2. coalmine environment, a virtual miner agent can direct
trainees to practice and experience underground
situations, activities and processes without putting them
in any actual danger and further improve trainees’ risk
awareness. Moreover, some emergent situations, which
have not previously occurred but could be encountered at
a mining site, can also be reproduced. However, most
current coalmine virtual environments hardly take into
account the complex behaviour mechanism of humanmachine-environment related risk factors in underground
coalmine accidents [1-2].
Multi-agent technology has been widely applied in
problem solving, virtual environments, collective robotics
and many other areas [3]. In a multi-agent virtual
environment, agents can autonomously sense the
environment and reason about their goals. By defining
the interactive rules among individuals, agents can fully
cooperate with each other to create the complex
mechanism of a dynamics system. Therefore, multi-agent
technologies are particularly suitable for exploring the
risk behaviours in an underground coalmine. Based on
multi-agent technologies, in this paper we present a
framework of virtual coalmine environments to model
and simulate underground risk behaviours.
According to the recent investigation and statistics of
underground accident cases in China and abroad [4-5],
unsafe human behaviours account for more than 80% of
underground accidents. Therefore, a significant challenge
in designing a coalmine virtual environment is to
effectively model and simulate underground human risk
behaviours. Moreover, typical coalmine accident
investigations conducted in recent 20 years in China also
show major accidents mainly result from "violations of
safety rules and regulations" [4]. This involves a broad
range of physical, psychological and social factors, such
as work experience, training level, fatigue, stress, panic,
emotional states, conscientiousness and personality
characteristics. Therefore, in order to approach
underground miners’ behaviour characteristics, we must
take into account underground mine workers’
physiological and psychological attributes such as
emotions, personality and motivation. In this paper, we
focus on modelling virtual miners’ emotional behaviours
based on the typical risk factors resulting in coalmine
accidents.
Currently, many virtual human models are available for
real-time applications. However, because of the scarcity
of human and social behavioural data for underground
coalmines, most current virtual human models can’t be
directly used in a virtual coalmine environment. There
are a lack of techniques to model and simulate
underground human behaviours that allow human risk
factors, such as fatigue, stress and panic, to be taken into
2
Int. j. adv. robot. syst., 2013, Vol. 10, 387:2013
account. In this paper, we attempt to incorporate current
virtual human technologies and underground miner’s
characteristics into modelling underground human risk
behaviours. Based on OCC (Ortony, Clore and Collins)
appraisal theories [6] and fuzzy logic techniques, we
present a fuzzy emotional behaviour model for a virtual
miner to simulate underground human responses to
potential hazardous events. The proposed emotional
behaviour model can create more believable virtual
coalmine environments. It can be used as a training tool
for potential miners to improve their risk awareness and
emergency decision-making ability. In addition, it can
also be used to analyse the risk factors of underground
accidents, especially human risk factors related to
underground mine workers’ emotions, personality and
motivation needs.
The main contributions of this paper are two-fold. First, it
presents a modelling methodology of underground risk
behaviours in a virtual coalmine environment based on
multi-agent technologies. Second, it implements the
emotional behaviour model for underground virtual
miner agents based on OCC appraisal theories and fuzzy
logic techniques.
This paper is organized as follows. In Section 2 related
work is reviewed. In Section 3 we propose a multi-agent
based virtual coalmine environment framework to model
and simulate underground risk behaviours. Section 4
introduces the modelling of human risk behaviour. A
fuzzy emotional behaviour mode will be presented for a
virtual miner agent. Section 5 presents the simulating
cases. Section 6 describes the evaluation of believability.
Finally, Section 7 presents some conclusions and possible
future work.
2. Related work
In the last few years, VR technology has increasingly been
used in mining industry. In [1], a PC-based VR training
simulator is developed to improve risk awareness for
underground miners. After identifying one of the
hazards, trainees are required to select appropriate
corrective actions from on-screen button icons. But the
training simulator pays little attention to the modelling of
human risk behaviours. In [7], a generic safety training
tool, SAFE-VR, is introduced. One of the key components
within the SAFE-VR world is the Hazards constructed by
oriented-object technology. In SAFE-VR, the Hazards are
inactive objects and are not suitable for modelling human
behaviours. In [8], a VR based safety training program is
presented for instructing the safety procedures and
guidelines of underground conveyor belts. However, the
proposed training program doesn’t involve modelling
human risk behaviours. Other existing research [2] also
focuses on geometric presentation or real-time dynamics
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3. to create immersive experiences. They hardly take into
account human-machine-environment related risk factors
resulting in underground safety accidents, especially
human risk behavioural factors.
proposed based on an OCC model. GEmA simulates 16
emotions by appraising events and actions with respect to
both goals and standards of agents. However, GemA
doesn’t include personality as well as motivation.
Human behaviour modelling and simulation in virtual
environments is still one of the most challenging research
topics. Many virtual human models have been presented
for real-time applications from training/education
systems to human computer interfaces and entertainment
films/computer games [9]. Each of these application areas
requires different properties at different levels such as
autonomous
behaviour,
natural
language
communication, personality modelling, emotional
simulation, adaptation to environmental constraints and
user needs.
Research in social science and human psychology has
shown that personality influences the emotion and
behaviour of an individual. For example, people who
have optimistic traits are inclined to have positive
emotions longer than pessimistic people. In [17], a model
of the dynamics of emotions is proposed for non-player
characters (NPCs) in a game. The NPC emotion model
focuses on the influence of personality on triggering
emotions. However, this model doesn’t take motivations
into account. In [18], an action selection mechanism is
presented to simulate human behaviours in realistic and
believable virtual humans according to their personality
and affective states. The proposed action selection model
takes into account a virtual human’s beliefs, desires,
intentions and the level of intensity of the events. Based
on personality and perceived intensity, a fuzzy model is
proposed to update the affective states of virtual humans.
In [19] a computational model of motivation is presented.
This model integrates motivation, emotion, personality,
behaviour and stimuli together. However, it only gives a
primary outline for the motivation model and is restricted
to being tested by a 3D facial animation system [18]. By
adopting the OCEAN (Openness, Conscientiousness,
Extroversion, Agreeableness, and Neuroticism) Model of
personality [20], a hierarchical fuzzy rule-based system is
constructed to facilitate the personality and emotion
control of the body language of a dynamic story character
[21]. There are other typical emotion models and systems
reviewed in [22, 23] such as Cathexis, ParleE, FearNot!
and Affective Reasoner. These models generally put
emphasis on explaining human emotion processes. They
have different advantages and disadvantages. However,
at this moment, it is not possible to find a universal model
that describes how emotions affect behaviours based on
personality and the events perceived from the
environment [17, 18].
