=Paper=
{{Paper
|id=None
|storemode=property
|title=General Theory of Interaction and Cognitive Architectures
|pdfUrl=https://ceur-ws.org/Vol-1000/ICTERI-2013-p-004-015-Inv-3.pdf
|volume=Vol-1000
|dblpUrl=https://dblp.org/rec/conf/icteri/Letichevsky13
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==General Theory of Interaction and Cognitive Architectures==
https://ceur-ws.org/Vol-1000/ICTERI-2013-p-004-015-Inv-3.pdf
General Theory of Interaction and Cognitive
Architectures
Alexander Letichevsky
Glushkov Institute of Cybernetics, Academy of Sciences of Ukraine
40 Glushkova ave., 03187, Kyiv, Ukraine
let@cyfra.net
Abstract. The challenge of creating a real-life computational equiva-
lent of the human mind is now attracting the attention of many scientific
groups from different areas of cybernetics and Artificial Intelligence such
as computational neuroscience, cognitive science, biologically inspired
cognitive architectures etc. The paper presents a new cognitive archi-
tecture based on insertion modeling, one of the paradigms of a general
theory of interaction, and a basis for multiagent system development.
Insertion cognitive architecture is represented as a multilevel insertion
machine which realizes itself as a high level insertion environment. It has
a center to evaluate the success of its behavior which is a special type
agent that can observe the interaction of a system with external environ-
ment. The main goal of a system is achieving maximum success repeated.
As an agent this machine is inserted into its external environment and
has the means to interact with it. The internal environment of intelligent
cognitive agent creates and develops its own model and the model of ex-
ternal environment. If the external environment contains other agents,
they can be modeled by internal environment which creates correspond-
ing machines and interprets those machines using corresponding drivers,
comparing the behaviors of models and external agents. Insertion ar-
chitecture is now under development on the base of Insertion modeling
system, developed in Glushkov Institute of Cybernetics.
Keywords. AgentBasedSystem, DistributedArtificialIntelligence, Rea-
soning, FormalMethod, Simulation
Key terms. AgentBasedSystem, DistributedArtificialIntelligence, Rea-
soning, FormalMethod, Simulation
1 Introduction
General theory of interaction is a theory of information interaction in complex
distributed multi-agent systems. It has a long history. Contemporary part of
this history can be considered as starting from neuro networks of McCulloch-
Pitts [23]. The model of neuro nets caused the appearance of abstract automata
theory, a theory which helps study the behavior and interaction of evolving
systems independently of their structure. The Kleene-Glushkov algebra [13, 7]
General Theory of Interaction and Cognitive Architectures 5
is the main tool for the description of the behaviors of finite state systems.
Automata theory originally concentrated on the study of analyses and synthesis
problems, generalization of finite state automata and complexity. Interaction in
explicit form appeared only in 70s as a general theory of interacting information
processes. It includes the CCS (Calculus of Communicated Processes) [24, 25]
and the π-calculus of R. Milner [26], CSP (Communicated Sequential Processes)
of T. Hoare [10], ACP (Algebra of Communicated Processes) [3] and many other
various branches of these basic theories. Now all these calculi and algebras are
the basis for modern research in this area. Fairly complete survey of the classical
process theory is presented in the Handbook of Process Algebras [4], published
in 2001.
Insertion modeling is a trend that is developing over the last decade as an
approach to a general theory of interaction of agents and environments in com-
plex distributed multi-agent systems. The first works in this direction have been
published in the middle of 90s [6, 15, 16]. In these studies, a model of interac-
tion between agents and environments based on the notion of insertion function
and the algebra of behaviors (similar to some kind of process algebra) has been
proposed. The paradigm shift from computing to interaction was extensively dis-
cussed in computer science that time, and our work was in some sense a response
to this trend. But the real roots of the insertion model should be sought even
earlier, in a model of interacting of control and operational automata, proposed
by V. Glushkov back in the 60s [8, 9] to describe the structure of computers.
In the 70s the algebraic abstraction of this model were studied in the theory of
discrete processors and provided a number of important results on the problem
of equivalence of programs, their equivalent transformations and optimization.
Macroconveyor models of parallel computing, which were investigated in 80s
years [11], even more close to the model of interaction of agents and environ-
ments. In these models, the processes corresponding to the parallel processors
can be considered as agents that interact in an environment of distributed data
structures.
