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Utilizing Emotions in Strategic Real-Time Artificial Intelligence


Abstract and Figures

Strategic real-time systems are of high poten-tial and their applications are growing, al-though they are mostly prevalent in video games, military training, and military plan-ning. We propose a paradigm to advance current systems by introducing emotions into the simulated agents that make decisions and solve situations cooperatively. By utilizing emotional reactions and communication, we hope to advance these systems so that the de-cision process better mimics human behavior. Since our system allows sharing of emotions with nearby agents it utilizes both internal and external emotional control.
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Utilizing Emotions in Strategic Real-Time Artificial Intelligence
Megan Olsen
Kyle Harrington
Hava Siegelmann
University of Massachusetts, Department of Computer Science, 140 Governors Drive, Amherst, MA 01003
Strategic real-time systems are of high poten-
tial and their applications are growing, al-
though they are mostly prevalent in video
games, military training, and military plan-
ning. We propose a paradigm to advance
current systems by introducing emotions into
the simulated agents that make decisions and
solve situations cooperatively. By utilizing
emotional reactions and communication, we
hope to advance these systems so that the de-
cision process better mimics human behavior.
Since our system allows sharing of emotions
with nearby agents it utilizes both internal
and external emotional control.
1. Introduction
Real-Time Artificial Intelligence has been investigated
for over a decade (Musliner et al., 1995). A system
is considered to be a Real-time AI system if it is able
to make decisions within a guaranteed response time
and thus meet domain deadlines. These systems face
many challenges, including working with partial infor-
mation, choosing the most crucial action if there are
multiple scenarios to react to, and working continu-
ously for an extended period of time without failure.
These syste ms are usually created as expert systems,
as they are used for a specific domain. However, they
should be able to handle a vast majority of scenarios
that occur, not just the specified test scenarios. The
results must be returned in a timely fashion (Musliner
et al., 1995).
Real-Time Strategy (RTS) is an offshoot of general
purp ose real-time AI. RTS refers specifically to sys-
tems where the primary purpose is to create strategy,
Presented at North East Student Colloquium on Artificial
Intelligence (NES CAI), 2008. Copyright the authors.
usually in a competitive atmosphere. For instance,
military training on how to engage the enemy done
via simulation is a RTS system. Only training that
with a computer strategy aspect is included however,
since it is not a RTS system if only the human con-
trols strategy. Currently the military uses simulations
heavily for training, and therefore it is crucial for them
that these systems advance (Herz & Macedonia, 2002).
Also, many popular video games such as Starcraft and
Warcraft incorporate Real-Time Strategy if at least
one player is the computer. These games all simulate
war among multiple players in which all but at least
one player may be computer controlled. Although ad-
vances may be made in the AI of these systems, they
do not see m to influence the military training develop-
ment. However, many groups are working to combine
the two groups so that meaningful work can be done to
advance both fields at once (Herz & Mace donia, 2002;
Buro, 2004). Ideally, the creation of war-related video
games will be able to influence the military training
simulations in years to come (Buro & Furtak, 2003).
Although they may at first seem unrelated, emotions
can play a large part in strategy especially when time
is limited. Emotions are believed to improve our re-
sponse time, increase our memory capacity, and pro-
vide quick communication (Rolls, 2005). We are able
to notice things that we fear quicker than things we
enjoy or are indifferent about, showing fear to be cru-
cial to our response time. Remembering an emotion
may enable a memory to be more useful for us later,
as we can react to the emotion of the experience with-
out needing to remember all of its details. Emotions
help us convey our experience to another person; for
instance, they will realize danger quicker from notic-
ing our fear than by hearing our explanation. Thus,
we propose to include emotion with RTS algorithms
to enhance our strategy.
Our system utilizes a current RTS gaming engine as
well as its included AIs. We will provide emotions for
the game’s units (agents), and determine how those
emotions affect the game play. We anticipate that
emotions will enhance the ability of the agents to react
to their environment and influence other agents, thus
increasing the performance of the AI. One of our main
contributions is the creation of an Emotion Map that
enables agents to communicate their emotions with
any surrounding agents without direct contact. This
Emotion Map saves the emotion of agents and diffuses
it for a period of time, enabling neighboring agents to
feel the emotion of their peers.
