisms’ function. preferred action and the importance to reach it. Emotions
are transformed into feelings that can also be used to
At the verge of the primary process to the secondary
emphasize or to avoid certain situations.
process are the defense mechanisms located (see Figure 5).
After a general initialization with a basic effort analysis,
Defense mechanisms are a kind of filter mechanism. The
the possible goals are evaluated regarding the possibility to
two tasks of the defense mechanisms are, firstly, to reduce
fulfill a certain drive wish, an emotional state, and social
the data which flow from the primary process to the
rules (evaluate goals” in Figure 6). Based on the available
secondary process and, secondly, to detect and resolve
system resources one or more possible goals are selected for
conflicts in input data. In order to process the first task (data
further processing (“select option” in Figure 6).
reduction), the data are assessed by emotions and the focus
The selected possible goals are the options that the system
of attention is set on specific data with a high level of
has at the moment. In the Selection of Action track of Figure
activation. To process the latter task, to detect a conflict, the
1 and Figure 6, possible action plans are generated and
defense mechanisms have access to an internalized rule
evaluated for each of them. Then, one option is selected and
base, the Super-Ego-Rules. And in order to, eventually,
executed.
resolve a conflict, the defense mechanisms can repress input
Decision making of the secondary process is a
data or alter them before the defense mechanisms pass them
deliberative process in contrast to the primary process.That
on to the secondary process. Which defense mechanism is
is, the options of the system can be processed during
chosen, depends on personality factor “conflict tension”
multiple cycles without any external actions. The system
518
can reason about several options in sequence before taking a Standard scenario eat
decision. Internal actions are used to perform analysis of The first chart in Figure 8 describes the behavior of Adam
options and to execute queries to the knowledge base that during the standard scenario (eat the Schnitzel). The first
modify the internal state of the possible goal. column shows Adam’s current plan. The combination of
high hunger drive (see Figure ), the perception of a
Calibration and Simulation Schnitzel and the memorized satisfaction for eating
The modules in SiMA encapsulate functionalities of the Schnitzel, make Adam initially follow the plan to EAT.
human mind and are developed independently, following a After the Schnitzel was consumed, Adam switches to the
black-box approach. Meaningful integration tests for these BEAT plan, as it
modules require a level of knowledge about module fits the new
interaction, which is not available, due to the high number perception (Bodo;
of modules, parameterization options and their functional no Schnitzel) and
structure. Therefore we keep integration testing to a new drive state.
minimum in favor of system testing, using exemplary cases. Figure visualizes
Calibration is performed in various steps on each scenario. the changes in
First the environmental situation (Adam, Bodo, and the Adams drive state
Figure 9: Drives: hunger in green
Schnitzel) is modelled as the most basic layer of calibration. in detail. Adam
(aggressive) and red (libidinous).
Next, the drive situation is modelled and memories are starts with high
created to match memorized actions to drives, according to hunger. While he eats, the hunger drops, since eating the
the simulation case description. Where needed, the defense Schnitzel changes Adam’s body state which the drives
mechanisms are modelled and harmonized with the drive represent. In time, the hunger subsided below the sexual
situation. Lastly, the acts are modelled and associated to the drives, which started out low but steadily increase. The
memorized actions. Each step could, and often did, require stamina drives (Figure 9 in blue and cyan) represent Adam’s
previous steps to recalibrate to allow modeling according to need for relaxation and changed in response to Adams
the description. This resulted in a calibration strategy exhaustion while approaching the Schnitzel (first two peaks)
similar to a waterfall model with feedback. and Bodo (third peak).
Alternative scenario beat
Simulation Results This scenario differs from the standard scenario in its
initial drive state. Adam starts with higher, faster increasing
As mentioned, we validate our model via test scenarios in
sexual drives and low hunger. The BEAT plan is memorized
the MASON simulation framework. This chapter
with the highest satisfaction for the sexual drives and is
summarizes the results of these simulations. We compare associated with the current emotional state (see Figure 10).
the agent’s behavior and internal state to the expectations
Beating reduces the anger and causes a short peak of joy.
defined in the simulation case. The internal state of the
agent is checked via data visualizations.
The simulation scenarios are designed to show the
capabilities and impacts of the functional modules.
Exemplary case 1 is focused on the primary process,
specifically the interaction between perception, drive state
and defense mechanisms. The secondary process focusses
on following the memorized action sequences. In each
scenario, the agent can choose between four plans: EAT,
BEAT, GIVE and SHARE2. The initial environmental
situation is also shared among scenarios. The blue lines Figure 10: Emotional state in the beating scenario.
indicate sight ranges, the green Agent will be referred to as Alternative scenarios give and share
Adam, the red agent as Bodo and the round shape between These scenarios use a defense mechanism to alter Adam’s
them as Viennese Schnitzel. behavior away from the current drive demands. This is
achieved by the drive mechanism “sublimation”, which
changes the valuations of the possible actions associated to
the hunger drive, away from their memorized satisfaction
values. Due to their similarities they are discussed together.
The third chart in Figure 8 shows Adam’s behavior during
the give scenario and the fourth chart shows Adam’s
behavior during the share scenario.
In both scenarios, the drive situation is similar to the
Figure 8: Behavior sequences in the simulation case. standard scenario, with the hunger drives dominating. A
super-ego rule is introduced to create a conflict between
2 Plans are written in capital letters to distinct them from actions.
high hunger drives and perceiving Bodo with the Schnitzel.
519
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