=Paper=
{{Paper
|id=Vol-3668/paper12
|storemode=property
|title=Goal-driven Situation Awareness Process Based on Predictive Modeling
|pdfUrl=https://ceur-ws.org/Vol-3668/paper12.pdf
|volume=Vol-3668
|authors=Yevhen Burov
|dblpUrl=https://dblp.org/rec/conf/colins/Burov24
}}
==Goal-driven Situation Awareness Process Based on Predictive Modeling==
https://ceur-ws.org/Vol-3668/paper12.pdf
Goal-driven Situation Awareness Process Based on
Predictive Modeling
Yevhen Burov1
1 Lviv polytechnic National University, 12 Bandera str, Lviv,79013, Ukraine
Abstract
The increasing complexity of information systems and the inherent limitations of the human mind give
rise to the need to delegate the tasks of situation assessment to intelligent agents. Classical models of
situation awareness process imply sequential, event-driven treating of situation awareness. However,
the recent development in cognitive psychology suggests the central role of predictive, generative
modeling in human situational awareness process, which confers advantages when compared with
sequential process. This article proposes a goal-driven process and architecture of situational aware
intelligent agent based on generative, predictive conceptual modeling. The main advantage is the ability
to reuse the rich knowledge about previous experiences, which is constantly updated and kept logically
consistent. Similarly to human cognition, such approach allows to reconcile the use of patterns from
experience with the information coming from the environment and execution feedback resulting in the
updates of those patterns and learning. Compared to the BDI proposed agent architecture adds the
ability to dynamically react to the changes in the environment, prioritize those changes in the goal
system, reuse and modify beliefs as a consistent pattern system in the knowledge base.
Keywords
Situational awareness, intelligent agent, artificial intelligence, goal system, conceptual modeling1
1. Introduction
A highly dynamic world requires humans to quickly assess situations, adapt and use existing
knowledge, make decisions, and perform actions coming from those decisions. Situational
awareness, making predictions, building hypotheses, and checking them against available data
are important parts of human cognition.
However, the increasing intricacy of the modern world and the inherent limitations of human
mind in promptly and accurately making decisions in complex scenarios give rise to the need to
delegate the task of situation assessment and decision-making to systems of goal-driven
intelligent agents. The incorporation of situation awareness into such systems poses a
noteworthy challenge in the realm of artificial intelligence.
The major factors, contributing to this challenge are:
• The need to use both prediction and perception of the environment, with reasoning and
modeling the impact of possible actions.
• Being goal-driven while goals provide the agency and autonomy to intelligent agent, help
to select the most important goal to follow in the moment, and actions which are furthering
this goal.
• The need to implement focusing on the small part of the world, related to selected goal.
Shifting attention rather than doing an explicit query and selection of related knowledge.
• Using contextual knowledge.
• The fuzzy nature of knowledge, where the ontology concept can be represented by
multiple prototypes depending on the context. Prototypes are working as initial templates and
COLINS-2024: 8th International Conference on Computational Linguistics and Intelligent Systems, April 12–13, 2024,
Lviv, Ukraine
yevhen.v.burov@lpnu.ua (Y. Burov)
0000-0001-8653-1520 (Y. Burov)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
provide patterns and constraints. Although the mind constructs the actual knowledge model
on top of this.
• The dynamic nature of the environment requires constant updating and validating the
model.
The recent developments in the understanding of how human cognition works, using
predictive modeling, provide an important insight into the possible organization of situation-
aware and goal-driven agents. Existing models of situation – aware systems need to be updated
taking into consideration those developments.
This article proposes and discusses the updated model of situation-aware system for goal-
driven intelligent agent, centered around predictive conceptual modeling. It consists of
introduction, background research analysis, main part, discussion and conclusion. In the
background analysis part, the insights from cognitive psychology about situational awareness is
analyzed. Likewise, the current understanding of how goals are organized and processed
conceptually is discussed. The Belief-Desires-Intention framework as a classical approach to
model intelligent agents and its application to situational aware systems is described. The
background section is concluded with the analysis of situational aware system models.
In the main part of the article the architecture of goal-driven situation-aware intelligent agent
is presented. The specific functional modules organization and interaction is discussed in more
detail. In conclusion, the advantages of goal-driven and based on predictive modeling approach
is highlighted along with the directions for further research.
2. Background research
2.1. Cognitive science about predictive situation awareness and conceptual
modeling
The recent developments in the understanding of cognitive processes in the human mind could
bring a valuable insight into the area of artificial intelligent systems. After all, this process was
formed as a result of millennia of evolutionary process and represents the most efficient form of
cognition we know now. The notion of concept occupies the central place in our understanding
of cognition [1].
Concepts themselves are the result of grouping and finding regularities in the world done in
our mind. But once formed we use concepts to build predictive models of the world. Without
concepts we are experientially blind [2].
Concepts give meaning to the world and allow us to reason about it using the networks of
related concepts. Concepts don’t have a fixed meaning or properties. The meaning of concept
changes depending on the context and goal of the person using it. Prototype theory is used to
represent multiple meanings of concept as typical instances of this concept in different contexts.