In recent years, insertion modeling has been applied to the development
of systems for the verification of requirements and specifications of distributed
interacting systems [2, 12, 19–21]. The system VRS, developed in order from Mo-
torola, has been successfully applied to verify the requirements and specifications
in the field of telecommunication systems, embedded systems, and real-time sys-
tems. A new insertion modeling system IMS [17], which is under development
in the Glushkov Institute of Cybernetics of the National Academy of Sciences
of Ukraine, is intended to extend the area of insertion modeling applications.
We found many common features of the tools used in software development area
based on formal methods and techniques used in biologically inspired cognitive
architectures. This gives us hope to introduce some new ideas to the development
of this subject domain.
This paper presents the main principals of insertion modeling and the con-
ception of cognitive architecture based on insertion modeling. To understand the
formal part of the paper reader must be familiar with the concepts of labeled
6 A. Letichevsky
transition system, bisimilarity and basic notions of general process theory. The
mathematical foundation of insertion modeling is presented in [18].
2 The Basic Principals
Insertion modeling deals with the construction of models and study the inter-
action of agents and environments in complex distributed multi-agent systems.
Informally, the basic principles of the paradigm of insertion modeling can be
formulated as follows.
1. The world is a hierarchy of environments and agents inserted into them.
2. Environments and agents are entities evolving in time.
3. Insertion of agent into environment changes the behavior of environment and
produces new environment which is in general ready for the insertion of new
agents.
4. Environments as agents can be inserted into higher level environment.
5. New agents can be inserted from external environment as well as from in-
ternal agents (environments).
6. Agents and environments can model another agents and environments on
the different levels of abstraction.
All these principles can be formalized in terms of transition systems, behavior
algebras, and insertion functions. This formalization can be used as high level
abstractions of biological entities needed for computer modeling of human mind.
The first and the second principals are commonly used in information mod-
elling of different kinds of systems, for example as in object oriented or agent
programming. They are also resembling to M. Minsky’s approach of the society
of mind [27].
The third principal is clear intuitively, but has a special refinement in inser-
tion modelling. We treat agents as transition systems with states considered up
to bisimilarity (or up to behavior, which is the same). The type of an agent is
the set of actions it can perform. The term action we use as a synonim of label
for transitions, and it can denote signals or messages to send, events in which an
agent can participate etc. This is the most general notion of agent which must
be distinguished from more special notions of autonomous or intellectual agents
in AI.
Transition system consists of states and transitions that connect states. Tran-
sitions are labeled by actions (signals, events, instructions, statements etc.).
Transition systems are evolving in time changing their states, and actions are
observable symbolic structures used for communication. We use the well-known
a
notation s −→ s0 to express the fact that transition system can evolve from the
0
state s to s performing action a. Usually transition systems are nondeterministic
and there can be several transitions coming from the same state even labeled by
the same action. If we abstract from the structure of states and concentrate only
on (branching) sequences of observable actions we obtain the state equivalence
General Theory of Interaction and Cognitive Architectures 7
called bisimilarity (originated from [28] and [24], exact definition can be found
in [18]). Bisimilar states generate the same behavior of transition systems.
Environment by definition is an agent that possesses the insertion function.
Given the state of environment s and the state of agent u, insertion function
computes the new state of environment which is denoted as s[u]. Note that we
consider states up to bisimilarity and if we have some representation of behaviors,
the behaviors of environment and agent can be used as states. The state s[u]
is a state of environment and we can use insertion function to insert a new
agent v into environment s[u] : (s[u])[v] = s[u, v]. Repeating this construction
we can obtain the state of environment s[u1 , u2 , . . .] with several agents inserted
into it. Insertion function can be considered as an operator over the states of
environment, and if the states are identified with behaviors, then the insertion
of a new agent changes the behavior of environment.
Environment is an agent with insertion function, so if we forget the inser-
tion function, then environment can be inserted as agent into a higher level
environment and we can obtain hierarchical structure like
s[s1 [u11 , u12 , . . .]E1 , s2 [u21 , u22 , . . .]E2 , . . .]E
Here notation s[u1 , u2 , . . .]E explicitly shows the environment E to which the
state s belongs (environment indexes can be omitted if they are known from the
context). This refines the fourth principle.