2. Related Work
Many exciting advances in computer science are sys-
tems that must function in real time. For example, a
model of ship damage control has been created that
relies on real time decision making. This model deter-
mines the best course of action given the state of the
ship and its many control systems. Tested in a simu-
lation environment against actual Navy captains, the
model vastly outp erformed the humans. This example
shows that Real-Time AI can even be valuable in situ-
ations where humans are already available to perform
the task (Bulitko & Wilkins, 2001).
One type of real time strategy system is the RTS game,
which can tackle many different fundamental AI issues.
For instance, game AI is closely related to adversarial
real-time planning, decision making under uncertainty,
opponent modeling, spatial and temporal reasoning,
resource management, collaboration, and pathfinding
(Buro & Furtak, 2004). One system that is working to
improve gaming in all of these aspects is ORTS. This
system is an open source game that is utilized in a
competition each summer to encourage AI experts to
test their skills and create software with a usable com-
bination of solutions. Although we will use a similar
system called “Globulation,” our enhancements could
also be applied to ORTS.
Another way to create an RTS game is by controlling
characters in gam es such as Quake. Laird et al. creates
bots that can strategize through first person shooter
games to beat human players. They create their bots
using real-time AI algorithms, giving them the ability
to anticipate another player’s action, make smart de-
cisions on where to go, and make smart decisions on
what actions to take. This type of strategy is different
from the type of strategy we will investigate, as it is
only a single e ntity moving in a world against other
similar entities (Laird, 2001).
Although there are currently no RTS systems that in-
corporate emotions that we are aware of, other soft-
ware systems do exist with them. For instance, the
digital life simulation game, the Sims, includes emo-
tions. These emotions control the behavior of in-game
agents; an example being that an unhappy agent is less
likely to obey the commands of the controlling player.
Many other examples of emotions being used in com-
puter systems relate to the field of human-computer
interactions (HCI). A great amount of work has been
done on improving a computer’s ability to dete ct a
user’s emotions, and then using that information to
change its interaction with the user. Much of this work
is in the affective computing field (Picard, 1997; aff,
2007), and tends to relate to voice and facial recogni-
tion. A RTS system used for training can benefit from
this work, but it is beyond our current scope.
Agents who must communicate indirectly often use
collaborating software (not be confused with collab-
oration software) such as blackboards (Corkill, 2003).
These blackboard systems allow agents to post infor-
mation that other agents can later access and discuss
as appropriate. The posted information tends to last
for a long period of time, and be accessed when it
becomes relevant for that agent. In our system we
utilize an “emotion map” that allows a different type
of indirect communication. With our map an agent
shares its emotions at each time step to the areas im-
mediately surrounding it. Any agents that are nearby
will see this information and incorporate it into their
own set of emotions. However, the information saved
to the map degrades quickly until it disappears, thus
preventing the emotions from lingering for more than
a few time steps. We thus only allow indirect commu-
nication with agents within that locality at the time
of the emotion, instead of sharing with any agent at
any time. Although this concept seems simple initially,
it is not only different from previous communication
paradigms but can also lead to powerful and dynamic
3. Globulation
There are currently a few open
source RTS platforms, including ORTS
( ˜mburo/orts/) and Globu-
lation (
Both of these systems run on similar premises, de-
signed as strategy war games where the characters
can be controlled by the AI. We chose to work
with Globulation, a multi-player game where players
compete for resources and territory. A player loses
the game if all of their agents have been destroyed.
A particularly novel aspect of the game is the lack of
control over each individual agent, an approach taken
by nearly every RTS. Instead, players can control
Figure 1. A portion of the Globulation map. The darkly
shaded area is area that has not been ex plored, and there-
fore cannot be seen. The large item in the middle is a
building, whereas the smaller items just south of it are
workers. The cluster of items at the bottom are a resource
that needs to be gathered.
agents by defining their behavior at each square on
the map (e.g., forbidden, harvest, defend, etc.). This
allows players to focus on more general strategies as
opposed to testing their point and click skills.
Globulation has multiple Artificial Intelligences (AIs)
that can be chosen as a player in the beginning of a
game. The AI will thus control the actions of its as-
signed player so that no human intervention is needed.
It will not only make overall player choices, but each
agent is also given its own set of decision processes.
