In this research, the different reactions of prey and predator birds are explored, considering the specific characteristics of each one and its environment. This work has its beginnings in an experiment that was carried out with various types of pets (bird, turtle and dog) [1] , where the foundations of emotions are laid as a consequence of the degree of consciousness that they can have according to their objectives and the stimuli coming from different environments. The above applies the five axioms of consciousness of Igor Alexander [2] .

The software is based on the artificial intelligence stream, where the Multi-Agent Systems (MAS) paradigm is used to simulate the interaction between birds. The above is to achieve intelligent behavior coordinated between a collection of autonomous agents. The latter coordinate their knowledge, objectives, skills, and plans to execute an action or solve a problem [3] - [5] related to the interaction of birds with their environment, including within this, other birds of their species and other species. The bird flight behavior algorithm is based on the flocking algorithm used in bird simulation, which was enriched by including emotions and different roles in bird behavior [6] .

This project focuses on exploring fields of study such as ethology and cognitive psychology, synthetic emotions, and MAS applied to the interaction between birds: prey and predators [6] [7] .

According to [8] , all behavior has motivation as its main driving force and this originates from various causes; one of them is emotions and these are the ones that determine processes as important as decision-making to show rational behaviors.

This research aims to provide an argument about the different circumstances and decisions that allow for better performance, according to the objectives of each subject.

To achieve this, the theory related to the cognitive structure of emotions is used as a theoretical framework, specifically the theory developed by Ortony, Clore, and Collins [9] , known as the OCC Theory. This theory allows us to formalize the algorithms related to the emotions that intervene in a behavior that interacts with that of other organisms.

Given that affects are the cognitive representation of emotions and are underlain by substantial information that is considered in the decision-making process. It is important to consider it to represent the behavior of any animated organism.

A simulation is a virtual representation of a real-world system or process using software and mathematical models. Its main objective is to imitate the behavior and interactions of the elements of the real system in order to study, understand or predict its operation in different situations.

Computer simulations are used to study the dynamic behavior of objects or systems in response to conditions that cannot be easily or safely applied in real life. Simulations are beneficial for allowing observers to measure and predict. This leads us to ask how the functioning of an entire system might be affected by altering individual components of this system [10] .

Within this project, simulation not only allows the study of individual behavior of prey and predator birds, but also facilitates the observation of how these interactions are affected by factors such as the availability of food, shelters, and changes in the environment. Furthermore, using OCC Theory, it is possible to analyze how emotions influence the decisions and actions of the simulated birds.

From an ethological perspective, the behavior of birds of prey [11] has a series of strategies to survive in environments where they constantly face the threat of predators. Some key aspects of their ethology include:

Ethology is a science based on the observation of animal behavior to discern the objectives of their different behaviors in the environments. According to Charles Darwin “the mental difference between man and the higher animals, though great, is certainly one of scale and not of kind” [12] . Based on the above, it is considered that cognition focuses on acquiring knowledge and intelligence on the ability to apply it successfully.

According to [12] , it is undeniable that humans are animals, so when we study the behaviors of other animals, we are not comparing separate intelligence but rather considering variation within a single category.

Based on the above points, the simulation experiment is based on considering bird cognition as a variant of animal cognition, which of course, includes human beings. From an ethological point of view, predatory birds [13] exhibit a unique set of strategies that allow them to capture prey and ensure their survival. Some important aspects of their ethology include:

The OCC theory [9] is a cognitive psychology theory that focuses on understanding and studying emotions and how they affect decision-making and perception. It was developed by two cognitive psychologists and a cognitive science computer scientist Andrew Ortony, Gerald Clore, and Allan Collins in the 1980s. Figure 1 graphically summarizes the OCC Theory. For more information on the OCC Theory, consult the references included in this article.

The assessment structure for decision making according to OCC Theory is made up of the following elements:

1) Macrostructure of assessment of the knowledge representation system.

2) Central/global variables that allow giving value to the logical object (events, agents, or objects).

The central variables of intensity (reactions) of the OCC theory are the following:

A) Desirability: the intensity of emotions about events correlates with the degree of desirability.

B) Plausibility (to abide or to violate): plausibility depends on the agent’s effort or lack of effort (control of action, responsibility) to comply with or violate these norms, and may produce the actions listed below.

C) Ability to attract: the ability to attract does not imply an understanding of its meaning (contrary to desirability, which considers consequences). This is why it is significant in emotions with low cognitive content.