Environment is an agent which can be inserted into external environment
and having agents inserted into this environment. The evolution of agents can
a a
be defined by the rules for transitions. The rules s[u] −→ s[u, v] and s[t[u, v]] −→
s[t[u], v] can be used for the illustration of the 5-th principal.
We consider the creating and manipulation of the models of external and
internal environments as the main property of cognitive processes of intellectual
agent. Formalization of this property in terms of insertion modeling supports
the 6-th principal.
Cognitive architecture will be constructed as a multilevel insertion environ-
ment. Below we shall define the main kinds of blocks used for construction of
cognitive architecture. They are local description unites and insertion machines.
3 Multilevel Environments
To represent behaviors of transition systems we use behavior algebras (a kind of
process algebra). Behavior algebra is defined by the set of actions and the set
of behaviors (processes). It has two operations and termination constants. Op-
erations are prefixing a.u (a - action, u - behavior) and nondeterministic choice
u + v (u and v - behaviors). Termination constants are successful termination
∆, deadlock 0, and undefined behavior ⊥. It has also approximation relation ⊆,
which is a partial order with minimal element ⊥, and is used for constructing
a complete algebra with fixed point theorem. To define infinite behaviors we
use equations in behavior algebra. These equations have the form of recursive
definitions ui = Fi (u1 , u2 , . . .), i = 1, 2, . . . and define left hand side functions as
8 A. Letichevsky
the components of a minimal fixed point. Left hand sides of these definitions can
depend on parameters ui (x) = Fi (u, x) of different types. In complete behavior
algebra each behavior has a representation (normal form)
X
u= ai .ui + εi
i∈I
which is defined uniquely (up to commutativity and associativity of nondeter-
ministic choice), if all ai .ui are different (εu is a termination constant).
The type of environment is defined by two action sets: the set of environment
actions and the set of agent actions. The last defines the type of agents which
can be inserted into this environment: if the set of agent actions is included in
the set of agent actions of environment then this agent can be inserted into this
environment. This relation is called compatibility relation between agents and
environments (agent is compatible with environment if it can be inserted into
this environment). Multilevel environment is a family of environments with dis-
tinguished the most external environment. The compatibility relation on the set
of environments defines a directed graph and we demand for multilevel environ-
ment that the outermost environment would be reachable from any environment
of the family in this graph.
To define the insertion function for some environment it is sufficient to define
transition relation for all states of environment including states with inserted
agents. The common approach is to define behavior by means of rules. The
following is an example of such rule:
b a
− s0 , u −
s→ → u0
c P (a, b, c)
− s0 [u0 ]
s[u] →
This rule can be interpreted as follows. Agent in the state u can make a transition
a
u−→ u0 . Environment allows this transition if the predicate P (a, b, c) is true. This
rule defines behavior property of environment in some local neighborhood of the
state s[u]. So such a rule belongs to the class of local description units discussed
in the next section.
At a given moment of time an agent belongs (is inserted) to only one en-
vironment. But if the type of an agent is compatible with the type of another
environment it can move to this environment. Such a movements can be de-
scribed by the following types of rules:
moveup E
u −−−−−−−→ u0
P1 (E, F, u, moveup(E))
moveup(F →E)
E[F [u, v], w] −−−−−−−−−−→ E[F [v], u0 , w]
moving from internal to external environment;
movedn F
u −−−−−−−→ u0
P2 (E, F, u, movedn(F ))
movedn(E→F )
E[F [v], u, w] −−−−−−−−−−→ E[F [u0 , v], w]
General Theory of Interaction and Cognitive Architectures 9
moving from external environment to internal one;
moveto G
u −−−−−−→ u0
P3 (E, F, u, moveto(F ))
moveto(F →G)
E[F [u, v], G[w]] −−−−−−−−−→ E[F [v], G[u0 , w]]
moving to another environment on the same level. In all cases permitting con-
ditions must include the compatibility conditions for corresponding agents and
environments. The rules above define the property of a system called mobility
and underlies the calculus of mobile ambients of Luca Cardelli [5].
4 Local Description Units over Attribute Environments
A special type of environments is considered in cognitive architecture to have
a sufficiently rich language for the description of environment states properties.
These environments are called attribute environments. There are two kinds of
attribute environments – concrete and symbolic.