[3,4]. The authors of [5] propose a method allowing to differentiate, retrieve and manage
meanings of concept in different contexts.
Concepts correspond to the object in the real world, but also to the imaginary, abstract objects.
The ability to manipulate abstract concepts is the powerful ability of the human mind, allowing
us to overcome the limitations of our working memory size or information processing speed.
Therefore, conceptual modeling as the ability to build, and reason with conceptual models is
an efficient way to represent and process information which holds promise also to be
implemented in systems with artificial intelligence.
Predictive modeling is a promising and quickly growing area in intelligent systems area. Thus,
the improved classification methods, based on predictive classification [6] or decomposition [7]
are developed.
According to the current understanding of cognitive psychology [8,9], the human brain
navigates and assesses the world using predictive modeling. Previously, the process of awareness
was often represented as linear, starting with perception of environment, the interpretation of
data readings, building the model based on those interpretations and available knowledge, and,
finally, making decisions and acting on them.
Predictive models are taken from the memory about previous experiences as a structure of
relevant patterns and dynamically re-constructed (updated) taking in consideration the current
goals and environment characteristics accessible via sensor data. Without previous experience
people are experientially blind and cannot understand the current situation.
The external data are perceived as a change in the world and this change is reconciled with
predictive model into coherent whole. In the process of such reconciliation model could be
changed. Such reconciliation happens on multiple levels of generality, starting from the most
abstract, general principles and going down into details.
The use of predictive modeling creates several advantages including faster reaction to changes
in environment and goals, the ability to reason and learn, constantly adapting the patterns to real-
world experiences. The mind constantly creates hypothesis about current context and checks
them against data coming from senses, other communication channels, and the results of
reasoning.
The insights from the current understanding of human cognitive situation awareness process,
centered around predictive modeling could be used to enhance the models of situational
awareness process in intelligent agents.
2.2. Goals and goal systems in human cognition
The implementation of goal-driven behavior is essential for intelligent agents, because
maintaining goal systems allows agents to be autonomous, flexibly prioritize their actions
depending on context. According to dictionary definition [9] goal is “The result or achievement
toward which effort is directed”. Such common definition excludes from the study of goal system
in cognition the negative goals – the projected states of the world which intelligent agent is
actively avoiding (such as injury, destruction, failure). Those kinds of states are structurally like
goals, they are included in goal setting and goal following processes on par with ordinary goals
but have negative motivation – instead of actively reaching the goal, intelligent agent plans and
acts to avoid those states.
In [10] is proposed another definition of goal, stressing the anticipatory nature of goal and its
influence on the behavior of agent. A goal is an “internal, mental representation that is
anticipatory and can take various formats, and it is used as a set-point in a control-system to drive
the external behavior of an agent for modifying the world”. A goal is not necessarily pursued. An
agent can set a goal and observe passively how it is fulfilled in the world. We will consider goals
as anticipated (desired or undesired) states of the world which intelligent agents strive to reach
(or avoid) through their actions.
In goal-related research authors differentiate between abstract and concrete goals. Abstract
goals are formulated generally, some of them could never be achieved. Abstract goals have no
specified plan of achievement. However, they influence the motivation to reach other, dependent
goals. Concrete, perceptual goals are focused on specific actions, often sensory-motor ones [8].
The author of [11] introduces the concept of abstract goals, which are not directly
accomplished by existing web services but involve decomposition into achievable goals and non-
deterministic choices by the user. It also presents the concept of Brokered Goals, which specify
achievable goals from a system perspective and serve as the link to semantic web-services
technology.
We can also differentiate between final goals, which are the terminal points of some kind of
project and proximate goals, which specify the intermediary states on the path to final goal
achievement. Intention [10] is defined as the next step to take on this path.
The usage of goals assumes the ability to check whether the specific goal was achieved and the
means to assess how far the current state is from the goal-state.
With each goal is associated the measure of motivation [12], specifying how important this
goal is in the current context. Motivation is used to select which goal should be acted on in the
current circumstances. Motivation is defined dynamically based on the current situation, other
goals, experiences, and the internal state of the agent. Motivation for a goal can be derived from
other, dependent goals.
Intelligent agents have many interrelated goals, forming goal systems [13]. The goal system
looks like an archipelago of goal structures. Some goals are context-dependent, and some
configurations are called out within contexts and situations. Other goals are abstract, long-
standing and are used in strategic planning, project selection, and following opportunities.
Goal systems are actively researched in psychology and computer science [14], aiming to
develop modeling frameworks for better understanding goal setting and goal-following
processes. In [15] a conceptual framework for goal-directed system is proposed.
Goals are often modeled with Ontologies. An example of such a model is [16]. The paper
demonstrates how goal modeling can be approached starting from a problem domain model
represented by an ontology. Ontological model and a goal model are used to represent
requirements and domain formalization.