There are many different AIs available for Globula-
tion, each with a different focus, level of detail, and
success rate. The AI we will test against is named
“Nicowar” and has the highest success rate of the AIs.
To allow our work to concentrate on the emotion as-
pect, we define emotions as part of each agent’s indi-
vidual decisions such that they can be ported to any
of the AIs that already exist for the game. Thus, the
decision processes for agents will be a combination of
a previously created AI and our emotional paradigm.
When designing our emotions we examined the defi-
ciencies of Nicowar. Although it is the most human-
competitive AI in the game, its flaws include bot-
tlenecks with pathfinding when dealing with a large
numbers of agents, avoiding enemy agents (defensive
agents), and finding enemy agents (offensive agents).
We will seek to address all of thes e flaws with our emo-
tional system.
3.1. Agents
Each player in the game has their own age nts that
can be controlled by an overarching AI. These agents
include warriors, workers, and explorers. Each agent
has a numerical amount of health that can decrease if
it is injured or increased if it is healed. Each agent is
also affected by a need to eat, and has a base desire
to do work. All agents are capable of movement in 2-
dimensional space within the map boundaries. They
will make decisions on what actions to perform based
on what they encounter as they move through the map,
see Figure 1. Our emotional changes will affect each
agent’s own decisions, but will not affect the overall
Each agent has its own purpose in the game, and
the three types serve very different functions. The
workers exist to gather resources, which are needed
for the player to build buildings, create more agents,
and feed the current agents. The warriors defend the
player’s buildings and attack the opponent’s buildings
and agents. The explorers will wander the map to de-
termine the locations of enemies and resources. The
warriors need the workers to gather resources for them,
whereas the workers need the warriors to defend them.
The workers and warriors both need the information
the explorers discover to be able to see an oncoming
attack, where to go to attack, and the location of ad-
ditional resources for when current supplies run low.
4. Emotions
4.1. Types of Emotions Modeled
Figure 2. The plane representing the range of an agent’s
emotions and 4 possible emotion stages: the origin is no
fear or frustration, representing contentment; point 1 rep-
resents an agent with little frustration but high fear; point
2 is an agent with low fear and medium frustration; and
point 3 represents an agent with high frustration and high
We chose to model two different basic emotions which
will be the same in each agent type, although each
agent type will be affected differently by its emotions.
Although we did not choose all of the 6 basic emo-
tions (happiness, fear, frustration, anger, sadness, re-
lief) (Rolls, 2005), we chose two of them c oupled with
a more advanced emotion. The first basic emotion that
we modeled was fear. Fear is increased when an agent
is attacked by an enemy agent, an agent is very dam-
aged and close to death, or the player is running low
on resources. The first two causes are obvious, and the
last cause is because agents will die if they do not have
food, which is a resource. The second basic emotion
modeled is frustration. Frustration is increased when
an agent is unable to perform the task allotted to it
or the agent has been on the same task for a signifi-
cant amount of time (details in the Simulation Details
The lack of these two emotions also constitutes an
emotion: contentment. For instance, if there is little or
no fear the agent feels content as the world seems safe.
Also, if the agent has little or no frustration then it is
content because everything is working well. Although
agents do not make decisions based on the combina-
tion of their 2 basic emotions, their emotional state
at any time can be represented by a point on a plane
with fear as the y-axis and frustration as the x-axis as
seen in Figure 2. However, without the emotion map
(explained below), emotions would be entirely internal
and not shared.
4.2. Effects on Agent Actions
Each emotion affects agents in ways related to five of
Eckman’s seven characteristics of em otion: Quick on-
set, automatic appraisal, commonalities in antecedent
events, brief duration, and unbidden occurrence (Eck-
man, 1994). Emotions occur based on events in an
agent’s neighborhood immediately when that event oc-
curs. The agent does not have time to decide that its
surroundings are a problem, but instead there is quick
onset due to automatic appraisal of the situation. For
all agents of a particular type the same antecedent
event types will cause the same amount of the same
emotion. Also, emotions are brief unless the same
event continues to occur, in which case the emotion
will continue to build at a slow rate. Emotions are not
consciously caused, as only outside events or the shar-
ing of emotions from another agent can cause them.