The state of concrete attribute environment is the valuation of attributes -
symbols that change their values while changing the state in time. Each attribute
has type (numeric, symbolic, enumerated, agent and behavior types, functional
types etc.). Some of functional and predicate symbols are interpreted symbols.
Now logic formulas can be used for the description of properties of agent or
environment states. We use the first order logic formulas as the basis that can
be extended by fuzzy logic, temporal logic etc.
The general form of local description unit is the following:
∀x(α(x, r) →< P (x, r) > β(x, r)),
where x is a list of typed parameters, r is a list of attributes, α(x, r) and β(x, r)
are logic formulas, < P (x, r) > is a process - finite behavior of an environment.
Local descriptions can be considered as formulas of dynamic logic, or Hoare
triples, or productions - the most popular units of procedural memory in AI.
In any case they describe local dynamic properties of environment behavior:
for all possible values of parameters, if precondition is true then a process of a
local description unit can start and after successful termination of this process
a postcondition must be true.
The states of symbolic environment are formulas of basic logic language of
environment. Such formulas are abstractions of classes of concrete states. Each
symbolic state covers the set of concrete states and the traces generated by local
description units cover the sets of concrete traces.
Local description units are the main units of knowledge representation in cog-
nitive architecture. A set of local description units can be used for the definition
of transitions of environment. In this case they can be considered as procedural
knowledge units. Logic knowledge can be represented as environment with the
states representing the current knowledge, and the local description units cor-
responding to the rules of inference in corresponding calculus. Local description
units can be applied in forward and backward modes. Forward mode can be used
for the generating of new knowledge, backward mode – for answering queries.
10 A. Letichevsky
5 Insertion Machines
Another construction blocks for cognitive architecture are insertion machines
intended for implementation of insertion environments. The input of insertion
machine is the description of a multilevel environment (a model of an environ-
ment) and its initial state, an output depends on the goal that is put to machine.
Multilevel environments are represented in cognitive architecture by means
of environment descriptions for different levels and a set of local description
units for insertion functions. Environment description contains the signature of
environment that includes types of attributes, types of inserted agents, and also
the description of sets of environment and agent actions. Local description units
used for the definition of insertion function are organized as a knowledge base
with special data structures providing efficient access to the needed descriptions
and history of their use.
To implement multilevel environment different kinds of insertion machines
are used. But all of them have the general architecture represented on the Fig.1.
Three main components of insertion machine are model driver (MD), behavior
Fig. 1. Architecture of Insertion Machine
unfolder (Unf), and interactor (Intr). Model driver is a component which con-
trols the machine traversal along the behavior tree of a model. The state of a
model is represented as a text in the input language of insertion machine and is
General Theory of Interaction and Cognitive Architectures 11
considered as an algebraic expression. The input language includes the recursive
definitions of agent behaviors, the notation for insertion function, and possibly
some compositions for environment states. Before computing insertion function
the state of a system must be represented in the form s[u1 , u2 , . . .]. This func-
tionality is performed by agent behavior unfolder. To make the movement, the
state of environment must be reduced to the normal form
X
ai .ui + ε
i∈I
where ai are actions, ui are environment states, ε is a termination constant. This
functionality is performed by the module environment interactor. It computes
the insertion function calling recursively if it is necessary the agent behavior
unfolder.
Two kinds of insertion machines are distinguished: real time or interactive
and analytical insertion machines. The first ones are functioning in the real or
virtual environment, interacting with it in the real or virtual time. Analytical
machines intended for model analysis, investigation of its properties, solving
problems etc. The drivers for two kinds of machines correspondingly are also di-
vided into interactive and analytical drivers. Interactive driver after normalizing
the state of environment must select exactly one alternative and perform the
action specified as a prefix of this alternative. Insertion machine with interactive
driver operates as an agent inserted into external environment with insertion
function defining the laws of functioning of this environment. External environ-
ment, for example, can change a behavior prefix of insertion machine according
to their insertion function. Cognitive interactive driver has criteria of successful
functioning in external environment, it accumulates the information about its
past in episodic memory, develops the models of external environment, uses some
learning algorithms to improve the strategy of selecting actions and increase the
level of successful functioning. In addition it should have specialized tools for ex-
change the signals with external environment (for example, perception of visual
or acoustical information, space movement etc.).