The global variables used in OCC theory are:

A) Sense of reality: the degree to which the event, agent, or object underlying the affective reaction appears real to the person experiencing the emotion.

B) Proximity: attempts to reflect the psychological proximity of the event, agent or object that induces the emotion.

C) Unexpected quality: affects the control that one has over the environment.

D) Excitement: the previously existing level affects the intensity of the emotions.

Three types of goals are considered within this theory:

Active pursuit goals

Goals of interest

Fill goals

Distributed artificial intelligence (DAI) is used for distributed problem solving. It consists of dividing modules that cooperate, and the knowledge about the problem is separated and/or shared between them [14] - [16] .

Hence, intelligent behavior is achieved through coordinating their knowledge, objectives, skills, and plans to execute an action or complete a task.

Depending on the different forms of communication between agents, we have systems:

1) Strongly coupled, where communication takes place through a shared memory.

2) Loosely coupled, where communication is through message passing.

In this type of system, three factors influence the choice of a particular organization: (1) the complexity of the problem that involves the number of agents, (2) the imprecision and uncertainty, (3) the eventual existence of a decomposition of the problem. In the case study, it was decided to use DAI given the complexity of the problem since it demands local points of view, which allow for a rapid transition between perception and action.

Design based on a local view is a promising approach to solving complex problems. It is easy to obtain results by avoiding the difficulties of trying to solve a problem, that is, results emerge as a result of local interactions. Systems must be able to adapt to dynamic environments. In the context of a Multi-Agent system, it is assumed that reasoning is always local due to the inherent distribution. This allows agents to appear and disappear during execution.

The design of these architectures is influenced by the behavioral psychology theory Brooks, Chapman and Agree, Kelabling, Maes, Ferber, Arkin mentioned in [15] . This class of agents is also known as: (1) behavior-based, (2) located (immersive), and (3) reactive.

The dynamics of action-selection for this type of system emerges based on two basic aspects: (1) the conditions of the environment, and (2) the internal objectives of each agent. Its main characteristics involve dynamic interaction with the environment, and internal mechanisms that allow working with limited resources and incomplete information hence an intelligent agent can exhibit the following behaviors:

According to [16] - [19] , a Multi-Agent system contains several agents with the following characteristics:

The project consists of three essential modules that are described below and their operation is summarized in Figure 2 .

Capture module: this module is responsible for obtaining the simulation parameters that will be entered by the user, such as the number of birds, the environmental conditions and the type of prey or predator birds present in the simulation.

Representation tools module: this module is responsible for modeling the interaction of prey and predator birds in the environment that surrounds them. OCC theory is used to represent the decision-making of birds regarding their behavior based on the information perceived in the capture module. It is also responsible for defining and executing the specific behaviors of the agents in response to the decisions made using the flocking algorithm.

Simulations module: its main function is to graphically show, through simulations, the interaction of prey and predator birds based on the model established in the representation tools module.

According to [20] , a cognitive model comprises the point of view of the animal behavior in question within its environment and is limited to a specific activity. Hence, the formality of mental models to be able to recreate reactive behavior through precise algorithms. Mental models allow us to represent the behaviors we want to incorporate in the different systems involving artificial intelligence. In the case study, we refer to the behaviors of birds in their two roles as predator and prey.

In this work, a mental model corresponds to a certain behavior that will come into action when a stimulus is presented, allowing it to interact with the dynamic and random environment on any given day. These mental models are algorithms represented through control structures and first-order logic.

In some cognitive areas, it is possible to formulate theories of competence, which specify what has to be calculated, when, and why. Later, based on these theories, develop an algorithm that represents it. This area of study is known as the theory of competence and is carried out based on mental models.

In this project, a Multi-Agent structure is implemented that effectively represents a dynamic virtual ecosystem. This system includes multiple agents represented in each of the roles: prey-predator birds, food and shelters. Through the interaction of these agents, a precise and detailed representation of the behaviors and relationships between the different entities in the simulated ecosystem is achieved.

Prey birds: birds of prey seek to survive in the simulated environment. They have behaviors such as searching for food, shelter, and flocks to increase their probability of survival. In addition, they are programmed to detect the presence of predatory birds and take evasive measures when they are in danger. They are represented by the following agents:

Predator birds: they are the hunters of the ecosystem. Their main objective is to search for and hunt pray birds. Their behavior is based on the detection of prey, hunting and competition with other predatory birds for available resources. They are represented by the following agents:

As for the environment, this is represented by food; these are essential resources for the survival of birds of prey. Refuges are safe areas where prey birds can hide from predators; they offer a place for birds to protect themselves and recover from risky situations.