The two characteristics that we do not relate to do not
apply to our situation (presence in other primates, dis-
tinctive physiology) (Eckman, 1994). Actions that are
taken due to an emotion are decided upon only once
the emotion reaches a specified threshold. Once that
threshold is reached, the agent ac ts according to both
its current situation and the fact that the particular
emotion is strong.
An emotion’s effect on each agent is homogeneous
throughout that agent type and differs between agent
types. The effects of emotion on our agents are based
on the idea of fight versus flight. If a worker experi-
ences enough fear, it will move in a direction toward
less fear until its fear falls below the required amount
for it to withdraw. If possible, it will continue to move
in a direction that will decrease its fear. This effect
minimizes the problem with the AI that causes agents
to not esc ape their enemies. A warrior, however, w ill
advance toward the cause of its fear (up to a specific
threshold) in hopes of vanquishing the source. This
reaction causes a warrior to move toward nearby en-
emy agents and attack, which is beneficial in both
offense and defense. This effect will improve on the
AI’s problem of not moving toward enemy agents of-
ten enough. However, if a warrior’s fear level crosses a
higher threshold it will retreat, improving on it’s abil-
ity to survive. The definition of “higher” was te sted
to determine an appropriate level, and is discussed in
the Simulation Details section.
The agent reactions are similar for frustration. If a
worker is feeling frustrated it will look elsewhere for
work, which will usually involve looking for resources
to gather. If the worker is already in a location with
resources but still feels frustration, it is likely due to
a large number of workers gathered who are causing
a bottleneck for retrieving resources. If a warrior has
frustration it will explore to look for enemies or will
wander around acting as a lookout, as frustration is
likely a result of no danger in its current lo cation.
Frustration directly combats the AI’s problem of failed
pathfinding. The AI already has a built-in check for
“boredom” that verifies that a worker is not idle for a
long period of time. Frustration, however, will solve
the problem of workers being crowded together at a
single entrance to an area with resources, or otherwise
needing to move to continue succeeding toward their
goals. Thus frustration can create a more efficient re-
source gathering mechanism for workers, and a higher
likelihood of encountering enemies for warriors.
The baseline for each agent is to have no fear and no
frustration (i.e. be content). Over time, any emotion
felt will decrease until it reaches this baseline or a new
experience replenishes the emotion. For example, if
a worker is running from an enemy agent and is not
chased, its fear level will continue to decrease with
each time step until it no longer has fear. However, if
the enemy chases it such that they continue to be the
same distance apart the agent will maintain the same
amount of fear. If the enemy is moving closer to the
agent, the fear will increase.
(a) Immediate emotion
(b) Decay of emotion af-
ter 1 time step
Figure 3. Approximate diffusion concept. The map in 3(a)
depicts the values in the squares under and surrounding
a agent that just experienced an emotion of strength 10.
The map in 3(b) depicts the values in those s ame squares
after a single time step, assuming that no agent is present
to modify the emotions of its surroundings with emotions
having a decay value of 2.
4.3. Emotion Map
For emotions to be most effective there must be a
mechanism for agents to infer each other’s emotions.
For humans, emotions are exceptionally useful as a
way to communicate. An agent’s emotion is there-
fore influenced by the emotions of other agents under
the same player via an Emotion Map and gradient.
Agent e motions cannot be interpreted or felt by an
opponent’s agents. At each time step, an agent’s emo-
tion will be saved to the map. Fear and frustration
are kept separately on the emotion map. The emotion
map affects an agent’s emotions and is updated by an
agent’s emotions at every time step. This frequency is
because emotions are vital to an agent’s decision mak-
ing, so it is therefore necessary that both the map and
the emotions felt by an agent from the map are as ac-
curate as possible. The agent’s emotion will be added
to the emotion on that square, and will be diffused
out to the adjacent sets of squares within a specified
cityblock distance. A distance of 2 is shown in Fig-
ure 3(a), assuming an emotion with value 10 was just
experienced. Given a max radius that defines the fur-
thest distance an emotion can travel and a value of the
emotion being felt, the amount of emotion that will b e
saved on the map at a location that is distance away
from the original point of the emotion is
Map(distance) =
valu e
v alue
max radius
. (1)
The emotion on the map will decrease over time in the
same way that the individual agent’s internal emotions
will decrease over time. At each time step, the current
emotion will decrease as shown in Figure 3(b), and
then any new emotions will be added.
