In this paper we describe our work on computational mind model for temperamental decision algorithms using Fuzzy Logic and an implementation of an emotional-behavioral multi-agent system for analysis and evaluation of different strategies based on temperamental behaviors. We describe our approach to emotional model using temperamental decision system based on theory about general types of superior nervous systems in humans and animals and we explain how we can apply Fuzzy Logic on temperamental decision system. We describe the simulation environment used in this work to test and evaluate the strategies. We have conducted a set of robotic experiments in order to test the performance of the system on its first implementation phase. The results achieved showed that the different set of temperamental characteristics influences significantly the performance of the agents and give us very positive feedback to proceed with this research and implementation in new directions.
The study of emotions on a computational perspective has called the attention of the researcher for some years. Different projects are conducted in order to create an agent whose internal structure and behaviour is inspired by ideas of emotions. There are many applications for emotional machines: education, health care, rescue, entertainment and other areas.
In psychology, emotion is often defined as a psychological state or process that functions in
the management of goals and needs of an individual [
In this work we approach the problem of building autonomous robots capable of interaction
with each other and build strategies based on a temperamental decision system. As described in
Pavlov's theory [
We will focus our work on temperament-based agents and in order to categorize the emotional
feedback we use simplified Mehrabian PAD model [
The temperamental agents were implemented using Visual Basic 6.0 and tested using
CiberMouse [
The paper is organized as follows. Section 2 presents the Temperament approach based on Pavlov's Theory in order to explain the fundamentals for the implementation performed for this project, and we present the Eysenck temperamental scale. Section 3 describes briefly the simulated environment and the changes introduced into Ciber-Mouse robotic simulator. Section 4 describes some experimental results. Section 5 describes the implementation of temperamental model using Fuzzy Logic and Mehrabian PAD simplified model. Section 6 describes the evaluation experiences we perform for this project. Finally Section 7 presents the conclusions and further work.
Since remote antiquity scientists notice different behaviours between people which were a very individual and specific approach. Some people are very mobile, emotionally excited and energetic. Others are sluggish, calm and imperturbable. Some are sociable and easily contact with others. Some are cheerful, others locked and reserved.
Temperament is a specific feature of Man, which determines the dynamics of his mental activity and behaviour. Two basic indexes of the dynamics of mental processes and behaviours at present are distinguishable: activity and emotionality.
Activity is expressed in different degrees of tendency actively to act, to appear in the diverse activity. It is possible to note two extremes: from one side, high energy, fervency and swiftness in the mental activity, the motions and the speech, while with another - passiveness, sluggishness, the apathy of mental activity, motion and speech. The second index of dynamicity is expressed in different degrees of emotional excitability, in the velocity of appearance and the force of the emotions of man, and in the emotional sensitiveness (receptivity to the emotional actions). Four basic forms of temperament may be distinguished, which were named as follows: sanguine (living), phlegmatic (slow, calm), choleric (energetic, passionate) and melancholic (locked, inclined to the deep experiences).
The definite scientific explanation of
temperaments was given by Ivan Pavlov's theory
about the types of higher nervous activity. Pavlov
opened three properties of the processes of
excitation and braking[
The combinations of the properties of nervous processes indicated were assumed as basis to determinations of the type of higher nervous activity. Depending on the combination of force, mobility and steadiness of the processes of excitation and braking four basic types of higher nervous activity are distinguished.
Pavlov correlated the types of nervous systems with the psychological types of temperaments isolated with it and revealed their complete similarity. Thus, temperament is a manifestation of the type of nervous system into the activity, the behaviour of man. As a result the relationship of the types of nervous system and temperaments appears as follows: 1) strong, balanced, mobile type - sanguine temperament; 2) strong, balanced, inert type - phlegmatic temperament; 3) strong, unbalanced, with the predominance of excitation - choleric temperament; 4) weak type - melancholic temperament.
Using the methodology of Eysenck [
(fig. 2).