Planning is also part of the goal processing process. It happens dynamically, taking in
consideration current context and goal system. Planning is based on previous knowledge about
following similar goals in similar circumstances. It builds an anticipated trajectory of states from
current state to final state through a sequence of intermediate states. The availability of perceived
trajectory from current state to the other, important state, influences motivation. For example, an
action could be perceived as harmful and avoided, if there’s a perceived sequence of states leading
from the results of this action to undesirable final state (such as injury).
Overall, the understanding of goal setting and processing process in human cognition provides
valuable insight into the organization of goal handling by intelligent agents.
2.3. Modeling goal-driven intelligent agents with Belief-Desire-Intention
framework
BDI (Belief-Desire-Intention) is the dominant framework in the modeling agency and
implementing intelligent agents. The BDI agent model has been the basis for research on
autonomous agents for the past 30 years. The BDI ecosystem is complex, with various agent
architectures, languages, and platforms developed [17].
The BDI model emphasizes the notion that an intelligent agent's behavior can be modeled by
examining how it processes information (beliefs), what it wants to achieve (desires), and how it
chooses to act (intents).
Beliefs refer to the agent's perception of the world, including its understanding of the current
situation, available information, and its own internal state. These beliefs are essentially the
agent's model of the environment it operates in.
Desires encompass the agent's goals, objectives, or preferences. They represent what the agent
wishes to achieve or accomplish in its environment. Desires are often described as a set of
possible states of the world that the agent finds favorable.
Intents are the agent's planned courses of action to achieve its desires based on its beliefs.
They represent the agent's decision on how to act in response to its current beliefs and desires.
Intents are the bridge between an agent's internal cognition and its external behavior.
A BDI agent program consists of initial beliefs and plan-rules specifying when a plan can be
used to achieve a goal or respond to changes in beliefs [18]. The execution of a BDI agent follows
a deliberation cycle that involves updating events, beliefs, and intentions, and executing plans.
Various formalisms are used to represent beliefs in BDI implementations. Symbolic models
such as models using propositional, first-order or modal logics are used to reasoning in well-
defined environments. [19, 20] They are computationally efficient and easy to implement but may
lack the ability to handle uncertainty. They can be too rigid and not well-suited for complex,
dynamic environments.
Probabilistic models, such as Bayesian networks or Markov decision processes are used when
the agent needs to reason under uncertainty or in stochastic environments. Beliefs are
represented as probabilities. They are excellent for reasoning under uncertainty but can be
computationally intensive. They may require a lot of data for accurate belief representation.
Apart from symbolic and probabilistic models, temporal models [21,22] are used. There are
also approaches using fuzzy logic, ontological models, situational analysis [23, 11].
The research in BDI framework introduces several kinds of desires (goals). Test goals are used
to check if a condition is true, and different agent programming languages have different
approaches to handling false test goals. Achievement goals can be procedural (goals to do) or
declarative (goals to be), and different agent programming languages support either procedural
or declarative goals. Procedural goals are independent of the agent's beliefs, while declarative
goals are related to the agent's beliefs [17]. Goals are often represented by simple terms or
conjunctions of positive literals, depending on the programming language. The work [18] propose
motives as extensions of BDI agent, expressing motives as an extension of goal concept. In [24]
the graded approach to estimate beliefs, goal in intentions is developed.
Plans in BDI modeling languages consist of steps such as goals, belief update operations, and
actions. Series-parallel interleaving allows plans to be built incrementally by sequentially and
parallelly composing other plans. Some languages provide finer control over the ordering of
steps, allowing them to be executed in any order or synchronized. Arbitrary interleaving allows
for more fine-grained ordering of steps than series-parallel interleaving. True concurrency in
some languages allows steps to be executed simultaneously, either interleaved or on different
processors.
In [25] plans are modeled using object -oriented approach as activity diagrams, which are later
translated into specific modeling language.
Research in BDI framework proposes a large number of developments, reflecting different
aspects of intelligent agents modeling and implementation. However, it lacks the implementation
of complex goal-systems, support of dynamic, contextual interplay of goals and beliefs. The BDI
framework could benefit from the implementation of insights coming from cognitive psychology
about human situational awareness and anticipated, predictive conceptual modeling.
2.4. Situation-aware systems modeling
According to the definition [26], situational awareness refers to the conscious comprehension
of the immediate surroundings and the ongoing events within it. The concept of situational
awareness encompasses the perception of the various elements present in the environment, the
understanding of their significance and interconnections, and the prediction of their future states.
The investigation of situational awareness falls within the broader domain of data fusion [27].
The phenomenon of situational awareness encompasses a range of operations, linked to
cognitive ability, including the discernment of relevant stimuli in the surrounding environment,
the identification and interpretation of patterns and objects, the recognition of familiar situations
based on past experiences, the process of logical decision making, and the subsequent execution
of those decisions, the evaluation of the success of actions taken, as well as the adjustment of
knowledge and procedures. The primary objective of a system that is situationally aware is to
make decisions and adjust the behavior of an intelligent agent in response to the dynamic
environment, in accordance with the agent's objectives. Situation-aware systems have been
recently introduced not only in autonomous driving but also in smart buildings and smart cities
[28].