Each agent can see a gradient of the map, and is there-
fore affected by this gradient with each decision made.
The agent’s own emotions are affected by the map such
that a small percentage of each of its emotions is de-
rived from the emotions on the map from the end of the
previous time step. The map therefore allows agents
to communicate indirectly, since the emotions held on
the map are due to another agent’s recent emotions.
Therefore, if an agent recently encountered an enemy
in a particular lo c ation, all close by agents will be
aware due to the emotions on the map. Also, any other
agents that come to the area within a short time span
will be aware as well. This use of the emotion map can
be equated to people hearing each other yell in fear or
anticipation of a fight, or grunting from boredom. It
is also related to the fact that another individual that
arrives slightly after the other agents may never know
that something recently occurred there. An example
of the emotion map being used can be seen in Figure
Figure 4. A series of images demonstrating an emotive
agent’s emotion map changing over time. Images are taken
every 4,000 time steps. Frustration is shown in the middle
shade of gray, fear is shown in the darker shade, and the
lightest shade is the overlap of the two emotions. Images
are organized chronologically from left-to-right and top-to-
bottom. Initially frustration is experienced by workers in
the home base of the agent. Eventually the opponent at-
tacks the agent and the agent’s home base is filled with
both fear and frustration.
5. Simulation Details
5.1. Globulation Set-Up
Simulations were run with version 0.9.1 of Globula-
tion 2. Evaluations were performed on the map Muka,
which is a one player versus one player map. Each
player has all necessary resources contained within a
region that is connected to the opponent via two land
bridges (at the top and bottom). The map wraps from
right to left, creating a land bridge from the left side
to the right side of each player’s region. Both players
also have an additional smaller peninsula containing
resources. The map is essentially symmetric although
it was created by hand due to limitations of the map
5.2. Emotion Controls
All emotions exist on a scale of 0 to 100. Both emo-
tions on the emotion map are initialized to 0 at ev-
ery location. Emotions then decay linearly at a rate
of 1 every time step. The constant diffusion radius
for both fear and frustration is 5. An example emo-
tion map changing over time can be seen in Figure 4.
Both fear and frustration were discounted by a factor
of 0.1, meaning that 10% of the previous emotion level
is added to the current emotion level. Fear is affected
by two factors: medical condition and surrounding en-
emy agents. Fear is increased by 1 every time step that
the agent is damaged, and increased by 10 for every
surrounding enemy agent. Frustration is increased in
a particular agent by one tenth of the amount of time
spent continuously performing the same task. This in-
crease of frustration allows agents stuck in a location
to free themselves by moving away from the frustration
Thresholds were required for specifying emotion con-
trolled behaviors. Worker and warrior agents surpass-
ing their frustration threshold will begin to move in the
opposite direction from the frustration gradient. For
both workers and warriors, the frustration threshold is
85. Worker agents with fear greater than 75 attempt to
evade the source of the fear by moving in the opposite
direction of the fear gradient whereas warrior agents
with fear greater than 55 are drawn toward the source
of fear, following the fear gradient. Once a warrior’s
fear increases above 90 it will retreat as well. These
values were determined via tests on AI Nicowar with
5.3. Agent Decisions
Each agent has two sets of controls that are the same
across all AIs: the built-in decision controls, and the
emotion-based decision controls. The built-in deci-
sion controls check for life threatening situations and
are executed first. Without the emotion-based deci-
sion controls, agents would choose an action randomly
if there was no life threatening situation (high need
for medical care or food). However, AIs that utilize
emotions use the emotion-based decision controls if no
other decision has been made by that agent. The built-
Figure 5. The decision tree for a warrior at each time step.
in and emotion-based controls are mutually exclusive
for each time tick, i.e. a decision is made by either one
or the other for each agent.
Each agent uses a specific decision tree when deter-
mining what action to perform each time step. The
decision tree for the emotion-based controls on war-
riors can be seen in Figure 5. These decisions are pri-
marily based on the thresholds discussed previously in
the paper. All updates from the emotion map o cc ur
at the beginning of each time tick. Thus, the fear (F )
of an agent at time t in location λ if it is surrounded
by φ enemies is shown in Equation 2 if M ap(F ear, λ)
refers to the amount of fear on the Emotion Map in
location λ and ω is a binary number that is 1 if the
agent is damaged and 0 otherwise.