In order to improve the believability of virtual agents in
virtual environments, a number of emotional agent
models have also been proposed. Most of these
computational models are based on the well-known OCC
model. According to OCC theory, emotions are triggered
by the subjective appraisal of the consequences of an
event on the agent’s goals, the actions performed by the
agent and the objects in the environment. The perception
and the cognitive appraisal of the event determine the
type and the intensity of the felt emotions. In the
following subsections, we focus on the prominent models
that use OCC theory.
EM Architecture used in the OZ project is the first
emotion model [10, 11]. The aim of the OZ project is to
provide the users with the experience of living in
dramatically interesting micro-worlds that include
moderately competent emotional agents. EM generates
emotions based on the importance level of the goal and
the success/failure of goals. A Fuzzy Logic Adaptive
Model of Emotions (FLAME) [12] is used to produce
emotions and simulate emotional processes. FLAME uses
fuzzy logic to represent emotions for mapping events and
observations of emotional states. However, the agent in
FLAME does not have clearly defined goals. Moreover,
personality is also not mentioned. In [13], Greta’s emotion
is triggered by belief that the probability of completing
one of its goals has been modified. Emotion and
Adaptation (EMA) [14] models consider both the
triggering of virtual humans’ emotions and their coping
behaviour. For the intensity of emotions, two parameters
are used: probability of goal attainment and goal
importance. The primary contribution of EMA is to show
how decision-theoretic planning techniques can calculate
the impact of events on goals and appraisals. EMA is
updated with recent developments [15]. The new work
also models a naturalistic emotional situation that
involves both rapid and slower emotional responses. In
[16], a generic emotional agent model, GEmA, is
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Uncertainty is an important aspect of human behaviour
under stress and panic. Traditionally, fuzzy systems are
usually used to model uncertain domains. Our research
involves developing a fuzzy logic based emotional
behaviour model to simulate the internal mental states of
a virtual miner. The virtual miner’s emotions are based
on the OCC model. We implement a subset of the OCC
model including Joy, Hope, Relief, Distress, Fear and
Disappointment, which focuses on the appraisal of the
perceived events and suits the specific application
environment in an underground coalmine. In order to
approach the personality traits of underground mine
workers, we adopt the OCEAN [20] model of personality
to enhance emotional simulation for the virtual miner.
Linqin Cai, Zhuo Yang, Simon X. Yang and Hongchun Qu: Modelling and Simulating
of Risk Behaviours in Virtual Environments Based on Multi-Agent and Fuzzy Logic
3
4. We stress Conscientiousness, Extroversion and Neuroticism
factors’ impacts on the motivation update process, the
emotion intensity threshold and the emotion decay.
Finally, virtual miner’s behaviours are constructed based
on their motivation needs, emotional states and
behaviour selection thresholds to simulate the emotional
responses to potential hazards and events in an
underground coalmine.
3. Risk behaviour simulation environment
based on multi-agent
A multi-agent simulation framework can present a
computational methodology to build an artificial
environment populated with various agents. Based on
agent-oriented methodology [3], agent based virtual
environment technology [24-25] and behaviour analysis
of an open complex system [26], the virtual coalmine
environment for underground risk behaviour simulation
is modelled as a three-tier architecture shown as in Figure
1. The proposed framework consists of the Simulation
Platform layer, the Multi-agent Environment layer and the
Intelligent Man-Machine Interface layer.
Intelligent Man-Machine Interface
Multi-agent Environment
Entity Agents
Device Agents
Miner Agents
Service Agents
InfoAgents
Environment Agents
AppAgents
CommAgents
Geometry and Physical Rending
Coalmining
Database
Simulation Platform(OpenGL,DirectX,Vega Prime,Operation System)
Figure 1. Multi-agent based virtual environment for underground
risk behaviour simulation
The Simulation Platform layer provides system running
support. This layer is composed of a hardware platform,
software development library, a distributed network
communication environment, an operation system and a
coalmining database. A typical software library includes
OpenGL, DirectX and Vega Prime. The coalmining
database mainly stores the required geological data,
measurement data, hydrological data and accident cases.
The geometry and physical rending is also implemented
in this layer. In addition, this layer is also responsible for
4
Int. j. adv. robot. syst., 2013, Vol. 10, 387:2013
the organization, optimization and management of
geometric scenes and the calculation of physical
properties such as collision detection and scenario-based
event management.
The Multi-agent Environment layer includes entity agents
(EntityAgents) and service agents (ServiceAgents). In this
layer, entity agents interact with each other and endow
the geometric entities with specific behavioural rules.
Correspondingly, EntityAgents can be classified as a
Device Agent, Miner Agent and Environment Agent. Each of
the Device Agents has a geometric shape of a specific
device, such as a tramcar, transducer or rescuer. A Device
Agent can be constructed according to the dynamics of a
specific device. A Miner agent is critical for building the
virtual coalmine environment for underground risk
behaviour simulation. It should take into account
underground miners’ physiological and psychological
attributes. The Environment Agent mainly describes the
risk behaviour of all kinds of underground hazards. It can
be implemented using the risk factors and environmental
parameters stored in the coalmining database.
In the Multi-agent Environment layer, ServiceAgents offer
basic system services and can generally be categorized into
three classes: communication service agent (CommAgent),
information service agent (InfoAgent) and specific
application service agent (AppAgent). CommAgent is
responsible for network communication provided by the
Simulation Platform layer such as High Level Architecture
(HLA) services. InfoAgent provides the necessary semantic
information and knowledge for intelligent behaviour in the
virtual coalmine environment. AppAgent acts as a service
provider in specific applications such as tutor agent and
expert agent in safety training.
ServiceAgents may interact not only with EntityAgents, but
also with the geometric entities in the virtual
environment. InfoAgent is responsible for translating
commands from EntityAgents into the state changes of
specific entities in geometric and physical scenes. In
addition, InfoAgent also translates the state changes of
geometric entities into meaningful knowledge and
transmits them to EntityAgents for decision-making. Each
agent has its own control mechanisms, knowledge base
and communication interfaces. Communication protocol
defines the basic messaging mechanisms and interaction
methods among agents.