Several models have been developed to represent the process of situational awareness. These
models can be categorized as process models, functional models, and formal models.
The early process models, such as John Boyd's Observe-Orient-Decide-Act (OODA) loop or the
Predict-Match-Extract-Search loop [29,30] were developed as a generalization of real-world
situational awareness processes in complex environments, such as the battlefield.
Functional models are exemplified by the Endsley model [31] and the JDL (Joint Directors of
Laboratories)/DFIG (Data fusion information group) models [32,33].
There are also investigations that explore different perspectives in the situational awareness
process using various formal frameworks, such as Category theory, generalized information
theory, interpreted systems, ontologies, and specification languages. However, the most widely
accepted framework for conceptualizing the situational awareness process is the functional
JDL/DFIG model. This model, like many other situational awareness process models, follows the
structure of human cognition process.
The JDL/DFIG model divides the process of situation awareness into five levels [33]:
• level 0. The assessment of signals/features. At this level, signals from various sensors are
gathered and interpreted as input data, representing attributes of measured entities. The
signals undergo processing, and errors in measured data are evaluated.
• level 1. The assessment of entities. The acquired data is interpreted as attributes of
entities from the ontology.
• level 2. The assessment of situations. The entities involved in the current context and their
relationships are analyzed in order to construct a model of the context and detect situations
within this context. This level operates with conceptual models of the environment, context,
and situations.
• level 3. The assessment of impact. Planning actions based on the detected situations,
making decisions, and analyzing the consequences of those decisions.
• level 4. The assessment of performance. Evaluating the correspondence between the
current situation and the goals of the system, analyzing performance, providing feedback to
lower levels of information fusion, and updating models.
The JDL/DFIG model is continually evolving and undergoing revisions. It is currently
recognized as a component of the data fusion process, which involves integrating multiple data
sources to generate more consistent, accurate, and useful information [34].
The classical models of situation-awareness process follow the paradigm of ‘smart camera’ –
feedforward perception model, implying that information is first read from the sensors, then
interpreted and built into the model of current context. In the next step this model is analyzed
and decisions pertaining to it are made.
However, the current understanding of human situation-aware processes stresses the primary
role of predictive modeling. Brain starts with building a model of context based on previous
knowledge and updates it taking into consideration sensor inputs.
Thus, there’s a need to update situation awareness process models according to the current
understanding of corresponding human processes.
3. The model of goal-driven situation aware system
3.1. Modeling system architecture
We will model situation-aware intelligent agent as a set of interrelated, dynamically constructed
conceptual models processed by corresponding functional modules. (fig. 1)
For the representation of those models, knowledge graph formalism [35] was chosen. not only
because it is commonly used in conceptual modeling, but also because of the common, conceptual
nature of all models used. Knowledge graph (KG) represents the local agent’s knowledge, stored
in local knowledge base. The vertices of this graph 𝑆𝑉𝑐𝑜𝑛 correspond to concepts in agent ontology
𝑂𝑛, and edges 𝑆𝐸𝑟𝑒𝑙 – to relationships between those concepts:
𝐺𝑟𝑘𝑛 = (𝑆𝑉𝑐𝑜𝑛 , 𝑆𝐸𝑟𝑒𝑙 ) (1)
To select part of knowledge graph relevant to current context we propose to use attentional
mechanism. Each vertex 𝑣𝑖 of 𝐺𝑟𝑘𝑛 has weight 𝑤𝑖 that is dynamically recalculated depending on
goals followed, situations detected, environment conditions available. Attentional mechanism
automatically selects interrelated concepts into conceptual model 𝐶𝑚𝑐𝑜𝑛 it works having weights
above certain threshold 𝑇ℎ, other concepts being ignored.
∀𝑣𝑖 ∈ 𝐶𝑚𝑐𝑜𝑛 : 𝑤𝑖 > 𝑇ℎ (2)
The weight assignment function 𝐹𝑤𝑡 assigns weights to the groups of related concepts
belonging to patterns 𝑃𝑡𝑖𝑛 used in current goal or task.
𝐹𝑤𝑡 : 𝑃𝑡𝑖𝑛 → ℝ (3)
The system has following functional modules (fig.1):
• context processing module (CPM);
• external data processing module (EDPM);
• goal system module (GSM);
• situation detection module (SDM);
• execution module (EM)
Each functional module works with the part of knowledge graph relevant to the task it is
performing. In this process it uses the knowledge patterns from knowledge base.
Figure1: Modeling system architecture
To avoid confusion, we will differentiate between the concepts of context and situation.
Context is understood as a task/goal/intent followed in the current moment in the specific
environment. While Situation is a possible condition/change/event which happens in the current
context and requires an action from intelligent agent. For example, context could describe a
person driving a car along the road. When a pedestrian starts crossing the street in the front of a
car a situation is detected and immediate reaction from the driver is required.
