Introduction
FIRE
and EVACUATE
. In the FIRE
situation, fire is observed due to smoke at some place on the platform, and all workers need to muster to their primary muster station. In the EVACUATE
situation, the fire is escalated so that some escape routes to the primary muster station are blocked and all personnel needs to muster at the lifeboat or alternative muster station. The purpose of this work is to have a model that can be used by a software agent so that the agent can exhibit human-like situation awareness (SA). Such agents can subsequently be used, for example, in training simulators to enrich trainees’ experience by showing them various scenarios in which the agent shows recognition of different situations (to makes various decisions). A participant can learn from the agent what information is important in a given scenario for correct SA.fireEscalates(oil, water)
. STO satisfies many characteristics of Endsley’s SA model, and it was implemented in the Web Ontology Language (OWL) using the full profile (OWL-Full). Now that OWL changed in 2009 and the support for OWL-Full, which is required to fulfill the theoretical requirements of Barwise and Devlin’s approach to situation modeling, is unavailable, STO is difficult for use as a platform for modeling SA.[Cat]
→ (On)
→ [Mat]
, where Cat
and Mat
are two concepts (each for one object/individual in the real world) related to each other by the relation On
. Sowa [61], and Akman and Surav [1] say that both context and situation are the same notions. Kokar et al. [32] report that contexts (situations) in AI are dealt with using predicates such as isa(c, p)
to mean that the proposition p
holds true
in the context c
.Previous works
A method to model situation awareness
Markov Logic Network
true
or false
. Formally, an MLN L is defined as a set of pairs (Fi, wi) with Fis being the formulas and wis being the weights assigned to the formulas.true
groundings of Fi in x, x[i] is the state or configuration (i.e., the truth assignments) of the predicates in Fi, and \(\phi_{i} \left( {x_{\left[ i \right]} } \right) = e^{{w_{i} }}\).The FIRE
and EVACUATE
emergency situations
FIRE
situation, and it will end when an all-clear alarm sounds, which means that the fire has been taken care of and the people can now return to their duties. In case a FIRE
situation escalates, meaning that the fire spreads and blocks various paths so that personnel’s safety could be further compromised, an EVACUATE
situation may come into effect, and this new situation is communicated to people by another alarm, different from the fire alarm. In the EVACUATE
situation, people must report to their designated secondary muster station, the lifeboat station, from where the final evacuation from the platform can proceed.Knowledge representation of emergency situations
FIRE
and EVACUATE
emergency situations” section, the FIRE
situation, which asks all personnel to move to the primary muster station, and the EVACUATE
situation, which involves escalation of a fire into a larger fire that obstructs the primary escape-route leading to the primary muster station, thereby necessitating re-routing to the alternative or lifeboat station. A set of FOL rules are proposed in Table 2 so that an agent recognizes these situations like the way a human counterpart recognizes them. The preconditions (antecedents of FOL rules) used here are common among experts and have been suggested in earlier studies [8, 18, 49, 55, 57, 62, 64‐66, 68]. The query predicates determine the probability of recognizing alarms, having a FIRE
situation, having an EVACUATE
situation, and having some (unknown) situation given the evidence predicates.Variables | Predicate name | Parameter types | Description |