Analytical insertion machine as opposed to interactive one can consider dif-
ferent variants of making decisions about performed actions, returning to choice
points (as in logic programming) and consider different paths in the behavior
tree of a model. The model of a system can include the model of external en-
vironment of this system, and the driver performance depends on the goals of
insertion machine. In the general case analytical machine solves the problems by
search of states, having the corresponding properties (goal states) or states in
which given safety properties are violated. The external environment for inser-
tion machine can be represented by a user who interacts with insertion machine,
sets problems, and controls the activity of insertion machine. Analytical machine
enriched by logic and deductive tools are used for generating traces of symbolic
models of systems. The state of symbolic model is represented by means of prop-
erties of the values of attributes rather than their concrete values.
Insertion machine with separated external environment interface can be im-
plemented as a transition system with hidden structure that separates the kernel
12 A. Letichevsky
environment state and the states of inserted agents. Such implementation can
be more efficient and can be constructed using partial computations or other
specialization and optimization programming tools.
6 Cognitive Architecture
Like well-known cognitive architectures such as Soar [14], ACT-R [1] or many
other from the list of BICA society [29] insertion cognitive architecture ICAR
is an environment for construction of cognitive agents. The main blocks of this
architecture are local description units, agents, represented by their behaviors,
and insertion machines. Building blocks are collected in memory units that have
structures of knowledge bases or associative memories.
On the abstract level ICAR has the same architecture as cognitive agents that
can be created in it. From this point of view it can be considered as an intellectual
assistant for user who interacts with ICAR in the process of creating cognitive
agents. The general architecture of cognitive agent of ICAR is represented on
Fig.2.
Fig. 2. Insertion cognitive architecture
In general cognitive agent is constructed as a real time multilevel insertion
machine which realizes itself as a highest level internal environment. As an agent,
General Theory of Interaction and Cognitive Architectures 13
this machine is inserted in its external environment and has the means to interact
with it. This external environment includes a user and objects of external (real
or virtual) world to which agent has access.
One or several self-models can be inserted into the internal environment of
cognitive agent to be used when interacting with external environment or making
decisions and planning future activities. An agent has an estimation mechanism
to evaluate the success of its behavior. This mechanism is realized in the form
of a special agent that can observe the interaction of a system with external
environment and make estimation according to some criteria. These criteria can
be predefined initially and evolves in the future according to obtained experience.
The main goal of a system is achieving maximum success repeated.
The self-models of cognitive agent are created and developed together with
the models of external environment. If the external environment contains other
agents, they can be modeled by internal environment which creates correspond-
ing machines and interprets those machines using corresponding drivers, com-
paring the behaviors of models and external agents. All these models are evolving
and developing in the process of accumulating the experience in interaction with
the external world.
Some mechanisms that model emotional or psychological features (humor and
concentration, pleasure and anger, etc.) can be implemented at higher levels of
cognitive strucure. The mechanisms of decision making, planning and executing
plans are also at higher levels.
The main part of cognitive structure is the base of models describing the
history of cognitive agent functioning at different levels of abstraction. The in-
terface with external world provides language (symbolic) communication and
image processing. All interaction histories are processed in the working memory
of the self-level insertion machines and then transferred to the appropriate levels
of a model base.
The model base is always active. The analytical insertion machines which
control and manage the structure of model base are always busy with searching
solution of problems and performing tasks with ansatisfactory answers, or cre-
ating new models. All this activity models subconcious levels of cognition and
time-to-time interact with the higher levels of cognitive structure. Independent
levels of cognitive structure are working in parallel.
The hierarchy of environments of cognitive agent in some sense are similar
to six layers of neocortex. Moving from low levels to higher ones the levels of
abstraction are increased and used more and more abstract symbolic models.
How to create such models is a big challenge and we are working on it now.
Cognitive analytical insertion machines of ICAR are used by cognitive agents
to learn their models and their interaction with external environment to solve
problems better, accepts user helps as a teacher and teach user how to interact
better with ICAR. General learning mechanisms are the parts of model drivers
of different types.
14 A. Letichevsky
7 Conclusions
The description of cognitive architecture in the last section is a very tentative
reflection of our far goals. The nearer goals include the further development
of our system of proving pogram correctness [22], communication in natural
language, and living in virtual reality. As a zero approximation of ICAR the
insertion modeling system [17] is used.
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