The central place in the situation-aware intelligent agent model is taken by Contextual model
𝐶𝑚𝑐𝑜𝑛 . Contextual models contain concepts and relationships relevant to the specific intent
followed in current environment. It is dynamically constructed from the current Environment
model, and intent, provided by Goal-system module using patterns of experience from the
knowledge base. Contextual model is constructed by CPM and contains a part of conceptual
model of environment 𝐶𝑚𝑒𝑛𝑣 𝑖𝑛𝑡
⊆ 𝐶𝑚𝑒𝑛𝑣 with the elements of environment relevant to the current
intent 𝐺𝑙𝑖𝑛𝑡 , part of knowledge graph containing concepts and relationships relevant to the
implementation of intent and deduced from patterns in knowledge base 𝐶𝑚𝑖𝑛𝑡 and intent itself:
𝐶𝑚𝑐𝑜𝑛 = (𝐶𝑚𝑒𝑛𝑣𝑖𝑛𝑡
, 𝐶𝑚𝑖𝑛𝑡 , 𝐺𝑙𝑖𝑛𝑡 ) (4)
Contextual model is passed from one moment of time to the next one. In this way, the system
continues to follow a goal from the previous moment, providing consistency and perseverance in
following goals. However, the CPM also updates the Contextual model and can switch from one
model to another if some unpredicted situation is detected or intention for the next moment
changed, as indicated by Goal-system module.
Thus, CPM, acting on the input from GSM, constructs the contextual model, based on available
knowledge from knowledge base. It fills in the missing data interacting with EDPM, and makes
decision related to the next action, which is passed to EM. Execution module interacts with
external services via corresponding APIs and sends feedback about the success/fail in the
execution of command.
Situation detection module analyses the contextual and the model of environment for the cues
about possible situations. The knowledge about such cues is taken from a knowledge base. In case
a situation requiring reaction from IA is detected, SDM updates the goal model in GSM, which will
influence the next goal processed.
Environment data processing module monitors the environment with sensors, interprets data
coming from them, updates the Environment model. The Environmental conceptual model is
reconciliated with Contextual model. For example, when a new object is detected by sensors, this
object is added with high weight to contextual model. The new configuration is processed by SDM
and GSM. If no important situations are detected or the change in the attended goals determined,
the new object is ignored.
Otherwise, the system could construct the new contextual model based on the new specified
goal.
On the other hand, EDPM follows the requests from CPM about getting additional information
from external data sources. CPM can also change policies of gathering data from sensors,
requiring more specific data, or prioritizing the collection of data for specific objects.
Goal system module maintains and processes the Goal models. It selects the goals relevant to
the current environment, assesses dynamically the weight of each, selects the most important
goal and builds the plans to reach it starting from the current context.
In the process of building the plan/intention it uses the knowledge of how similar goals were
followed in similar environments previously. Goal module maintains predictive models for each
followed goals- it can find the chains of intermediary goals, while following the more distant goal.
Goal module updates the contextual model, taking into consideration the environment objects
and relationships relevant to the most important goal and intent as a next step to reach this goal.
It also adds to the contextual model objects which are not present in the environment, but
important for following this goal/intent. Let’s consider each functional module and its
interactions with other modules.
3.2. Maintaining the system of goals and developing current moment’s intent
Goal system module maintains the set of goals and their relationships for intelligent agent. In
every time moment it selects the most important intent, which becomes a focus of attention and
passes it to the CPM, that constructs the contextual model to support the execution of this intent
in the current environment state.
Goal system is a set of goals with dependency relationships between them and motivation
function 𝐹𝑚𝑡 which computes the motivations for each goal:
𝑆𝑢𝐺𝑙 = (𝑆𝐺𝑙, 𝑆𝑅𝑒𝑔𝑙 , 𝐹𝑚𝑡 ) (5)
Goal is formalized as the state in the world, with associated assessment function 𝐹𝑒𝑣 and
weight 𝑤𝑔𝑙 . The state of the world in goal is described as conceptual model, including elements,
belonging to this state 𝐶𝑚𝑔𝑙 and their status, derived from their properties values and described
by a set of conditions 𝑆𝐶𝑑.
𝐺𝑙 = (𝐶𝑚𝑔𝑙 , 𝑆𝐶𝑑, 𝑤𝑔𝑙 , 𝐹𝑒𝑣 ) (6)
Assessment function 𝐹𝑒𝑣 allows to evaluate whether in the current state, represented by
Contextual model 𝐶𝑚𝑐𝑜𝑛 the goal is attained:
𝐹𝑒𝑣 : (𝐶𝑚𝑐𝑜𝑛 , 𝐶𝑚𝑔𝑙 , 𝑆𝐶𝑑) → (𝑡𝑟𝑢𝑒, 𝑓𝑎𝑙𝑠𝑒) (7)
The metric of distance between two states/goals 𝐹𝑑𝑠 : (𝐺𝑙𝑖 , 𝐺𝑙𝑗 ) → ℝ is also useful when
assessing the dependencies between goals.