---|---|---|---|
Listens | L
| (agent, alarm, time) | An agent listens to an alarm during time interval time |
Recognizes | R
| (agent, alarm, time) | An agent recognizes an alarm during time |
HasIntentToReach | HITR
| (agent, musterLoc, time) | An agent has intention to reach a muster location during time |
HasEmrgSit | HES
| (agent, emgSitType, time) | An agent has an emergency situation during time |
SeesThreat | ST
| (agent, threatType, time) | An agent sees a threat during time |
HasFocusOn | HFO
| (agent, pa, time) | An agent has focus on a PA during time |
HasSomeEmrgSit | HSES
| (agent) | An agent gets a sense of some emergency situation |
FollowsPA | FPA
| (agent, pa, time) | An agent understands and follows a PA during time |
KnowsEmrgTypeOfAlarm | KETA
| (emgSitType, alarm) | An agent knows which alarm is used in a given emergency type |
KnowsEmrgTypeOfThreat | KETT
| (threatType, emgSitType) | An agent knows which threat type would give rise to a particular emergency situation |
KnowsEmrgTypeOfPA | KETPA
| (pa, emgSitType) | An agent knows what emergency situation is being announced in PA |
BeforeSeeingThreat | BST
| (agent, alarm, time) | BST is paired with HITR with logical ‘and’ connective to mean that HITR is true only when the agent has determined the muster location before seeing a threat |
# | Rules |
---|---|
1
| ¬L(ag,al,t) ⇒ ¬R(ag,al,t). |
2
| L(ag, + al,t)^HITR(ag, + mloc,t)^BST(ag, + al,t) ⇒R(ag, + al,t) |
3
| L(ag,al,t) ⇒HSES(ag) |
4
| ST(ag,thrt,t) ⇒HSES(ag) |
5
| (HFO(ag, + p_a,t)^FPA(ag, + p_a,t)^KETPA(+p_a, + eTyp)) v (ST(ag, + thrt,t)^KETT(+thrt, + eTyp)) v (L(ag, + al,t)^HITR(ag, + mloc,t)^KETA(+al, + eTyp)^BST(ag, + al,t)) ⇒ HES(ag, +eTyp, t) |
6
| HES(ag,FIRE,t) ⇒ ¬HES(ag,EVACUATE,t) |
7
| HES(ag,EVACUATE,t) ⇒ ¬HES(ag,FIRE,t) |
8
| HES(ag,FIRE,t0)^HES(ag,EVACUATE,t1)^Gt(t1,t0) ⇒¬HES(ag,FIRE,t1) |
Reasoning
mloc
that takes values from the set {MESSHALL, LIFEBOAT}
. Literature shows that intention is an important cognitive state that affects one’s ability to participate in a decision-making process [7, 64]. Intention is modeled here as a predicate HITR
that takes a value true
if the agent develops the intention to move to mloc
during a time interval t
. An agent’s intention can be inferred by observing which route is taken up immediately after listening to the alarm. The agent can also be delayed in developing the intention to reach mloc
and may require other cues for building up this intention. Therefore, to know if an alarm is recognized without the help of other cues, such as observing smoke, it is necessary to know when the agent develops the intention of moving to the required muster station after listening to an alarm. HITR
is used in conjunction with the predicate BST
that ensures the intention of moving to the muster location is developed before seeing a threat because if an agent sees a threat, it would be unclear if its intention of moving to mloc
is due to the threat or the alarm. The probability of recognizing the alarm is determined by using the conjunction of the three predicates. If any of the antecedent predicates fail, the chances of recognizing the alarm will be reduced.ST
(see Table 1) is used to indicate that the agent observes a threat. An agent who sees a threat (such as smoke or blowout) is highly likely to discover the type of emergencies involved (FIRE
or EVACUATE
). Rules # 3 and 4 say that an agent will be aware of ‘some’ emergency if it just listens to an alarm or observes a threat.HFO
is true