The Multi-Agent structure allows for dynamic interaction between these agents. Prey birds seek food and shelter, while predatory birds hunt and compete with each other.

Prey birds may also seek the safety of shelters and form flocks to increase their collective protection.

Each agent follows a set of rules and behaviors defined according to its function to show coherent behavior of birds in the ecosystem. That is, predatory birds may follow hunting strategies, while prey birds may have evasion patterns in response to the presence of predators. They are reactive agents interacting with a dynamic environment.

The main menu is shown below, where the initial parameters for performing the simulations will be obtained ( Figure 10 ).

This menu uses two sliding scales, better known in the computer field by their term in English sliders, to determine how many birds of each type will be initially found in the simulation. Where there can be a maximum of 100 prey birds and a maximum of 5 predatory birds. It also has three drop-down lists of options, better known in the computer field as combo boxes, where the parameters are determined: a) climate (sunny-cloudy), b) number of shelters: none, few (from 1 … 4), normal (from 5 … 8) or many (from 9 … 12), and c) amount of food that will exist at the beginning of the simulation: none, little (from 20 … 30), normal (from 31 … 40) or a lot (from 41 … 60). These will be displayed as a value is selected for each parameter, at the end a button will be displayed to start the simulation ( Figure 11 ).

The following images are used to simulate the flight of predatory birds ( Figure 12 ).

The following arrangement of images is used to simulate the flight of birds of prey ( Figure 13 ).

Shelters, food and rain were represented in the program with the images shown in Figure 14 .

During the execution of the program, one can have an environment with a sunny day, or a rainy day as shown in Figure 15 and Figure 16 , respectively.

While it is raining, predatory birds remain wandering around, while prey seek to feel safe, either by staying in a flock or by seeking shelter, from which they will leave to return to their natural behavior once the rain has stopped, as shown in Figure 17 .

Certain statistics for predator birds can be displayed, such as their food level, speed, whether or not they are competing, and whether they are hunting or roaming, as shown in Figure 18 .

When starting the program, a box is also displayed in the upper right corner that indicates general information about the simulation, such as the number of prey birds that have been hunted by predators, how many have starved to death, or how many predators have starved to death as shown in Figure 19 .

It is also possible to check the emotions felt by both prey and predatory birds at any time with the left mouse button, as shown in Figure 20 and Figure 21 , respectively.

During the simulation development, we encountered several challenges that required ingenious solutions. One of the initial obstacles was the design of the classes and objects that would represent the birds in the simulation. To solve this, we carried out an exhaustive class design, defining the properties and methods needed for each one; which corresponds to the multi-agent architecture that represents the different elements (agents) that make up the behavior of prey and predator birds; as well as their environment.

Interactions between birds also presented a challenge, as we had to manage how prey birds responded to the presence of predators or the weather, and how predators hunted or competed when they were close to each other. Establishing clear rules for these interactions turned out to be essential.

Another problem that arose was the logic behind knowing when behaviors such as searching for food or shelter should occur. To do this, we relied on our mental models and affective-cognitive structures to determine which emotions led to the execution of these behaviors.

Performance also became a concern as the simulation grew in complexity and size. To optimize it, we applied techniques such as eliminating unnecessary calculations and using efficient data structures.

This program could be used as a basis in the future to be explored with circumstances that were not considered for this specific program. Examples could be: bird reproduction, different ecosystems and climates, specific bird species, etc.

The behavior of birds of prey and predators was successfully represented in an environment with different characteristics using various graphical tools to demonstrate the results of the simulation.

Managing the interaction between prey and predator birds has required the definition of clear rules to achieve realistic and predictable behaviors. This involved the implementation of detection and decision-making systems that contributed to the authenticity of the simulation.

The simulation of the behavior of birds of prey and predators represents a significant achievement in the creation of a realistic and functional virtual environment that can be applied in various contexts for studying and analyzing bird behavior in their natural environment.

This work represents the research conducted by André Navarro-Bárcenas and Ricardo Céspedes-Valle to obtain a Bachelor’s degree in Computer Engineering from Universidad Autónoma Metropolitana-Azcapotzalco. It is also part of the divisional project Design of intelligent interfaces for simulating the behavior of living or animate organisms, from the same University.