With each goal and relationship weights are associated. The weight of the goal 𝑤𝑔𝑙 reflects its
importance and is used to calculate motivation to reach this goal.
𝑖𝑗
The weights of relationships 𝑤𝑟𝑙𝑔 reflect how closely dependent two goals 𝑔𝑙𝑖 and 𝑔𝑙𝑗 as states
of the world are. The dependency relationship can reflect causal, probabilistic or pragmatic
dependency between states. In this way proximate goals obtain their weight from the weight of
the final goal.
Goals and relationships weights are dynamically recalculated and normalized with every
change in the Environment model. The actual values of weights are assigned and adjusted in the
process of learning.
Goals have different sources of provenance. There are abstract goals, set initially. Those goals
cannot be reached, but they influence the weight (motivation) of other dependent goals. Abstract
goals could have positive or negative motivation. In a goal system they work as general principles
and values which influence the motivation for other, dependent goals. They can also reflect high-
end, strategic goals. The example of such goals: attain high status, be punctual, avoid injury. Other
goals come from external goal-setting sources or because of detecting important situations. Goals
are removed once they are reached or deemed unreachable or unimportant.
Related goals form structures and patterns. One of such structures is the chain of proximate
goals on the path to the final goal, representing a plan of reaching this goal.
𝑃𝑙(𝑔𝑖 ) = (𝑔𝑖1 , 𝑔𝑖2 , … , 𝑔𝑖𝑛 ) (8)
Plans are built using the knowledge of how similar goals were attained. However, in order to
accommodate for different states of environment, there are multiple different variants of plans
for each goal.
Another structure reflects the probabilistic relationship between two states, in which the
weight of relationship depends on the probability of following state given the initial state.
The weights of goals are recalculated and normalized in every time moment. In the first step
of selection all goals not related to the current Environment model obtain low weight. After that,
only goals which can be acted upon in the current Environment are left. After this, the goal with
maximum value of motivation function is selected. Motivation is calculated depending on goal
weights. If the selected goal has multiple proximate goals, leading to it, then the proximate goal
closest to the current state is chosen. Intention, as a next actionable state is derived from this
nearest proximate goal.
The goal system keeps track of the history of progressing towards the specific goal, which
helps to maintain perseverance and following up on the attained goals. GSM receives feedback
from the context processing module about the success/fail of intent execution and updates
accordingly plans and knowledge base.
3.2.1. Detecting situations in the current environment
Situation detection module functionally corresponds to the second level of DFIG model. It
analyses the Environment model for cues and patterns related to situations, which could happen
in current environment. For this it monitors the set of cues. Each cue is a condition (pattern) with
weight, reflecting its importance.
𝐶𝑢𝑒 = (𝐶𝑑𝑐𝑢𝑒 , 𝑤𝑐𝑢𝑒 ) (9)
Cues are ordered according to their weight. Cues leading to the situations with greater impact
have more weight. The impact is deduced from the knowledge base. If an important cue is
detected, SDM may ask the EDPM for additional diagnostics data, allowing it to confirm/decline
the presence of a situation. If an important situation is confirmed, SDM updates the goal system
with a new goal, having high weight and related to the reaction to this situation. GSM builds the
plan and forms the intention to fix the situation.
SDM, while analyzing the causal chains leading from the current state to possible negative
state in the future can predict the probability of this outcome and form a preventive intention to
avoid such negative outcome. Likewise, SDM can predict positive opportunity as a chain of states
starting from current state and having higher probability of realization. In this case it will also
create a new goal in the goal system.
Thus, SDM works as the analysis module looking for events with negative consequences and
for opportunities to further. SDM interacts with knowledge base to get information about cues
and situation patterns.
3.2.2. Monitoring the environment and getting information
External data processing module monitors the environment state via available sensors. It
detects the new objects in the environment and classifies them into specific classes, using local
ontology. Thus, the set of EDPM functions correspond to zero and first levels of DFIG model.
Additionally, EDPM issues requests and obtains the information from the external services,
processes and reconciliates the answers with agent’s ontology and knowledge base. Therefore,
external information services act as ‘on-demand’ sensors providing additional information for
SDM and CPM.
A new goal is coming from external source is also processed first by EDPM, which
conceptualize it using the local ontology concepts and relationships and passes to GSM.
EDPM maintains and modifies the Environment model, reflecting the objects present in the
environment and their characteristics. Environment model is used by other modules. CPM
constructs its Contextual model taking a part of Environment model and adding to it objects
relevant to current intent. SDM monitors the Environment model for cues pointing to possible
situations. Both CPM and SDM can issue requests for EDPM to get additional information from
the environment or external information services.
3.3. Implementing the intent, performing actions, and getting feedback
The Context processing module has a purpose to transform the intent obtained from GSM into
set of commands, passed to the Execution Module and implementing this intent. CPM maintains
the Contextual conceptual model with concept instances and relationships, relevant to the task of
following the stated intent in the current environment state. For this purpose, it copies the objects
relevant to current task/intent from the Environment model.