when the agent has a focus on a PA being uttered. An agent that is engaged in all activities except what is communicated in the PA is defined to have no focus, whereas one that suspends its current engagements and begins performing the actions according to the PA is considered to have focused on the PA. Similarly, if an agent, while moving, suddenly changes its course because of instructions given in the PA a moment before, this also considered to have exhibited a clear sign of responding to the PA. In general, gestures can be noticed to determine if an agent has a focus on an ongoing PA or not. The predicate FPA
is used to demonstrate the requirement of following the PA. If HFO
is true,
but FPA
is false,
it means that, though the agent had focused on the PA’s words, it is confused or does not have an understanding of the situation, and therefore, the agent is unable to follow the PA. Rule#5 is a disjunction of three different rules: the first determines SA about the emergency based on focus and understanding of PA, the second uses direct exposure to the threat/hazard, and the third is based on the recognition of alarms. This last disjunct in rule#5 uses the predicate KETA
to link an alarm to the corresponding situation or emergency type because that is needed to conclude in the consequent predicate HSES
. Rules # 6 & 7 are to ensure that FIRE
and EVACUATE
are two distinct types of situations, besides that EVACUATE
may occur because of a fire [8, 62].t0
a FIRE situation is observed, and during some later interval t1 (where t0
\(\prec\) t1
) this situation escalates to EVACUATE, then the FIRE situation will no longer exist during t1, although one may witness real fires during the EVACUATE situation.Case studies: SA during offshore emergency scenarios
Situations in experimental scenarios
GPA
) followed by the relevant PA is made right after the initial fire event. The escalation of the fire in the galley to fire in the mess hall is then announced by a Prepare to Abandon Platform Alarm (PAPA
), followed by another PA. Initially, a participant is situated in their cabin (see the floor map in Fig. 3-1) when a GPA
alarm activates, followed by a platform announcement. The PA announcement directs the participant to muster at their designated muster station, which is the mess hall on A-deck for a FIRE
situation. Upon hearing the GPA
, the participant needs to move out of the cabin and choose from the primary route (the solid lines, which goes through the main stairwell), or the secondary escape route (the dotted lines, which uses the external stairwell) to reach A-deck. The participants were trained to deal with these situations earlier using escape route training videos and instructions in the training session S1. While moving toward the mess hall, after a fixed interval of time t0
, the participant receives a call to abandon the platform. This is the PAPA
alarm, which indicates to the participants that they should immediately move to the secondary or alternative muster location, which is the lifeboat station at the starboard side of the platform (see Fig. 3-2). The time interval when PAPA
is activated to the end of a scenario is termed t1
. Thus, t0
is the time interval in which the participants get all cues related with the FIRE emergency, such as smoke in the stairwell, GPA
alarm, and PA announcement that includes the words “fire in the galley”. Similarly, t1
is the time interval that starts when t0
expires and ends at the end of the scenario. During the t1
period, the participant receives cues related with an EVACUATE
situation. The PAs use clear words as to what needs to be done in an emergency and what parts of the escape route are expected to be blocked due to fire or smoke. Although GPA
and PAPA
are activated at different times, indicating two different situations, the other environmental cues can be observed at any time during their lifetimes. For example, smoke in the main stairwell is considered as a cue for a FIRE