F (t) = 0.8 · F (t 1) + 10φ + ω + 0.1 · Map(F, λ) (2)
An agent’s frustration (A) at time t can be similarly
set as seen in Equation 3 if χ is a binary number that is
1 if actionTickTimer represents the time the agent has
been doing the same action, (actionT ickT imer > 50),
and (actionT ickT imer/10).
A(t) = 0.8 · A(t 1) + χ + 0.1 · Map(A, λ) (3)
6. Results
We will test multiple AIs against themselves both with
and without emotions to determine if the AI was im-
proved with the emotions we implemented. We will
also test AIs that have only fear or only frustration.
The emotive AI will always be player 1, whereas the
non-emotive AI will always be player 2. For all statis-
tics of results we therefore compare player 1 to player
2. This ensures that our results are not biased due to
starting location on the map, as the non-emotive ver-
sus non-emotive results are also calculated this way.
To analyze the succe ss of a game we can examine the
hit points (HP) per agent for each player, with agents
referring to regular agents and buildings. HP represent
the health of an agent, which decreases as an agent is
injured and increas es when it is healed; if an agent
reaches 0 HP it dies . A high HP per agent ratio can
signify that either the player has a high number of
agents in various stages of health, or that all agents
have high HP. HP also rises as the level of an agent
increases, so high HP can signify more powerful war-
riors as well. Since all of these scenarios can represent
a successful game, they also imply good performance.
We can therefore use the ratio of HP to agents to de-
termine whether the emotions improved the AI. Since
each of these games is two AIs playing against each
other, we can take the difference of their ratios at each
time s tep and then average them. This average rep-
resents how much better the HP/agent ratio for the
first AI is over the second AI for the duration of the
game. We can also use the number of buildings and
agents as a measurement of success. A higher num-
ber of buildings and agents on average implies that
enough resources were gathered and that defense was
relatively strong.
Figure 6. Results from Nicowar versus Nicowar. Each label
on the X-axis represents the version of Nicowar (player 1)
played against a non-emotive Nicowar (player 2). From left
to right the bars represent: difference of average hp/unit
(blue), difference of average hp/unit when player 1 one (or-
ange), average number of agents (green), average number
of buildings (purple).
Initially we test Nicowar vs. Nicowar, as seen in Fig-
ure 6. Player 1 wins 3/8 of the games when neither AI
has emotion, and wins 1/4 of the games when player
1 has emotion and player 2 does not. At this point
it is difficult to say if there is a s ignificant change in
wins. However we do see significant improvement of
HP/agent when the emotion AI wins the game over
when the non-emotion AI wins. It seems likely that
the emotions give the AI an advantage overall. These
results also show us that the AI performs worse when
only fear or only frustration are used in the game, as
opposed to the combination, but only in the sense that
the emotion agent never wins a game. The difference
of the overall HP/agent for each situation is not sta-
tistically significant.
(a) Warrush vs Warrush
(b) Numbi vs Numbi
Figure 7. The difference between the average number of hit
points per agent on player 1’s team versus the same aver-
age for player 2. Each label on the X-axis represents the
version of the AI played against a non-emotive version of
the same AI. From left to right the bars represent: differ-
ence of average hp/unit (blue), average numb er of agents
(green), average number of buildings (purple).
We can also test other AIs versus themselves, as seen
in Figure 7. For Warrush, there is no significant
difference between using both emotions or no emo-
tion. However, there is an extremely significant dif-
ference when using only frustration. In this case, the
agents of Warrush retain a very high amount of health
throughout the game, most likely due to agents surviv-
ing longer and thus gaining levels and increasing their
maximum amount of health. There could also be an
improved ability for finding the healers.
Numbi, however, does not see any change with the ad-
dition of emotion (Figure 7(b)). The chosen emotions
and how they were modeled probably do not affect
Numbi as it already performs those specific tasks well.
This verifies that emotions, as with all decision pro-
cesses, should be created with details designed for the
specific situation.