CPM uses knowledge base patterns for information on how similar tasks were executed in
similar environment state to construct the actual Contextual model. Alternatively, CPM can reuse
conceptual models from previous experience and reconcile them logically with current state of
Environment model.
The availability of models’ sequence from previous experiences allows us to prepare and use
models ahead of time. Contextual model, updated by applying patterns from knowledge base can
have additional elements, not present in Environment model.
CPM fills the gaps in knowledge by addressing EDPM for additional information coming from
sensors or external services. When contextual model has enough data, CPM makes decision about
the right practice to apply to implement the intent. CPM obtains data from knowledge base about
external services, commands and parameters needed to execute the commands implementing the
intent. Additionally, the knowledge about how to test the success/fail conditions using services
or Environment model is supplied.
CPM creates and sends commands to Execution Module, which selects and addresses
corresponding external services. EM constructs a local conceptual Command execution model,
containing knowledge about of how to execute command, the expected results, error conditions
and processing. The feedback about success or failure of command is sent back to CPM or tested
additionally following the changes in environment model.
4. Conclusions and discussion
The introduction of generative, predictive models of the world as a basis for intelligent agents’
situational awareness confers several advantages compared to sequential, reactive,
interpretational, and event-driven architectures. The main advantage is the ability to reuse the
rich knowledge about previous experiences, which is constantly updated and kept logically
consistent. Such knowledge typically comes from learning in the process of real-world task
execution, and not in the form of externally imposed rules. Moreover, similarly to human
cognition, such an approach allows to reconcile the use of the patterns from experience with the
information coming from the environment using execution feedback resulting in the updates of
those patterns.
Compared to the BDI proposed agent architecture adds the ability to dynamically react to the
changes in the environment, prioritize those changes in the goal system, reuse and modify beliefs
as a consistent pattern system in the knowledge base.
However, in order to implement the proposed model in practice several research problems
should be resolved including the construction of conceptual models from pattern hierarchies in
knowledge base and reconciliation of them with data coming from environment.
References
[1] Zhengyu Sha. “Concepts and Conceptual Representation,” 2022, 103–88.
https://doi.org/10.1007/978-981-19-8143-2_3.
[2] Barrett, L. F. “How Emotions Are Made: The Secret Life of the Brain,” 2017.
https://www.semanticscholar.org/paper/How-Emotions-Are-Made%3A-The-Secret-Life-
of-the-Brain-Barrett/b54f2fd56ec084aa8e1a3b05dd24d93ecd5c945a.
[3] Marika C. Inhoff, Laura A. Libby, Takao Noguchi, Bradley C. Love, Bradley C. Love, and Charan
Ranganath. “Dynamic Integration of Conceptual Information during Learning.” PLOS ONE 13,
no. 11 (2018). https://doi.org/10.1371/JOURNAL.PONE.0207357.
[4] Cuper, Alicja, and Małgorzata Cuper-Ferrigno. “A Few Remarks on Prototype Theory in
Cognitive Linguistics.” Language Culture Politics International Journal 1 (December 9, 2021):
57–67. https://doi.org/10.54515/lcp.2021.1.57-67.
[5] Burov, Yevhen, and Ihor Karpov. “Contextual Concept Meaning Alignment Based on
Prototype Theory.” In: Proceedings of COLINS-2023: 6th International Conference on
Computational Linguistics and Intelligent Systems, 2023.
[6] Tekouabou, S. C. K., Cherif, W., Toulni, H., Abdelaoui, E. A., & Silkan,H. (2022). Using Class
Membership based Approach to Improve PredictiveClassification in Customer Relationship
Management Systems. International Journal of Computing, 21(2), 242-250.
https://doi.org/10.47839/ijc.21.2.2593
[7] Mienye, I. D., Sun, Y., & Wang, Z. (2020). Improved Predictive Sparse Decomposition Method
with Densenet for Prediction of Lung Cancer. International Journal of Computing, 19(4), 533-
541. https://doi.org/10.47839/ijc.19.4.1986
[8] Clark, Andy. “Whatever next? Predictive Brains, Situated Agents, and the Future of Cognitive
Science.” Behavioral and Brain Sciences 36, no. 3 (2013): 181–204.
https://doi.org/10.1017/S0140525X12000477..
[9] Goal. URL: https://www.dictionary.com/browse/goal
[10] Castelfranchi, Cristiano. “Intentions in the Light of Goals.” Topoi 33, no. 1 (April 2014): 103–
16. https://doi.org/10.1007/s11245-013-9218-3.
[11] Gugliotta, Alessio, Stefan Dietze, and John Domingue. “A Situations & Goals Semantic Model
for Designing and Implementing Semantic Web Services-Based Processes.” Services
Computing. SCC ’08. IEEE International Conference 2 (2008): 581–82.
[12] Motivation: Definition, Types, Theories, and How to Find It - Verywell Mind.
URL:https://www.verywellmind.com/what-is-motivation-2795378.