situation. Some participants reached at this spot in the main stairwell after the PAPA
was activated. Situations like these are complex because of confusion due to conflicting cues.
Data set for training and testing the model
Empirical data set (D1)
Predicates | Parameters | |
---|---|---|
P1G1 | P2G1 | |
L(agent, alarm, time)
| Y: P1G1, GPA,t0;
Y: P1G1, PAPA,t1;
| Y: P2G1, GPA, t0
Y: P2G1, PAPA, t1
|
HML(alarm,musterlocation)
| Y: GPA, MSH;
Y: PAPA, LFB;
| Y: GPA, MSH
Y: PAPA, LFB
|
HITR(agent,musterlocation,time)
| Y: P1G1,MSH, t0;
Y:P1G1, MSH, t1;
Y:P1G1, LFB, t1;
N: P1G1, LFB,t0;
| Y: P2G1,MSH, t0
Y: P2G1,LFB, t1
|
R(agent,alarm,time)
| Y: P1G1, GPA, t0;
N: P1G1, PAPA, t1;
| Y: P2G1, GPA, t0
Y: P2G1, PAPA, t1
|
HSES(agent)
| Y: P1G1;
| Y: P2G1
|
ST(agent,threat,time)
| Y: P1G1, SMK_MSHA, t1;
Y: P1G1, SMK_STAI, t1;
Y: P1G1, SMK_VENT, t1
| Y:P2G1,SMK_VENT,t0
|
HES(agent,emergencyType,
time)
| Y: P1G1, FIRE, t0;
Y: P1G1, FIRE, t1;
Y: P1G1, EVACUATE, t1
| Y: P2G1,FIRE, t0
Y: P2G1,EVACUATE,t1
|
HFO(agent,PA,time)
| Y: P1G1, PA_GPA, t0;
N: P1G1, PA_PAPA, t1
| Y: P2G1,PA_GPA,t0
Y: P2G1,PA_PAPA,t1
|
FPA(agent,PA,time)
| Y: P1G1, PA_GPA, t0;
Y: P1G1, PA_PAPA,t1;
| Y:P2G1,PA_GPA,t0
Y: P2G1, PA_PAPA,t1
|
KETA(alarm,emergencyType)
| Y: GPA, FIRE;
Y: PAPA, EVACUATE;
| Y: GPA, FIRE
Y: PAPA, EVACUATE
|
KETT(threat, emergencyType)
| Y: SMK_VENT,EVACUATE;
Y: SMK_STAI,FIRE;
Y: SMK_MSHA, EVACUATE;
| Y: SMK_VENT,EVACUATE
Y: SMK_STAI,FIRE
Y:SMK_MSHA,EVACUATE
|
KETPA(PA, emergencyType)
| Y: PA_GPA, FIRE;
Y: PA_PAPA,EVACUATE;
| Y: PA_GPA, FIRE
Y: PA_PAPA, EVACUATE
|
Greater(time,time)
| Y: t1, t0;
| Y: t1, t0
|
Empirical dataset (D2)
Setting up the model
KETA
, KETT
, and KETPA
. The predicates KETA
, KETT
, and KETPA
employ open world assumption because these predicates are designed to be present in the model as a container for the background knowledge. KETA
is true
when the agent has knowledge about which alarm is for which emergency situation type, i.e., the fact that the GPA
alarm sounds for the FIRE
type emergency, and the PAPA
alarm is activated for EVACUATE
type. KETT
is used to mean which type of threat is observed in an emergency. For example, a fire confined to a small area, at most, could mean to move to the primary muster station. Three types of threats are considered in this study. The threat smoke in the stairwell (SMK_STAI
) should be recognized as a FIRE type emergency. If an agent sees smoke coming out of the mess hall vent (SMK_VENT
), or the agent enters into the mess hall and sees smoke (SMK_MSHA
) there, it means the situation is of type EVACUATE
because the primary muster station is compromised. If KETT
is true,
it means that the agent knows the relationships between a threat and possible type of emergency situation that could originate from this threat. Similarly, the KETPA
predicate is true
if the agent knows which words in the PA would lead to a particular emergency type. For example, the sentences, “a fire in the galley” or “move to primary muster station” mean that the emergency type is FIRE
. On the other hand, the words, “primary escape route is blocked” or “a fire has escalated” mean that the situation is EVACUATE
. This knowledge was given to the participants of Smith’s experiment as part of the training curriculum. Therefore, during training of the model the truth values of KETA
, KETT
, and KETPA
are taken as true
to mean that the agents based on the proposed model have this background knowledge.Calculating the model weights
R
, HES
and HSES