The Intelligent Man-Machine Interface layer is the interface
with which the user interacts directly with virtual
environment via VR multi-modal presentation devices. A
typical immersive VR system consists of a data glove
with a hand tracker, a Head Mount Display (HMD) with
a head tracker, a 3D sound server and a host computer.
The hand tracker, the head tracker and the data glove
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5. control the head, hand and finger orientations
respectively while an HMD presents computer-generated
images. Inside a virtual environment, the user can
manipulate and interact with graphical objects with a
computer-generated virtual hand. The orientation of this
virtual hand is controlled by the measured hand and
fingers’ positions.
In the proposed framework, a multi-agent based
simulation environment can fully create the overall
behaviours of underground risk accidents. Moreover,
this framework is helpful to the system development,
maintenance and upgrade. To make things more precise
and ambiguity-free, a multi-agent based virtual coalmine
environment is formally defined as follows.
Definition 1 The multi-agent based virtual coalmine
environment (MBVC) can be described as a three-tuple:
MBVC::= <CEnvironment, Agents, EAInteractions>, where
CEnvironment is an underground natural physical
environment model that represents the global knowledge
of coalmine properties, Agents is a multi-agent system
and EAInteractions: CEnvironment × Agents is the
interaction between agents and the virtual environment.
Definition 2 Agents can be defined as a two-tuple:
Agents::= <AGENT, COMMUNICATIONS>, where AGENT
is the set of agents, including ServiceAgents and
EntityAgents, i.e., AGENT= {Agenti�i = 1, 2... n; (Agenti ∈
ServiceAgents) or (Agenti ∈ EntityAgents)} and
COMMUNICATIONS is the set of communications.
Definition 3 COMMUNICATIONS can be represented as:
COMMUNICATIONS = {<Sender, Receiver, MsgContent> |
(Sender∈
AGENT)
and
(Receiver
∈
AGENT)
and
Definition 5 Event in the virtual coalmine environment
can be described as an eight-tuple: Event ::=<EventID,
EventCause,
EventObject,
EventRegion,
EventLevel,
StartTime, EndTime, EventEvolution>, where EventID is the
event ID number, EventCause is the event cause,
EventObject is the lists of agents that are affected by the
event, EventRegion is the region affected by the event,
EventLevel is the intensity of the event, StartTime is when
the event begins, EndTime is when the event ends and
may be NULL and EventEvolution is the event dynamics,
which can be a process description for simple events or be
defined with a finite state machine for complex events.
4. Human risk behaviour modelling
According to the behaviour characteristics of mine
workers in underground coalmines, we focus on
modelling underground human risk behaviours. Based
OCC appraisal theories and fuzzy logic, we propose a
fuzzy emotional behaviour model for a virtual miner
agent to simulate human responding behaviours to
underground potential risk events.
4.1. Virtual miner model
According to the actual coalmine environment,
underground
mine
worker’s
behavioural
characteristics[4-5] and related virtual human models [2728], a virtual miner agent is organized as a hierarchical
model, which incorporates perception, motion control,
internal states, behaviour and cognition sub-modules, as
shown in Figure 2. The model is reactive and able to
rapidly adapt to external unexpected situations and can
also plan consistent goal-oriented behaviours.
(MsgContent ∈ Communication (L))}, where Sender is the
communication sender, Receiver is the communication
receiver, MsgContent is the communication content
related to the communication protocol and L represents
the communication language.
Definition
4
Agent-environment
interaction,
EAInteractions, can be described with a partially
observable Markov Decision Process [25] and be
represented as a six-tuple: EAInteractions::= <State, Action,
T, R, O, P>, where State is the finite set of environmental
states, State = {statei|i=1,2,…,n}, Action is the finite set of
agent actions, Action={actionj|j= 1,2,…,m}, T is the state
transition function from statet into statet+1 when an action,
actiont, is taken, T(statet,actiont,statet+1), R is the immediate
reward function for taking actiont in statet+1,
Virtual Miner
Cognition
World
model
Planners
(Goals)
Internal States
(Motivation,Emotion,
Personality)
Perception
States
Working Memory
Check(Gas,Num)
……
Behavior
Motion Control
Actions
(Events, Objects, Agents）
R( statet +1, actiont ) : State × Action → R , O is the finite set of
observations, O( statet , rt ) and P is the probability of
making observation pt +1 from statet +1 after having taken
action actiont, P ( action t , state t +1 , p t +1 ) .
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Virtual Coalmine
Figure 2. Virtual miner agent model
Linqin Cai, Zhuo Yang, Simon X. Yang and Hongchun Qu: Modelling and Simulating
of Risk Behaviours in Virtual Environments Based on Multi-Agent and Fuzzy Logic
5
6. In the Motion Control level, we employ a human
animation software package called DI-Guy to
parameterize the basic motions. Due to the actual
environment in an underground coalmine, the virtual
miner’s behaviours are mainly reflected with its gestures
and postures, including changes in direction, changes in
speed, gaze direction, body orientation, etc. When
accepting motion commands from the Behaviour module,
the virtual miner selects the basic parameterized motion
sequences from its motion repertoire to update its
gestures and postures.
The Behaviour module is constructed by a set of
parameterized basic actions and is controlled by a
behaviour selection mechanism. Basic actions are
described by finite state machines and/or hierarchical
finite state machines. These parameterized basic actions
are semantically independent of each other and serve as
the building blocks to build complex behaviours.
Formally, the state space of virtual environment was S,
S={statei|i=1,2,…}, the effective action space of the virtual
miner was A, A={actionj|j= 1,2,…}. The virtual miner can
obtain a set of precepts P, P={perceptionk|k=1,2,…} and has
a set of Internal states I, I={innerAttrm|m=1,2,…}. The
Perception module can perceive the environment states S
and Perception:P → S. The Cognition module generates the
current task T for the virtual miner to implement its goal
G with task planner Plan : G → T . The Behaviour module
can create motion control commands B according to
external environment perception P, internal attributes I
and domain knowledge K to implement its current task T,
Behavior(T ) : P × I × K → B . Finally, the motion control
commands are implemented by a movement mechanism
in the Motion Control module, MotionCont rol : B → A . In
each step t ， the virtual miner takes actiont and the
environment state indefinitely changes from statet to
statet+1, i.e., statet+1=Environment (statet, actiont).