The horizontal scale (from 0 to 24) is the scale of emotional receptivity. It characterizes the level of the sociability of man: ● 2 or less - deep introvert, extremely
unsociable and locked person ● 2 - 10 - introvert, unsociable and locked
person ● 11 - 13 – an average level of sociability ● 14 or more - extrovert, sociable person The vertical scale - scale of neurotic (anxiety), characterize the emotional stability or instability of human psyche: Fig. 2. Scale for the determination of the temperament, by ● 11 - 13 - personality is moderately steady Eysenck
emotionally ● 10 and less - emotionally unstable personality, it is always disturbed ● 14 and more - emotionally steady person up to emotional coldness
Ciber-Mouse is a modality included in the Micro-Mouse competition organized by Aveiro
University (Portugal), and running since 2001 with every year periodicity. This modality is directed to
teams interested in the algorithmic issues and software control of mobile autonomous robots. This
modality is supported by a software environment, which simulates both robots and a labyrinth [
The simulation system possesses a distributed architecture where some types of applications
communicate among each other,
nominated, a simulator, an application
for each agent and a viewer application
(fig. 3). The architecture is client-server,
where the simulator acts as the server
and both the agents and the viewer, acts
as clients. This architecture is similar to
the Simulation League of RoboCup
[
The simulator shapes all the components of the robots hardware and the labyrinth. The simulation is executed Fig. 3. Cyber-Mouse simulation architecture in discrete time, cycle by cycle. In the beginning of each cycle of simulation the simulator sends to all robotic agents in test, the measures of its sensors, and to all viewers the positions and robots information. The agents can answer with the power values to apply to the engines that command the wheels.
All robots in test have the same physical characteristics. All have the same sensors and the same engines.
a) Each robot (fig. 4) is equipped with the following sensors [
Beacon sensor that indicates which is the difference between robots direction and the beacon direction. ● Ground sensor, active when robot enters in the arrival zone. ● Compass sensor, that allows robot to know which its absolute orientation in the labyrinth is. ● Collision sensor, asset in the case of robot collision. ● Vision sensor, works by identifying other robots and their emotional state.
The measures of the sensors include some noise added for the shape simulator in order to simulate real sensors. b) In order to detect the beginning of the test and possible interruptions each robot has 2 buttons: ● Start, active when is initiated
the test. ● Stop, active when a test
interruption exists. c) In terms of virtual engines robots is constituted by: ● 2 wheels for 2 independent motors, one on the left and one on the right; ● LED of finishing, to light
when reached the arrival zone. Fig. 4. Virtual robot diagram In each cycle of simulation the agents receive the values measured by all its sensors and must decide which power to apply in each motor. The perception that a robotic agent has from the exterior environment is limited and noisy transforming him into the most appropriate tool to perform our work with almost realistic precision.
We assume a two layer architecture for our emotional model. One layer is physical and describe superior Nervous system from the Pavlov perspective. The other layer is psychical and work with appraisal model created by Mehrabian.
In order to perform evaluation of agent needs, motivations and stimulus we will create appraisal bank
[
We choose Ciber-Mouse Simulation environment to implement and test our model because it offers an open, modular and flexible platform permitting unlimited applications and fully configurable simulation system. It permits to add new sensors and functionalities to the robots, has fast performance and is adequate to test algorithmic models and their performance.
Fuzzy logic was first developed by Zadeh [
As we show on previous chapter, the Pavlov's theory define the temperamental model based on characteristics of superior nervous system, but at the same time there are no pure temperamental type in nature, but there are mixtures of different properties which characterize one or another unique temperamental type. So, as we see, one person can have all temperamental type in different ratio. The different proportion of values: force, mobility and steadiness of processes of excitation and braking define the unique temperamental type for each person. Based on this uncertainty we use Fuzzy Logic for describe and monitorize the temperamental types in our project. Lets analyse most important variables for our model and their fuzzy interpretation.
In our multi-agent system the force of excitation and braking processes is represented by the force of the motor and reach of the sensors. In the begin of the simulation we generate the variables values to determine the unique combination which will represent some temperamental type. Using fuzzy logic we can determinate which temperament we obtain for the agent. We define superior limit for the force value in order to obtain better simulation of real world.
Mobility of the agent is represented by its “persistence” to reach the goal and avoid negative emotions. For instance if some agent is “comfortable” in some place, and his mobility is low, he will not look to move to search other places. He will stop his motors and just stay in the same place until his emotional state change and force him to move. At the same time, one agent who have high mobility will search new places and new directories even if he is comfortable enough in some temporal phase.
The steadiness of the agent is the velocity of his emotional state variation. For example, more balanced agents have slow variation of emotional state. For this we introduce the variable called Anxiety which is used for increase or decrease the Pleasure variable. The value of Anxiety depends on the temperament of the agent. We choose the values for anxiety based on the Eysenck test.
This variables were based on Eysenck test described on second section. The Melancholic and Phlegmatic temperamental types are included in Introverts group and Sanguine and Choleric types are included in Extroverts group. We will evaluate they performance to reach the beacon, conditioned by they temperamental needs. Each agent possesses the property of Pleasure, Arousal and Dominance which describes its state in the temporal line. For instance the Pleasure depends of the proximity of the agent with other agents, and follows the next strategy:
For Extrovert agent Pleasure increase if he is near of other agent with high level of Pleasure. This increase is not a constant and depends on the value of Pleasure of the nearby agent. If the Extrovert agent is near an agent with low (negative) Pleasure, his own Pleasure will decrease slowly. If the Extrovert agent is far from any agent in the space, his Pleasure decrease constantly. Similar rules were implemented for Introvert agents, but with opposite effects. For Arousal and Dominance we implement different algorithms to evaluate the best strategy of emotional programming.