. The model is trained separately for data sets D1 and D2 using a discriminative learning method so that weights can be assigned to the rules presented in Table 2. It was observed that some participants did not listen to an alarm even though it was audible. The use of Listens
(L
) as a predicate came up (see Table 2) with the empirical observations, where, with some participants the predicate takes a false
value. On the other hand, if Hears were used instead of Listens
, then there would not be any case with a false
value for Hears because all the participants had hearing abilities in the normal range. Similar considerations were taken for other rules. Table 4 shows the weights. A portion of ground MN obtained by grounding the rules#2–5 is depicted in Fig. 4, which shows how the nodes corresponding to each predicate are related.# | Rules |
w
D1
|
w
D2
|
---|---|---|---|
1 | ¬L(ag,al,t) ⇒ ¬R(ag,al,t). | ∞ | ∞ |
2 | L(ag,GPA,t)^HITR(ag,MSH,t)^BST(ag, + al,t) ⇒ R(ag, + al,t) | 2.09 | 1.95 |
3 | L(ag,PAPA,t)^HITR(ag,MSH,t)^BST(ag, + al,t) ⇒ R(ag, + al,t) | 0.57 | 0.15 |
4 | L(ag,PAPA,t)^HITR(ag,LFB,t)^BST(ag, + al,t) ⇒ R(ag, + al,t) | 2.66 | 2.71 |
5 | L(ag,al,t) ⇒ HSES(ag) | 1.27 | 1.4 |
6 | L(ag,al,t)^ ¬R(ag,al,t) ⇒ HSES(ag) | 0.32 | 0.36 |
7 | ST(ag,thrt,t) ⇒ HSES(ag) | 0.93 | 1.01 |
8 | (HFO(ag,PA_GPA,t)^FPA(ag, + p_a,t)^KETPA(+p_a,FIRE))v(ST(ag,SMK_VENT,t)^KETT(+thrt, + eTyp))v(L(ag,GPA,t)^HITR(ag,MSH,t)^ KETA(+al, + eTyp)^BST(ag, + al,t)) ⇒ HES(ag, + eTyp,t) | 0.30 | 0.30 |
9 | (HFO(ag,PA_GPA,t)^FPA(ag, + p_a,t)^KETPA(+p_a,FIRE))v(ST(ag,SMK_VENT,t)^KETT(+thrt, + eTyp))v(L(ag,GPA,t)^HITR(ag,LFB,t)^ KETA(+al, + eTyp)^BST(ag, + al,t)) ⇒ HES(ag, + eTyp,t) | 0.20 | 0.25 |
10 | HES(ag,FIRE,t) ⇒ ¬HES(ag,EVACUATE,t) | 1.38 | 1.47 |
11 | HES(ag,EVACUATE,t) ⇒ ¬HES(ag,FIRE,t) | 1.38 | 1.47 |
12 | HES(ag,FIRE,t0)^HES(ag,EVACUATE,t1)^Gt(t1,t0) ⇒ ¬HES(ag,FIRE,t1) | − 1.54 | − 0.45 |
Results and discussion
true
in the present conditions. The most important things an agent seeks in an evolving emergency are the recognition of alarms and determination of the type of emergency it is in at a given time. For this reason, the query predicates are obtained by grounding the following predicates:R
is read as the agent
, ag
, recognizes an alarm
, al
, during the time
interval t
. HES
means that the agent
, ag
, has an emergency, e
, of type emgSitType
, during time
t
, and the predicate HSES
represents an agent
, ag
, who has got some sense of emergency. If in any case, the truth value of HSES
is true
and HES
is false,
it would mean that the agent is unable to determine the type of emergency despite that it has sensed the emergency situation. The predicates obtained after grounding the predicates listed in Table 2 other than the query predicates mentioned in (5) are used as part of the evidence predicates that need to be provided to the inference engine to obtain the results of the queries presented in (5).# | Evidence | Empirical result | Model output probability | |
---|---|---|---|---|
1. | 1 | L(P1G1, GPA, t0)
| R(P1G1, GPA, t0)
| 0.91 |
2 | HITR(P1G1, MSH, t0)
| HES(P1G1, FIRE, t0)
| 0.92 | |
3 | BST(P1G1, GPA, t0)
| HES(P1G1, FIRE, t1)
| 0.74 | |
4 | HITR(P1G1, MSH, t1)
| ¬ HES(P1G1, EVACUATE,t1) | 0.16 | |
5 | ST(P1G1, SMK_MSHA, t1)
| ¬ HES(P1G1, EVACUATE,t0) | 0.12 | |
6 | ST(P1G1, SMK_STAI, t1)
| HSES(P1G1)
| 0.99 | |
7 | ST(P1G1, SMK_VENT, t1)
| ¬ R(P1G1, PAPA, t1) | 0.0 | |
8 | HFO(P1G1, PA_GPA, t0)
| |||
9 | FPA(P1G1, PA_GPA, t0)
| |||
10 | ¬ L(P1G1, PAPA, t1) | |||
11 | ¬ BST(P1G1, PAPA, t1) | |||
12 | ¬ HFO(P1G1, PA_PAPA, t1) | |||
13 | ¬ FPA(P1G1, PA_PAPA, t1) | |||
14 | HITR(P1G1, LFB, t1)
| |||
2. | 1 | L(P2G1, GPA, t0)
| R(P2G1, GPA, t0)
| 0.87 |
2 | HITR(P2G1, MSH, t0)
| HES(P2G1, FIRE, t0)
| 0.94 | |
3 | BST(P2G1, GPA, t0)
| ¬ HES(P2G1, FIRE, t1) | 0.29 | |
4 | ST(P2G1, SMK_VENT, t0)