It is also interesting to note that in all situations, using
only frustration results in completely no variation in
game outcome. As can be seen in all figures, there is
no standard error between runs. This may be due to
frustration somehow removing the uncertainty in agent
action, such that no random choice is ever made. This
may imply that makes decisions based on fear results
in the later need to rely on the original basic decision
making processes.
7. Conclusions
The addition of emotions has shown an overall im-
provement in the performance of the AIs. As expected,
some AIs improved more than others and responded
more favorably to certain scenarios. For instance,
the AI Warrush improved significantly more with only
frustration as opposed to utilizing both. These results
suggest that the two emotions may conflict in certain
scenarios. One such scenario is enemies in the base
area, as the workers fear may stop the warriors from
attacking or the warrior frustration of constant attacks
may influence worker movement patterns. Depending
on the scenarios likely to oc cur, different emotional
influences should be utilized for different AIs.
The Nicowar AI, however, improved substantially
more with both emotions, and was the only emotive
AI to win multiple games. As both emotions were
designed specifically to counteract problems in the
Nicowar AI, this may imply that emotions designed
for a specific system will improve that system’s func-
tionality the most although it may also improve similar
systems. For real-time AI, these results suggest that
emotions should be explored for designing new deci-
sion processes.
We also conclude that an emotion map is an effi-
cient way to allow agents to communicate emotion
with neighbors. It does not require direct commu-
nication but is more rem iniscent of cellular commu-
nication. Since the interactions and social constructs
that arise from emotion sharing are one of the key as-
pects of emotions, such an ability is necessary. We are
currently continuing this work to increase the success
of the emotions in controlling the agents by adding
other emotions and modifying the emotional reactions
of agents. It seems reasonable to utilize these fairly
simple constructs to improve an agent’s decisions.
The spatio-temporal evocation of emotion can be seen
visually in Figure 4 at a few critical points in time.
From these images it is possible to see that emo-
tional evocation occurs during relevant times during
the game play. Given that the expression of emo-
tion occurs in the proper situations and that results
show performance improvements for the emotive case,
it is reasonable to conclude that an emotional frame-
work will benefit the AIs in Globulation. Since the
tested paradigm shows improvement over no emotion,
more complex emotional systems should be examined.
The relative success of our system demonstrates that
adding emotions to a real-time system with artificial
intelligence is feasible and warrants further study.
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... With respect to affective cues to help direct observation, Olsen et al. (2008) explored a particular form of empathy, called emotional contagion. According to Hatfield et al. (1992), emotional contagion is defined as "the tendency to automatically mimic and synchronize expressions [...] to converge emotionally." ...
... This process is rather important in group dynamics because, as shown by Barsade (2012), it increases perceived task performance and cooperativeness and decreases conflicts. To implement a mechanism similar to this concept, Olsen et al. (2008) developed a real-time artificial intelligent system used in games, which uses emotion maps in the communication of emotions among agents. It enables agents to communicate their emotions to others in the neighborhood without direct interaction. ...
... Based on the analysis of these studies, we can conclude that affective responses (emotions and personality) may be useful clues to be observed in the environment to find trustful relations. The emotion map by Olsen et al. (2008) could be increased to personality maps with the help of APP techniques so it would be possible to know the personality of users before interaction. No trust and reputation model using the features presented earlier was identified in the review by Granatyr et al. (2015), so we think it is a good opportunity to employ such concepts along with the alreadyexisting mechanisms. ...
Trust allows the behavior evaluation of individuals by setting confidence values, which are used in decisions about whether or not to interact. They have been used in several fields, and many trust and reputation models were developed recently. We perceived that most of them were built upon the numeric and cognitive paradigms, which do not use affective aspects to build trust or help in decision making. Studies in psychology argued that personality, emotions, and mood are important in decision making and can change the behaviors of individuals. Based on that, we present links between trust and affective computing, showing relations of trust dimensions found in trust models with affective aspects, and presenting why affective computing approaches fit trust issues often addressed by research in this field. We also discuss trust relationships and decision making, emotions, and personality. Affective computing concepts have been used in a dispersed way and specifically in some models, so we aim to bring them together to encourage the growth of fuller trust models similar to those used by humans. We aim to find relations between both fields so it will be possible to employ such concepts to develop trust models using this new paradigm, defined as the affective paradigm.