In this paper, we focus on the virtual miner’s emotion model,
taking into account personality and motivation and further
simulate the emotional responses to external events based on
OCC theories and fuzzy logic techniques. The concrete
implementation of other modules such as Perception,
Motion Control and Cognition can be referenced in [29].
4.2. Fuzzy emotional behaviour modelling
Underground human behaviour is uncertain and fuzzy.
With the aim of implementing fuzzy emotional behaviour,
we apply fuzzy sets to represent emotional states and
fuzzy rules to represent the mapping from events to
emotions. Moreover, to approach underground mine
workers’ characteristics, we take into account
underground miners’ personality traits and the impacts
of perceived events on goal attachment.
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Int. j. adv. robot. syst., 2013, Vol. 10, 387:2013
Firstly, according to the actual underground work
environment, personality p of the virtual miner is
constant. p is initialized with the level of intensity of each
personality scale defined by the OCEAN at time t=0.
Definition 6 Personality can be described as a vector p:
p=[O, C, E, A, N], where O, C, E, A and N represent
Openness, Conscientiousness, Extroversion, Agreeableness
and Neuroticism, respectively. The value of O, C, E, A and
N is in the interval of [0, 1].
In this paper, we stress the impacts of Conscientiousness,
Extroversion and Neuroticism on the intensity threshold of
emotions, the emotion’s decay and the motivation update
process.
The virtual miner’s emotional state vector et is dynamic
over time and initialized to 0 at time t=0. At each time t, et
represents the intensity of six basic emotions defined by
the OCC model.
Definition 7 At time t, the virtual miner’s emotional state
can be described as vector et: et=[Jo, Ho, Re, Di, Fe, Da],
where Jo, Ho, Re, Di, Fe and Da represent Joy, Hope, Relief,
Distress, Fear and Disappointment, respectively. The value
of Jo, Ho, Re, Di, Fe and Da is in the interval of [0, 1].
In the OCC model, the intensity of the emotions of Joy,
Distress, Relief, Disappointment, Hope and Fear is positively
correlated with the degree of desirability/undesirability of
the event and to its probability of occurrence. Once a
virtual miner has perceived an event, an update process
is carried out in order to change its affective states. The
new emotional state vector et+1 over time t+1 is calculated
by taking into account the current perceived event and
the goal of the virtual agent as follows:
et +1 = et + I e ( goal , event ) ,
(1)
where et is the intensity of emotion at time t, Ie(goal, event)
represents an emotional influence vector that contains a
desired change of intensity for each of the six emotions.
To compute the intensities of emotions, the OCC model
postulates four global intensity variables (arousal,
unexpectedness, proximity and sense of reality) and up to
four local variables for each emotion type [6]. While the
virtual miner’s emotion generation is based on the OCC
model, we do not represent the complex intensity system
of the OCC model but instead focus on a simple
implementation of OCC cognitive processing [30].
Moreover, according to OCC emotional variables, we
define fuzzy rules to update emotion states for the virtual
miner agent. Following [13] and [17], we take the impact
of perceived events on goal attainment and the goal
importance into account. These variables are denoted as
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7. ProbabilityImpact(goal,
event)
and
Importance(goal),
respectively. To obtain this Ie vector, we define a set of
fuzzy rules of the form given as follows:
AND
IF
ProbabilityImpact(goal,event)
IS
Aj
Importance(goal) IS Bk
THEN Intensity(Joy) IS C1, Intensity(Hope) IS C2,
Intensity(Relief) IS C3, Intensity(Distress) IS C4,
Intensity(Fear) IS C5, and Intensity(Disappointment)
IS C6.
where Aj is the impact intensity level (Negative, NoImpact
and Positive) of the current perceived event on the
probability of goal attainment, Bk is the importance level
(NoImportant, SlightlyImportant and ExtremelyImportant) of
the goal and C1,C2,…and C6 are the emotional intensity
(LowIntensity, MediumIntensity and HighIntensity) to each
of the six basic emotions.
Consider a miner aiming to inspect underground hazards
to prevent underground safety accidents. An event, such as
loose rock falling from the damaged roof may affect the
miner’s current goal. If the rock falling is not very serious,
it can help prevent roof safety accidents. Thus, a slight rock
falling event is desirable and has a positive impact on the
goal attainment of inspecting potential hazards. Therefore,
when some small rocks fall, miners always positively
investigate the causes and identify their potential hazards.
The fuzzy rule relevant to the situation is as follows:
Rule 1: IF ProbabilityImpact(Inspecting Hazards, Rock
Falling) IS Positive AND Importance(Inspecting
hazards) IS ExtremelyImportant
THEN Intensity(Joy) IS MediumIntensity, Intensity(Hope)
IS HighIntensity, Intensity(Relief) IS LowIntensity,
Intensity(Distress) IS LowIntensity, Intensity(Fear) IS
MediumIntensity and Intensity(Disappointment) IS
LowIntensity.
In this paper, the perceived event’s impact on goal
attainment and the goal importance are fuzzified using a
Gaussian-shaped membership function. Based on related
fuzzy rules, we can obtain the fuzzy emotional vector
Intensity (e) with the fuzzy inference process. We choose
the Mamdani model and Sup-Min composition to
compute the matching degree for each rule. The inference
formula is given in Equation (2).
μC (z) = ∨[μA (x) ∧ μB (y) ∧ μA(x) ∧ μB(y)]∧ μC (z)
'
x, y
'
'
(2)
where x is the impact of the perceived events on goal
attainment, y is the goal importance and z is the intensity
of emotion. The ∧ operator takes the minimum of the
membership functions and the ∨ operator takes the
maximum of the membership function. A, A', B, B', C and
C' are fuzzy sets. Correspondingly, A and A' are the
impact intensity set (Negative, NoImpact, Positive) of the
current perceived event on the probability of goal
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attainment, B and B' are the importance level set
(NoImportant, SlightlyImportant, ExtremelyImportant) of the
goal and C and C' are the emotional intensity set
(LowIntensity, MediumIntensity, HighIntensity).
To calculate the crisp intensity of emotional vector Ie,
fuzzy emotional vector Intensity (e) is defuzzified by the
Mamdani centroid defuzzification model as:
Z MON =
μ
C
( z ) zdz
z
μC ( z)dz
.