| R(P2G1, PAPA, t1)
| 0.92 | |
5 | HFO(P2G1, PA_GPA,t0)
| HES(P2G1, EVACUATE,t1)
| 0.98 | |
6 | FPA(P2G1, PA_GPA, t0)
| ¬ HES(P2G1, EVACUATE,t0) | 0.07 | |
7 | L(P2G1, PAPA, t1)
| HSES(P2G1)
| 0.98 | |
8 | HFO(P2G1, PA_PAPA, t1)
| |||
9 | FPA(P2G1, PA_PAPA, t1)
| |||
10 | BST(P2G1, PAPA, t1)
| |||
11 | HITR(P2G1, LFB, t1)
| |||
3. | 1 | L(P3G1, GPA, t0)
| ¬ R(P3G1, GPA, t0) | 0.49 |
2 | ¬ HITR(P3G1,MSH,t0) | ¬ HES(P3G1, FIRE, t0) | 0.44 | |
3 | ¬ BST(P3G1, GPA, t0) | ¬ HES(P3G1, FIRE, t1) | 0.15 | |
4 | ST(P3G1, SMK_VENT, t0)
| R(P3G1, PAPA, t1)
| 0.93 | |
5 | ¬ HFO(P3G1,PA_GPA, t0) | HES(P3G1, EVACUATE,t1)
| 0.99 | |
6 | ¬ FPA(P3G1, PA_GPA, t0) | ¬ HES(P3G1, EVACUATE,t0) | 0.24 | |
7 | L(P3G1, PAPA, t1)
| HSES(P3G1)
| 0.90 | |
8 | HFO(P3G1, PA_PAPA, t1)
| |||
9 | FPA(P3G1, PA_PAPA, t1)
| |||
10 | BST(P3G1, PAPA, t1)
| |||
11 | HITR(P3G1, LFB, t1)
| |||
4. | 1 | L(P1G2,GPA,t0)
| R(P1G2, GPA, t0)
| 0.88 |
2 | HITR(P1G2,MSH,t0)
| HES(P1G2, FIRE, t0)
| 0.88 | |
3 | BST(P1G2,GPA,t0)
| ¬ HES(P1G2, FIRE, t1) | 0.08 | |
4 | HITR(P1G2,MSH,t1)
| R(P1G2, PAPA, t1)
| 0.94 | |
5 | ST(P1G2, SMK_VENT, t0)
| HES(P1G2, EVACUATE,t1)
| 0.98 | |
6 | ST(P1G2, SMK_VENT, t1)
| HSES(P1G2)
| 0.99 | |
7 | HFO(P1G2, PA_GPA, t0)
| HES(P1G2, EVACUATE,t0)
| 0.15 | |
8 | FPA(P1G2, PA_GPA, t0)
| |||
9 | L(P1G2, PAPA, t1)
| |||
10 | HFO(P1G2, PA_PAPA, t1)
| |||
11 | FPA(P1G2, PA_PAPA, t1)
| |||
12 | HITR(P1G2, LFB, t1)
| |||
13 | BST(P1G2,PAPA,t1)
| |||
5. | 1 | L(P2G2,GPA,t0)
| R(P2G2,GPA,t0)
| 0.87 |
2 | HITR(P2G2,MSH,t0)
| ¬ R(P2G2, GPA, t1) | 0.0 | |
3 | BST(P2G2, GPA, t0)
| ¬ R(P2G2, PAPA, t0) | 0.0 | |
4 | HITR(P2G2,MSH,t1)
| ¬ R(P2G2,PAPA,t1) | 0.49 | |
5 | ST(P2G2,SMK_MSHA,t1)
| HES(P2G2,FIRE,t0)
| 0.93 | |
6 | ST(P2G2,SMK_VENT,t1)
| HES(P2G2,FIRE,t1)
| 0.52 | |
7 | ST(P2G2,SMK_STAI,t1)
| ¬ HES(P2G2,EVACUATE,t0) | 0.06 | |
8 | HFO(P2G2,PA_GPA,t0)
| ¬ HES(P2G2,EVACUATE,t1) | 0.47 | |
9 | FPA(P2G2,PA_GPA,t0)
| HSES(P2G2)
| 0.99 | |
10 | L(P2G2,PAPA,t1)
| |||
11 | ¬ BST(P2G2,PAPA,t1) | |||
12 | ¬ HFO(P2G2,PA_PAPA,t1) | |||
13 | ¬ FPA(P2G2,PA_PAPA,t1) | |||
14 | HITR(P2G2,LFB,t1)
| |||
6. | 1 | L(P3G2,GPA,t0)
| ¬ R(P3G2,GPA,t0) | 0.5 |
2 | ¬ HITR(P3G2,MSH,t0) | ¬ R(P3G2,GPA, t1) | 0.0 | |
3 | ¬ BST(P3G2,GPA,t0) | ¬ R(P3G2,PAPA,t0) | 0.0 | |
4 | ¬ ST(P3G2,SMK_MSHA,t0) | R(P3G2,PAPA,t1)
| 0.91 | |
5 | ST(P3G2,SMK_VENT,t1)
| HES(P3G2,FIRE,t0)
| 0.99 | |
6 | ST(P3G2,SMK_STAI,t1)
| ¬ HES(P3G2,FIRE,t1) | 0.13 | |
7 | HFO(P3G2,PA_GPA,t0)
| HES(P3G2,EVACUATE,t1)
| 0.98 | |
8 | FPA(P3G2,PA_GPA,t0)
| HSES(P3G2)
| 0.99 | |
9 | L(P3G2,PAPA,t1)
| ¬ HES(P3G2,EVACUATE,t0) | 0.05 | |
10 | HFO(P3G2,PA_PAPA,t1)
| |||
11 | BST(P3G2,PAPA,t1)
| |||
12 | FPA(P3G2,PA_PAPA,t1)
| |||
13 | HITR(P3G2,LFB,t1)
| |||
7. | 1 | L(P4G2,GPA,t0)
| R(P4G2,GPA,t0)
| 0.87 |
2 | HITR(P4G2,MSH,t0)
| HES(P4G2,FIRE,t0)
| 0.93 | |
3 | BST(P4G2,GPA,t0)
| HES(P4G2,FIRE,t1)
| 0.56 | |
4 | HITR(P4G2,MSH,t1)
| ¬ R(P4G2,PAPA,t1) | 0.49 | |
5 | ST(P4G2,SMK_MSHA,t1)
| ¬ HES(P4G2,EVACUATE,t1) | 0.47 | |
6 | ST(P4G2,SMK_VENT,t1)
| HSES(P4G2)
| 0.99 | |
7 | ST(P4G2,SMK_STAI,t1)
| |||
8 | HFO(P4G2,PA_GPA,t0)
| |||
9 | FPA(P4G2,PA_GPA,t0)
| |||
10 | L(P4G2,PAPA,t1)
| |||
11 | ¬ BST(P4G2,PAPA,t1) | |||
12 | HFO(P4G2,PA_PAPA,t1)
| |||
13 | ¬ FPA(P4G2,PA_PAPA,t1) | |||
14 | HITR(P4G2,LFB,t1)
|
t0
that starts from the beginning of a session until the time when the GPA
alarm stops. The second interval is termed t1
, which is the interval that follows immediately after t0
ends, and it ends at the end of each session. t0
covers the period when there is FIRE
type emergency, and t1
covers the duration when there is EVACUATE
type emergency. This division of time is important to assess the importance of cues relevant to each emergency type. For example, if an agent observes smoke in the central stairwell, then this is an important cue for FIRE