[13] Kruglanski, Arie W, James Y Shah, Ayelet Fishbach, Ron Friedman, Woo Young Chun, and
David Sleeth-Keppler. “A Theory of Goal Systems.” The Motivated Mind, 2018, 207–50.
https://doi.org/10.4324/9781315175867-6.
[14] Vancouver, Jeffrey B, Justin M Weinhardt, and Aaron M Schmidt. “A Formal, Computational
Theory of Multiple-Goal Pursuit: Integrating Goal-Choice and Goal-Striving Processes.”
Journal of Applied Psychology 95, no. 6 (2010): 985. https://doi.org/10.1037/a0020628.
[15] Pezzulo, Giovanni, and Cristiano Castelfranchi. “Thinking as the Control of Imagination: A
Conceptual Framework for Goal-Directed Systems.” Psychological Research PRPF 73 (2009):
559–77. https://doi.org/10.1007/s00426-009-0237-z.
[16] Ribino, Patrizia, Massimo Cossentino, Carmelo Lodato, Salvatore Lopes, Luca Sabatucci, and
Valeria Seidita. “Ontology and Goal Model in Designing BDI Multi-Agent Systems.” WOA@ AI*
IA 1099 (2013): 66–72.
[17] Silva, Lavindra De, Felipe Meneguzzi, and Brian Logan. “BDI Agent Architectures: A Survey.”
In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence,
4914–21. Yokohama, Japan: International Joint Conferences on Artificial Intelligence
Organization, 2020. https://doi.org/10.24963/ijcai.2020/684.
[18] Saadi, Adel. “A Further Exploration of the Natural Incorporation of Motives in BDI
Architectures,” 1–7. IEEE, 2021.
[19] Aiguier, Marc, Isabelle Bloch, Salim Nibouche, and Ramon Pino Perez. “Morpho-Logic from a
Topos Perspective: Application to Symbolic AI.” arXiv Preprint arXiv:2303.04895, 2023.
[20] Su, Yun, Yongqiang Dai, and Xiaohong Li. “Logical Model and Verification of Emotion Triggers
for BDI Agents,” 1135–40. IEEE, 2019.
[21] La Torre, Salvatore, and Gennaro Parlato. On the Model-Checking of Branching-Time
Temporal Logic with BDI Modalities, in: Proceedings of AAMAS. 681–89, 2020.
[22] Alzetta, Francesco, Paolo Giorgini, Mauro Marinoni, and Davide Calvaresi. “RT-BDI: A Real-
Time BDI Model.” edited by Yves Demazeau, Tom Holvoet, Juan M. Corchado, and Stefania
Costantini, 12092:16–29. Lecture Notes in Computer Science. Cham: Springer International
Publishing, 2020. https://doi.org/10.1007/978-3-030-49778-1_2.
[23] Buford, John, Gabriel Jakobson, and Lundy Lewis. “Extending BDI Multi-Agent Systems with
Situation Management,” 1–7. IEEE, 2006.
[24] Dunin-Kȩplicz , Linh Anh , Andrzej Szałas “A Framework for Graded Beliefs, Goals and
Intentions,” 2010. https://doi.org/10.3233/FI-2010-263.
[25] Caillou, Philippe, Benoit Gaudou, Arnaud Grignard, Chi Quang Truong, and Patrick
Taillandier. “A Simple-to-Use BDI Architecture for Agent-Based Modeling and Simulation,”
15–28. Springer, 2017.
[26] APA dictionary of psychology, Situational awareness, 2022. URL:
https://dictionary.apa.org/situation-awareness
[27] J. Bleiholder, F. Naumann. Data fusion. ACM computing surveys (CSUR) 41, no. 1 (2009): 1-
41
[28] Grigore Stamatescu, Ioana Făgărăşan, and Anatoly Sachenko, “Sensing and Data-Driven
Control for Smart Building and Smart City Systems,” Journal of Sensors, vol. 2019, Article ID
4528034, 3 pages, 2019. https://doi.org/10.1155/2019/4528034.
[29] C. Richards, C. Boyd’s OODA loop. Richards, 2020.
[30] E. Shahbazian, D. Blodgett., P. Labbé, The extended OODA model for data fusion systems.
Fusion01. (2001)
[31] M C Wright, , J M Taekman, M R Endsley. “Objective Measures of Situation Awareness in a
Simulated Medical Environment.” Quality & Safety in Health Care 13 Suppl 1 (2004) 65–71.
[32] A.N Steinberg. C.L. Bowman, Revisions to the JDL data fusion model. In: Handbook of
multisensor data fusion, CRC Press, 2008, pp. 65-88.
[33] E. Blasch, “One decade of the Data Fusion Information Group (DFIG) model.” Commercial +
Scientific Sensing and Imaging (2015)
[34] Foo, Pek Hui, and Gee Wah Ng. "High-level information fusion: An overview." J. Adv. Inf.
Fusion 8, no. 1 (2013): 33-72.
[35] Ehrlinger, Lisa, and Wolfram Wöß. “Towards a Definition of Knowledge Graphs.” SEMANTiCS
(Posters, Demos, SuCCESS) 48, no. 1–4 (2016)