(3)
z
According to Equation (1), adding Ie to the virtual miner’s
current emotional state et, we can obtain the new
emotional states et+1.
Furthermore, we define an intensity threshold for each
emotion according to the character’s personality [13,
17]. The intensity threshold, denoted as α , controls the
activation of the emotion type. Emotion types with an
intensity level lower than α are considered as moods.
The impact of mood on the virtual miner’s emotions
and behaviours is out of the scope of this paper and
will not be presented here. On the other hand, the
emotion with the highest intensity inhibits other
emotions. For example, an emotion like sadness will
tend to inhibit joy if sadness is more intense than joy.
Moreover, we give a slight preference to negative
emotions since they often dominate in situations where
opposite emotions are triggered with nearly equal
intensities [12]. Only the most dominant emotion
whose computed intensity exceeds the pre-defined
threshold α does have an impact on the agent
behaviours. Therefore, when perceiving an event, the
virtual miner can obtain its current most predominant
emotion state based on the fuzzy rules and the emotion
filter mechanism.
The intensity of emotions will naturally decay through
time once the triggering event has disappeared. Using
Picard’s decay function [31], at any time t the value for
the intensity of an emotion is given as:
et = et −1e − d ( t −t0 ) ,
(4)
where et and et-1 is the emotion intensity at time t and t-1,
respectively, t0 is the emotion triggering time and d is the
decay rate, which determines how fast the intensity of the
particular emotion will decrease over time. Parameter d is
related to the emotion type and the agent’s personality
straits. Generally, negative emotions tend to decay slower
than positive emotions [12]. In our current
implementation, each emotion will be attached to a
Linqin Cai, Zhuo Yang, Simon X. Yang and Hongchun Qu: Modelling and Simulating
of Risk Behaviours in Virtual Environments Based on Multi-Agent and Fuzzy Logic
7
8. specific d value according to the agent’s personality
straits. An extrovert individual feels positive emotions
with a lower decay rate than neutral or introvert
personalities. However, extroversion has no impact on
negative emotions. A neurotic individual feels negative
emotions with a higher decay rate, without any impact on
positive emotions.
On the other hand, the virtual agent’s motivation tends to
interrupt the cognitive process to satisfy a higher internal
need. In order to create more believable behaviours in
virtual entertainment, a virtual character should also
select its actions taking its motivational states into
consideration [19]. In each simulation step, the intensity
of motivational needs will be impacted by internal
physiological parameters and external environmental
factors [32]. Internal physiological parameters include
behaviour type, behaviour duration time, motivation
duration time and so on. For instance, the intensity of run
is higher than that of walk. External environment factors
include hazards, safety warning signs, water, other
agents, etc. In this paper, the virtual miner’s motivational
states vector mt is dynamic over time t and is initialized to
0 at time t=0. At each time t, mt represents the intensity of
fatigue, hunger and thirst.
Definition 8 At time t, motivation can be defined as
vector mt: mt=[Fa, Hu, Th], where Fa, Hu and Th represent
Fatigue, Hunger and Thirst, respectively. The Value of Fa,
Hu and Th is in the interval of [0, 1].
Finally, combined with internal attributes such as
emotion, motivation and perceived external situations, an
action selection mechanism initiates appropriate
behaviours to satisfy these desires. In case more than one
desire awaits fulfilment, the most important desire ranked
by the action selection mechanism receives the highest
priority. In this paper, the internal variable with the
highest intensity will be dominant, motivation inhibits
emotion with equal intensity and fear inhibits motivation
variables when the fear level is higher than the
motivation level. Once a desire is fulfilled, the value of
the internal variable begins to change back asymptotically
to its nominal value according to Equation (4).
In the virtual coalmine environment we classify miners
by their job categories. Each of the job categories of the
miner has an associated action selection mechanism with
behaviour-triggering combinations of internal state
thresholds and situation patterns set accordingly. For a
safety inspector, when the current site must be inspected
(such as spotting a roof support, inspecting the
equipment status and hitting sidewall conditions), the
virtual miner will firstly complete these tasks but when
the fear level is dominant, the virtual miner will escape
from the dangerous site, when the miner is tired, he will
find a place to rest until the value of his Fatigue value
returns to normal levels and when he is happy because of
identifying unexpected hazards, the virtual miner will
powerfully walk to the next goal place.
5. Case study and implementation
The virtual miner’s internal needs are updated according
to Equation (5).
mi (t ) = min mi (t − 1)δ i + β i +
γ
k
,
ki (t ), 1
(5)
where mi (t ) is the need intensity value of the ith
motivation variable at time t, δ i is the decay rate of the
ith motivation, β i is the growth rate of the ith
motivation and γ ki (t ) is the impact of the kth external
environment factor on the ith motivation need at time t,
γ ki ∈ [−1, 1] . The value of δ i and β i is set according to
the internal physiological parameters and the
personality traits. Generally, the Conscientiousness
parameter in OCEAN influences how soon goals will be
abandoned and new goals are adopted [33]. A
conscientious personality will try and achieve all goals.
However, neurotic people will in general experience
negative thoughts, which can lead to less ambition. We
also define activation threshold m for each motivation
variable. Once these states reach an activation threshold,
they send a signal to the cognitive process indicating a
specific desire.
8
Int. j. adv. robot. syst., 2013, Vol. 10, 387:2013
To test the proposed simulation environment and related
behaviour models, we have reconstructed some accident
cases in an underground coalmine [4], which can train
miners’ decision-making ability when facing occasional
or emergency situations in an underground mine
environment. We have collected the typical coalmine
accident cases from the last 20 years in China. We
employed Multigen Creator to construct the geometric
modes of the virtual mine, the virtual miner, a safety
lamp, a self-rescuer, etc. The other simulating tools and
developing environments include VC++, DI-Guy SDK,
Vega Prime and Open GL.
In the first case, the virtual miner is a safety inspector
who aims to spot potential hazards in the underground
coalmine to prevent safety accidents. The virtual miner
will first enter into the underground coalmine from the
coalmine entrance. Then, he will walk through the shaft
station, the main roadway, the transformer substation,
the working face, etc. In this process, some typical
hazards, such as loose rocks, an unsupported roof in a
work area, excessive water, a damaged support and
shorts on electrical cables may appear. The virtual miner
may also possibly engage in some risk behaviours, which
www.intechopen.com
9. violate underground safety rules and regulations,
because of his motivation needs. When identifying a
hazard, the virtual miner will inspect it with nonverbal
postures/gestures and some basic actions such as gaze,
aim, hit and hammer according to his current emotional
states and internal motivation needs.