type emergency because in that case, the agent should move to the primary muster station, the mess hall. On the other hand, smoke in the central stairwell should not be considered during t1
, or when the PAPA
alarm sounds, because PAPA
alarm is a call to gather at the secondary, or alternative muster station, the LIFEBOAT station. Often in such cases, the primary muster station may have been compromised, or the routes that lead to the primary muster station may have been blocked.Simulation results against the participant P1G1
t0
had already expired. On the other hand, this also means that P1G1 recognized the GPA
alarm, R(P1G1, GPA, t0)
, and developed awareness about the FIRE situation, HES(P1G1, FIRE, t0)
, during the initial time interval t0
. But as a slow mover, P1G1 observed the smoke in the stairwell, mess hall, and the smoke coming through the mess hall ventilation during t1
. P1G1 also did not pay attention to the PAPA
alarm, which is the reason for ¬L(P1G1, PAPA, t1)
, which was activated when P1G1 was still in the main stairwell. P1G1 took about 20 s more in t1
, ignoring the fact that the PAPA
alarm implies a re-route towards the lifeboat station through the secondary escape route. So, unnoticed from the PAPA
alarm and the relevant PA, P1G1 entered the mess hall and saw thick smoke. Studies [47, 54] suggest that humans show dominance on visual information than on other types of sensory cues such as auditory information. Observing smoke drew the P1G1’s attention on smoke, and he instantly realized a need to move out of the mess hall, which was done by re-routing to the lifeboat. But this realization of the situation comes only when P1G1 saw smoke, and it was not due to the PAPA
alarm or the relevant PA. In a real situation, entering an area filled with smoke due to fire or any other toxic element could be lethal. Also, observing a fire or smoke is a natural cue that would develop awareness about a fire situation. It is, nevertheless, hard to develop awareness about an evacuation situation by watching a fire or smoke unless the relevant alarms and/or platform announcements are heard and recognized. This is the reason why P1G1, although mustered at the lifeboat station, is considered to be poor in responding to the evacuation situation, and that is why we have ¬R(P1G1, PAPA, t1)
and ¬HES(P1G1, EVACUATE, t1)
in the empirical results for P1G1. Similarly, P1G1 spent a fraction of the interval t1
maintaining the impression of a fire situation, although the fire situation had already been escalated to an evacuation situation, which is why we have a predicate HES(P1G1, FIRE, t1)
in the empirical results. The model output is probabilities obtained against the query predicates, as shown in the last column of Table 5.true
. Similarly, a low output probability should serve a good fit for the queries predicate when its empirical truth value is false
. This is very much evident for P1G1. Given the listed evidence for P1G1, the probability that an agent would recognize a GPA
is 0.91, and the probability the same agent would get immediate fire emergency awareness is 0.92. However, there are fewer chances (only 16%) that the agent would respond to the escalating situation from FIRE
to EVACUATE
because the likelihood of recognition of the PAPA
alarm is zero, as the agent does not listen to or has no focus on the sounding alarm. In any case, if we change the evidence truth value for the predicate 1.10 in Table 5 from false
to true
, the corresponding probability of recognizing PAPA
during t1