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For the past quarter century, AI researchers have used the paradigm of collaborating software systems to tackle large and difficult problems. Blackboard systems were the first at- tempt at integrating "cooperating" software modules. The goal was to achieve the flexible, brainstorming style of prob- lem solving exhibited by a group of diverse human experts working together to address problems that no single expert could solve alone. Multi-agent systems research is revisiting the collaborating-software paradigm from an agent-centric orientation. Again the goal is to achieve effective collabo- ration with a group of independent software entities, but in a way that appears to be markedly different from the approach taken in blackboard systems. In this paper, I compare and contrast these two approaches. Examining collaborating software from both perspectives pro- vides insights into the nature of collaboration, reveals unre- solved problems in integrating disparate contributions, and underscores issues in coordinating collaborative activities.
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The research agendas of artificial intelligence and real-time systems are converging as AI methods move toward domains that require real-time responses, and real-time systems move toward complex applications that require intelligent behavior. They meet at the crossroads in an exciting new subfield commonly called “real-time AI.” This subfield is still being defined, and the precise goals for various real-time AI systems are in flux. Our goal is to identify promising areas for future research in both real-time and AI techniques. We describe an organizing conceptual structure for current real-time AI research, exploring the meanings this term has acquired. We then identify the goals of real-time AI research and specify some necessary steps for reaching them
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th human and computer controlled characters, making them a rich laboratory for artificial intelligence research into developing intelligent and social autonomous agents. Indeed, computer games offer a fitting subject for serious academic study, undergraduate education, and graduate student and faculty research. Creating and efficiently rendering these environments touches on every topic in a computer science curriculum. The "Teaching Game Design " sidebar describes the benefits and challenges of developing computer game design courses, an increasingly popular field of study COMPUTER GAME AI In computer games, designers can use AI to control individual characters, provide strategic direction to character groups, dynamically change parameters to make the game appropriately challenging, or produce a sports game's play-by-play commentary.2,3 Computer games offer an inexpensive, reliable, and surprisingly accessible research environment, often with built-in AI interfaces. Moreover, computer
This chapter examines the concept of emotions and their computational basis in the brain. It defines emotions as states elicited by the so-called reinforcers and explains that this definition may help provide a better understanding of the functions and classifications of emotions and the information processing systems in the brain that are involved in emotion. It proposes a hypothesis that brains are designed around reward and punishment evaluation systems, because this is the way genes can build a complex system that will produce appropriate but flexible behaviour to increase their fitness.
This article motivates AI research in the area of real–time strategy (RTS) games and de-scribes the road–map and the current status of the ORTS project whose goals are to imple-ment an RTS game programming environment and to build AI systems that eventually can outperform human experts in this popular and challenging domain.
This position paper discusses AI challenges in the area of real–time strategy games and presents a research agenda aimed at improving AI performance in these popular multi– player computer games. RTS Games and AI Research Real–time strategy (RTS) games such as Blizzard Entertain-ment's Starcraft (tm) and Warcraft (tm) series form a large and growing part of the multi–billion dollar computer games industry. In these games several players fight over re-sources, which are scattered over a terrain, by first setting up economies, building armies, and ultimately trying to elimi-nate all enemy units and buildings. The current AI perfor-mance in commercial RTS games is poor. The main reasons why the AI performance in RTS games is lagging behind de-velopments in related areas such as classic board games are the following: • RTS games feature hundreds or even thousands of in-teracting objects, imperfect information, and fast–paced micro–actions. By contrast, World–class game AI sys-tems mostly exist for turn–based perfect information games in which the majority of moves have global con-sequences and human planning abilities therefore can be outsmarted by mere enumeration.
This paper presents a formalism called Time Interval Petri Networks (TIPNs) and shows their efficacy for model-based envisionment of real-time concurrent interacting processes with temporal constraints. The TIPN formalism is developed by extending Petri Networks to Time Interval Petri Networks.
Rts games and realtime ai research
  • M Buro
  • T Furtak
Buro, M., & Furtak, T. (2004). Rts games and realtime ai research. Proceedings of the Behavior Representation in Modeling and Simulation Conference (BRIMS).
Computer games and the military: Two views
  • J Herz
  • M Macedonia
Herz, J., & Macedonia, M. (2002). Computer games and the military: Two views (Technical Report).