In this case, the virtual miner is a very neurotic person with
medium conscientiousness, who is anxious, nervous and
prone to depression. Therefore, the virtual miner’s
personality p is initialized as p=[0, 0.45, 0.3, 0, 0.7] according
to Definition 6. Currently, we can’t take into account the
Openness and Agreeableness of the OCEAN model.
At each simulating step, the virtual miner’s motivational
needs are updated based on Equation (5) (introduced in
Section 4.2). According to our tests, the value of the decay
rate δ i is in the interval of [0, 0.05] and the value of the
growth rate of βi is in the interval of [0, 0.1]. As the
safety-inspection tasks are fulfilled, the virtual miner’s
fatigue value becomes very high. Fatigue becomes the
most dominant motivation state.
On the other hand, the virtual miner’s emotion vector et is
updated based on Equation (1). In the OCC model, Relief
is generated when an undesirable event failed to occur. In
this case, the intensity of Relief increases more quickly
when no risk events occur during safety inspection.
However, according to the action selection mechanism,
the virtual miner’s motivation state, Fatigue, is the most
predominant. As a result, the virtual miner’s risk
awareness to potential dangers decreases because of his
need to take a rest, which is motivated by the Fatigue state.
Figure 3 shows some screenshots of the simulation case.
The right frame shows the changing intensity of emotions
and motivations in the simulation process. The checked
states of emotions and motivations indicate the most
dominate internal attributes, which influence the virtual
miner’s behaviours. In Figure 3 (a), the motivation state,
Fatigue, is the most predominate. The virtual miner
tiredly walks through a tunnel without spotting the loose
rocks on the damaged roof shown in the red frame.
The roof state is controlled by a roof agent, which belongs
to an Environment Agent. The interaction between the roof
agent and the virtual miner is implemented by the
communication mechanism introduced in Definition 3. In
this case, when the roof strength value is less than the
pre-set value, a rock falling event on the roof will occur.
Then, the roof agent instantly messages this event to
virtual miners in its influence region.
When perceiving the rock fall event, the virtual miner
carries out an appraisal using fuzzy rules. In this case, a
severe rock fall generally indicates that a more severe
www.intechopen.com
roof falling accident is likely to occur. Therefore, a severe
rock falling event is generally undesirable. It can stop the
virtual miner from inspecting potential hazards and even
threaten the miner’s life. The fuzzy rule relevant to the
event is as follows:
Rule 2: IF ProbabilityImpact(Inspecting Hazards, Rock
Falling) IS Negative AND Importance (Inspecting
hazards) IS ExtremelyImportant
THEN Intensity(Joy) IS LowIntensity, Intensity(Hope) IS
LowIntensity, Intensity(Relief) IS MediumIntensity,
Intensity(Distress) IS MediumIntensity, Intensity(Fear)
IS HighIntensity and Intensity(Disappointment) IS
MediumIntensity.
According to the fuzzy inference process introduced in
Section 4.2 and Rule 2, the virtual miner believes a more
severe roof falling accident will occur and his safetyinspection goal likely fails. According to Equation (1),
emotional influence vector Ie can be obtained based on
Equation (2) and Equation (3). In this case, the emotion
state, Fear, becomes very high and is the most dominant.
The motivation state, Fatigue, is temporarily inhibited.
Thus, the virtual miner escapes in a panic from the site of
the falling rocks to pursue his safety needs. In Figure 3(b),
facing an unexpected event of a severe rocks fall, the
virtual miner runs away from the falling rocks.
The context of the second simulation case is a typical
coalmine locomotive transport accident. In underground
coalmines, all mine workers should be aware of the jobspecific hazards in their workplace. Moreover, all of them
should be able to identify all the generic hazards or risk
behaviours violating safety rules and regulations outside
of their workplace.
In this case, the virtual miner’s motivational needs are
updated with Equation (5) (Section 4.2). When finishing
his tasks, the virtual miner’s Fatigue value increases. Thus,
the virtual miner’s awareness to hazards will greatly
decrease. Driven by his desire to go out of the
underground mine, the virtual miner hurries to tiredly
pass through an intersection of roadway. As a result, the
virtual miner is seriously hit by an approaching
locomotive. Figure 4 shows some screenshots of the
simulated accident. Figure 4 (a) is the scene of the
coalmine entrance. In Figure 4 (b), the virtual miner is
walking in a main roadway. In Figure 4 (c), the virtual
miner is tiredly going through the intersection of the
transportation roadways. He violates the related rules of
“when a mine car is going through, the human must stop.”
Figure 4 (d) is the scene where the virtual miner was
seriously injured by a locomotive. The case explores the
causal relationships among psychological motivation
needs, violating behaviours and risk accidents. The
reconstructed case can be used for miner training to
improve the ability of hazard awareness in underground
working environments.
Linqin Cai, Zhuo Yang, Simon X. Yang and Hongchun Qu: Modelling and Simulating
of Risk Behaviours in Virtual Environments Based on Multi-Agent and Fuzzy Logic
9
10. Loose rock
(a)
(b)
Figure 3. Underground hazard spotting case. (a) Tiredly walking through a tunnel, (b) avoiding a rock falling event
6. Evaluation of believability
One challenge in designing a virtual environment is to
validate it. Due to safety and ethical issues, it is difficult
to fully validate a coalmine virtual environment, which
really requires multiple real-life studies with people in
underground risk situations. To evaluate the believability
of the proposed system and behaviour models, we chose
a user assessment method. Users evaluated the system
and then feedback was gathered via a questionnaire.
Previous studies have fully proved this method is
advantageous and effective [12, 16, 30, 32, 34].
Participants in the evaluation were recruited by email from
our lab. We selected 20 students. The ages of the subjects
10 Int. j. adv. robot. syst., 2013, Vol. 10, 387:2013
were in the range of 22-25 years old. The male-to-female
ratio was 3:1. All the participants were first year graduate
students. Most of them received their bachelor degree in
computer science, automation science, or system
engineering. While the results depend on the ability of our
subjects to accurately judge how realistic the behaviours of
the virtual miners are, we feel that these students have the
necessary common knowledge to perform this task
according to our experiment introduction and instruction.