would increase from 0.0 to 0.48. The reason for getting a zero probability is due to the hard constraint (rule#1) listed in Table 4. Similarly, if P1G1 realized the presence of smoke in the stairwell during t0
rather than t1
, for example, if P1G1 had moved fast, then the chances for having a FIRE
situation during t1
would have been lowered from 0.74 to 0.46, and the chances for getting awareness about the EVACUATE
situation would be increased from 16 to 23% during t1
. This is because the SMK_STAI
, i.e., seeing smoke in the stairs, is a positive cue for a fire situation, but when one observes it in the presence of a cue that is for an evacuation situation, for example, a PAPA
alarm, the two conflicting cues would cause confusion, and the agent needs to decide which cue should be considered. P1G1 preferred SMK_STAI
during t1
over the PAPA
alarm and so entered the mess hall, although this decision was wrong as it wasted egress time and exposed the participant to a hazard.Simulation results against the participant P2G1
HES(P2G1, FIRE, t1)
, was false
. The rest of the model output probabilities, estimated for modeling P2G1’s behavior, are reasonable.Simulation results against the participants P3G1 and P1G2
t0
was the smoke coming out from the mess hall ventilation. P3G1 did not recognize the GPA
alarm nor heed the PA for the FIRE
emergency. P3G1 never had any intention to move to the mess hall. The model output for recognizing the GPA
alarm (0.49) during t0
is reasonable because the time when the GPA
starts sounding is the time when the participant is in the cabin, and there are no other available cues except the alarm sound and the relevant PA. The model output probabilities are in good agreement with the empirical results except for a slightly larger value of 0.44 for the probability of having awareness about FIRE
emergency during t0
, whereas P3G1 remained unaware about the fire emergency, and from the beginning of the scenario P3G1 had decided to muster at the LIFEBOAT station. The results obtained against the evidence for the participant P1G2 are all in good agreement with the empirical values.Simulation results against the participant P2G2
t0
with 0.87 probability. P2G2 did not recognize the PAPA
during the experiment, and the model output is 0.49 for the predicate R(P2G2, PAPA, t1)
. The reason for having a probability near 0.5 is that when the interval shifted from t0
to t1
, there are only two cues suggesting that the situation has escalated from FIRE
to EVACUATE
(smoke from the vents and the smoke in the mess hall) and the smoke in the stairwell is a cue for moving to the mess hall. This is a conflicting situation. Moreover, as P2G2 moved into the mess hall while the PAPA
alarm was still on along with the relevant PA, the predicate BST(P2G2, PAPA, t1)
takes a false
value in the evidence that reduced the probability of recognizing PAPA
during t1
from 0.94 (if BST(P2G2, PAPA, t1)
is true
) to 0.49 when the predicate BST is false
, as in the case of P2G2. Similar reasoning is true
for recognizing the FIRE
and EVACUATE
situations during t1
. If we set BST(P2G2, PAPA, t1)
true
in the evidence dataset for P2G2, then the new values for probabilities for having awareness about FIRE
and EVACUATE
situations during t0
and t1
come out to be 0.94 for a FIRE
at t0
and 0.96 for EVACUATE
at t1
. This shows the importance of recognizing the alarm before seeing any real threat.Simulation results against the participants P3G2 and P4G2
GPA
alarm, and the model probability against the query predicate is 0.5 for similar reasons we observed in the case of P3G1. The rest of the results for P3G2, as reported in Table 5, support the empirical results for P3G2. Similar reasons are there for the results obtained against the query predicates for P4G2.