Analysis of emotional states leads to the conclusion that the human emotions such as anger, fear, depression, elation, etc. are discrete and we need to define some kind of measures to have a basic framework to describe each emotional state using the same scale. After studing the appraisal theory we find Mehrabian model more suitable for computational needs since it defines three dimensions to describe each emotional state and provides an extensive list of emotional labels for points in the PAD space (Fig 9) gives an impression of the emotional meaning of combinations of Pleasure, Arousal and Dominance (PAD).
In the Analysis of Big-Five Personality factors in terms of PAD temperamental model [
As we already refer, each temperamental type have it own regression functions describing the dependence between the PAD values and the final emotional state of the agent (6), (7) and (8). Using these regressions we can define the psychical temperament layer for our model. The influence of each PAD component to the temperamental type is shown in (1)-(5) formulas, so using them we can distinct the different weight of the measures from Appraisal Bank and personalize them for each temperamental type. Lets define and describe the Appraisal Bank we use for our project.
Appraisal bank defines needs, motivations and stimulus of the agent as a set of subjective
measures, called appraisal dimensions. We based our idea on Broekens and DeGroot [
Needs: reach the beacon, satisfies personal (temperamental) characteristics like necessity of company of other agents or necessity of loneliness, avoid threats (angry agents).
Motivations and stimulus: See or lose the goals or threats.
We describe this NMS system in order of PAD model. We define pleasure as conductance of the goal. For instance if the agent see the beacon and no obstacle are present his pleasure is high, while if he see the threat or lose the goal is highly unpleasant. Arousal is the amount of attention each event need, for instance avoid threat need the attention of the agent and lose it need no attention. Dominance is a measure that defines the amount of freedom of the agent. For example, see the wall decrease the dominance and see the no obstructed way to goal increase the dominance. The weight we attribute to each event differ on their importance and thus we divided them in three groups of importance: high, medium and low. We will define the importance of each events on simulation system, but each temperamental type has its own “value scale” for each need. We will define the “personality functions” which transform the importance of each event from appraisal bank in it personal importance for each agent depending on the agent temperamental type.
See_Beacon Near_Beacon Lose_Beacon
See_wall
Collide_wall See_happy_agent Near_happy_agent Lose_happy_agent
See_threat Near_threat Lose_threat
So we define as very important agent’s surviveness, which approach avoiding the threats. Medium importance is the performance of the agent in relation of the final goal of the simulation: reach the beacon. Low importance is the satisfaction of the temperamental needs of each agent: looks for a company or for isolation.
On the Fig. 10 we represent the diagram which describes the relationship between the Simulation environment, Decision layer and Data layer. Physical Bank contains the fuzzy measures of force, mobility and steadiness. Values for motors and sensors are archived in Physical Bank for each temperamental type. This diagram presents a scheme for two temperaments and three agents, but as we already explain we use four temperaments for physical layer and 5 temperaments for psychical layer.
Lets analyze the relationship between the central nervous system defined temperaments and
PAD dimensions using the information from Big-Five personality factors and Eysenck [
In this work we evaluate the group of 9 agents and their performance to reach the goal. In each evaluation we use different combination of temperamental configurations.
There are different temperamental types of agents and their performance that we can observe on table 2 which represents means of some measures of the Physical temperamental type and Arousal measures.
Temperament vs Emotional State
Column C 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4
Temperament 1-Sanguine, 2- Choleric, 3- Phlegmatic and 4- Melancholic.
As we can observe from this table Melancholic temperament has better performance than other Physical temperamental types. It achieves better time performance on reaching the goal and better emotional state. On Fig. 13 we can observe the emotional state growing when the temperament changes. We also want to show some curious result (Fig. 14) of the test in the empty space. Here we can see pure agent interaction without obstacles and free access to the beacon. As we see the agents with high arousal level come very closely to the beacon as well as extravert agents (rounded by the red circle) form the cluster near the beacon. In the example shown which is very usual to happen in the open simulation space we observe the optimal emotional values for each agent in accordance with each temperamental type.
In this paper we have addressed the problem of multi-layer scalable emotional model which can be easily introduced in any non-emotional system. We presented the appraisal bank which can be easily adapted to most systems. We find that the runtime-scaling is very useful to describe the system in such uncertain area as emotional interaction of different agent with non-homogeneous characteristics and responses.
We compare the results of these evaluations with our previous results [
This work was partially supported by the Portuguese National Science and Technology Fundation, Project FCT/POSC/EIA/57671/2004.