Before starting the evaluation, participants were given a
twenty-minute introduction to the system and the
evaluation method. In this introduction, they were first
notified that their responses would be used to further
enhance the system. This way, users were encouraged to
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11. give constructive criticism of the system, while avoiding
assessing the system in an unduly positive fashion
because they are either trying to be nice or are impressed
by the interface. In addition, the participants were given
an introduction to the underground mining environment.
Emphasis was put on the typical hazards in underground
coalmine and the typical risk behaviours resulting from
the violation of coalmine safety rules and regulations.
Then participants were handed instruction sheets, which
walked them through different scenarios and showed
them the different aspects of the virtual miner’s
behaviours. Eventually, they were asked to answer the
following question on five levels: very low, low, medium,
high and very high.
(c)
How well does the virtual miner show its behaviours?
The question was also quantified from one (very low) to
five (very high) in order to further verify the evaluation
results. In order to ensure the anonymity of the response,
the participants were also asked not to write their names
or mark the papers in any way that might be used to
identify them at a later time period.
(d)
Figure 4. Typical locomotive transport accident simulation.
(a) the scene of the coalmine entrance, (b) walking in the main
roadway, (c) hurrying to pass through an intersection of the
transportation roadway, (d) injured by a locomotive
(a)
(b)
www.intechopen.com
In the evaluation, participants worked with the system in
two modes: first without the fuzzy emotional behaviour
model (i.e., Random-Mode) and second with the fuzzy
emotional behaviour model (i.e., Emotion-Mode). In
Random-Mode, the virtual miner’s behaviour is chosen at
random when facing emerging events.
The questionnaires were collected and analysed for 20
subjects. In addition, participants’ interpretation of the
evaluation scores enable us to further calibrate the
behaviour model. Figure 5 shows the average satisfaction
of users in two modes. As Figure 5 shows, in RandomMode, the satisfaction of users is 55% medium and 5%
high. In Emotion-Mode, the satisfaction of users is 25%
medium, 45% high and 15% very high. The introduction
of the fuzzy emotional behaviour model conveys a more
realistic or convincing behaviour to the users since the
rating is 45% high, which improves this measure by 40%
on average. On the other hand, there is 5% of users whose
satisfaction is high in Random-Mode. This shows the
Linqin Cai, Zhuo Yang, Simon X. Yang and Hongchun Qu: Modelling and Simulating
of Risk Behaviours in Virtual Environments Based on Multi-Agent and Fuzzy Logic
11
12. selected random behaviour at that time was consistent
with the virtual scene.
60%
50%
Random-Mode
Emotion-Mode
40%
30%
20%
10%
0%
very low
low
medium
high
very high
Figure 5. The comparison of the risk behaviour of virtual miner
without the emotion model (Random-Model) and with the
emotion model (Emotion-Model)
To further compare these results, we applied the
difference of the scores derived by subtracting the
Random-Model’s score from the Emotion-Model’s. In the
evaluation, the mean score for the Emotion-Model is 3.55.
The mean score for the Random-Model is 2.5. The data for
the differences in believability scores is presented in
histogram form in Figure 6. This histogram shows the
differences in how the Emotion-Model and the RandomModel are scored on a scale from one (very low) to five
(very high). As Figure 6 shows, 15 of the 20 participants
felt the Emotion-Model was more believable, two felt
they were evenly believable and three felt the RandomModel was more believable. Also, nine of the 20
participants score the Emotion-Model one point higher
than the Random-Model on the one-five scale.
In order to test the statistical significance of these results
more rigorously, we applied a t-test [30] to the difference
of the scores that each user gave the Emotion-Model and
the Random-Model. A t-test is used to make statistical
claims about the actual mean of some population given
the results of some sample of the population. The t-test is
used in this case because it does not require the standard
deviation of the larger population to be known, also it is
reasonably robust in cases where the distribution is not
normal as long as the sample size is at least 15, the
distribution is not significantly skewed and there are no
strong outliers.
In this test, the sample size is 20. For each user, we
determine the difference in believability scores given to
the Emotion-Model and the Random-Model. If each
participant gave them the same scores, then the mean of
the distribution would be zero. The parameters and
calculated values for the t-test are listed in Table 1.
n
20
Mean
1.05
SD
1.43
SE
0.32
T value
3.28
t0.01(19)
2.86
Table 1. Results of the mean t-test of the difference in
believability scores given to Emotion-Model and Random-Model
As shown in Table 1, this actual sample has a mean of
1.05, standard deviation (SD) of 1.43 and mean standard
error (SE) of 0.32. The t value of 3.28 is greater than t0.01 (19)
of 2.86. This data confirms the hypothesis that the mean of
the population is greater than zero with 99% confidence.
Therefore, we can show with high probability that, given
this sample, if we were to sample the entire population,
the Emotion-Model’s mean score for believability would
be greater than the Random-Model’s. In other words, the
behaviours of the Emotion-Model are more believable
than that of the Random-Model.
Number of Users
7. Conclusion and future work
Emotion-Model’s score minus Random-Model’s score
Figure 6. The comparison of the difference of the believability
scores derived by subtracting the Random-Model’s believability
scores from the Emotion-Model’s
12 Int. j. adv. robot. syst., 2013, Vol. 10, 387:2013
In this paper, a multi-agent based virtual coalmine
environment framework is developed to simulate
underground coalmine risk behaviours. The proposed
approach can not only avoid the disadvantages of
traditional experiences based analysis of coalmine risk
accidents, but also build a systematic methodology for
risk behaviour simulation of the virtual coalmine. In
addition, a fuzzy emotional behaviour model is proposed
to simulate the underground human risk behaviours,
which maps the impact of perceived events on goal
attainment and the goal importance to create more
believable
emotion
responses
to
underground
unexpected events. To capture the underground mine
workers’ behaviour characteristics, we take into account
the impacts of underground mine workers’ personality
traits on the intensity threshold of emotions, the emotions’
decay and the motivation update process.
www.intechopen.com
13. Finally, we introduce the implementation of the hazard
spotting case and the typical locomotive transport
accident. To further evaluate the believability of the
proposed system and behaviour models, we chose a
user assessment method. In addition, we applied a t-test
to the difference in the scores each user gave the
Emotion-Model and the Random-Model. Experimental
results show the proposed model can create a more
believable virtual coalmine environment to simulate
emotional behaviours, which promises to assist in
improving underground miners’ risk awareness and
further training miners’ decision-making abilities when
facing occasional or emergency situations in
underground coalmines.
There exist many factors that influence the behaviours of
an individual, which make it impossible to predict with
accuracy the actions that a person performs in the face of
certain events [16]. In the OCC model, emotions are
triggered by appraisal of the events, objects and other
agents. This paper has implemented event appraisal
based on OCC theories and fuzzy logic. In our next work,
we will enhance the emotion model, taking into account
other factors’ influences on the affective state of the
virtual miner.
Validation by capturing data from real-life underground
emergency situations is very difficult because of safety
and ethical issues. In this paper, we evaluate the
believability of risk behaviours of a virtual miner to a
limited extent by a user assessment method. Behavioural
data used to construct a human risk behaviour model are
based on the underground risk factors that resulted in
coalmine accidents. In order to calibrate the behaviour
model, we can further investigate all kinds of risk factors
that have resulted in accidents, especially mine workers’
physical and psychological parameters, to obtain more
accurate behavioural rules. In addition, some
environmental parameters that may affect mine workers’
physiology and psychology, such as gas density,
environment temperature and air pressure, can be
collected to improve the behaviour model. We will take
these factors into account in our next work. Moreover, we
also plan to perform experiments in more complex
situations with field users in coalmines and to further
validate the system and modes.
8. Acknowledgements
This work is supported by the National Natural Science
Foundation of China (50804061, 61102145), the Research
Project of Chongqing Municipal Education Commission
(KJ130522) and the Natural Science Foundation Project of
CQ CSTC (CSTC, 2009BB2281, cstc2013jcyjA40042,
cstc2013jcyjA40014)
www.intechopen.com
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![coalmine environment, a virtual miner agent can direct
trainees to practice and experience underground
situations, activities and processes without putting them
in any actual danger and further improve trainees’ risk
awareness. Moreover, some emergent situations, which
have not previously occurred but could be encountered at
a mining site, can also be reproduced. However, most
current coalmine virtual environments hardly take into
account the complex behaviour mechanism of humanmachine-environment related risk factors in underground
coalmine accidents [1-2].
Multi-agent technology has been widely applied in
problem solving, virtual environments, collective robotics
and many other areas [3]. In a multi-agent virtual
environment, agents can autonomously sense the
environment and reason about their goals. By defining
the interactive rules among individuals, agents can fully
cooperate with each other to create the complex
mechanism of a dynamics system. Therefore, multi-agent
technologies are particularly suitable for exploring the
risk behaviours in an underground coalmine. Based on
multi-agent technologies, in this paper we present a
framework of virtual coalmine environments to model
and simulate underground risk behaviours.
According to the recent investigation and statistics of
underground accident cases in China and abroad [4-5],
unsafe human behaviours account for more than 80% of
underground accidents. Therefore, a significant challenge
in designing a coalmine virtual environment is to
effectively model and simulate underground human risk
behaviours. Moreover, typical coalmine accident
investigations conducted in recent 20 years in China also
show major accidents mainly result from "violations of
safety rules and regulations" [4]. This involves a broad
range of physical, psychological and social factors, such
as work experience, training level, fatigue, stress, panic,
emotional states, conscientiousness and personality
characteristics. Therefore, in order to approach
underground miners’ behaviour characteristics, we must
take into account underground mine workers’
physiological and psychological attributes such as
emotions, personality and motivation. In this paper, we
focus on modelling virtual miners’ emotional behaviours
based on the typical risk factors resulting in coalmine
accidents.
Currently, many virtual human models are available for
real-time applications. However, because of the scarcity
of human and social behavioural data for underground
coalmines, most current virtual human models can’t be
directly used in a virtual coalmine environment. There
are a lack of techniques to model and simulate
underground human behaviours that allow human risk
factors, such as fatigue, stress and panic, to be taken into
2
Int. j. adv. robot. syst., 2013, Vol. 10, 387:2013
account. In this paper, we attempt to incorporate current
virtual human technologies and underground miner’s
characteristics into modelling underground human risk
behaviours. Based on OCC (Ortony, Clore and Collins)
appraisal theories [6] and fuzzy logic techniques, we
present a fuzzy emotional behaviour model for a virtual
miner to simulate underground human responses to
potential hazardous events. The proposed emotional
behaviour model can create more believable virtual
coalmine environments. It can be used as a training tool
for potential miners to improve their risk awareness and
emergency decision-making ability. In addition, it can
also be used to analyse the risk factors of underground
accidents, especially human risk factors related to
underground mine workers’ emotions, personality and
motivation needs.
The main contributions of this paper are two-fold. First, it
presents a modelling methodology of underground risk
behaviours in a virtual coalmine environment based on
multi-agent technologies. Second, it implements the
emotional behaviour model for underground virtual
miner agents based on OCC appraisal theories and fuzzy
logic techniques.
This paper is organized as follows. In Section 2 related
work is reviewed. In Section 3 we propose a multi-agent
based virtual coalmine environment framework to model
and simulate underground risk behaviours. Section 4
introduces the modelling of human risk behaviour. A
fuzzy emotional behaviour mode will be presented for a
virtual miner agent. Section 5 presents the simulating
cases. Section 6 describes the evaluation of believability.
Finally, Section 7 presents some conclusions and possible
future work.
2. Related work
In the last few years, VR technology has increasingly been
used in mining industry. In [1], a PC-based VR training
simulator is developed to improve risk awareness for
underground miners. After identifying one of the
hazards, trainees are required to select appropriate
corrective actions from on-screen button icons. But the
training simulator pays little attention to the modelling of
human risk behaviours. In [7], a generic safety training
tool, SAFE-VR, is introduced. One of the key components
within the SAFE-VR world is the Hazards constructed by
oriented-object technology. In SAFE-VR, the Hazards are
inactive objects and are not suitable for modelling human
behaviours. In [8], a VR based safety training program is
presented for instructing the safety procedures and
guidelines of underground conveyor belts. However, the
proposed training program doesn’t involve modelling
human risk behaviours. Other existing research [2] also
focuses on geometric presentation or real-time dynamics
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