Consistent Dynamicgroup Emotions for Virtual Agents.

1
Consistent Dynamic-group Emotions for Virtual
Agents.
Joost Broekens, Niels Netten, Doug DeGroot
broekens, cnetten, degroot_at_liacs.nl, - LIACS,
Leiden University, Netherlands
Abstract The use of computational models of
emotion in virtual agents enhances the realism of
these agents in a variety of domains, including
virtual reality training and entertainment
computing. We consider these two domains as
prototypical for Multi-emotional-Agent-Systems
(MeASs), which are the focus of this paper. MeASs
typically include groups of agents organised into
clusters, for example a special-force unit. While
each agent in such a group has its own emotional
model, resulting in realistic individual
emotional behaviour, the group as a whole can
show unrealistic emotional behaviour. Currently
there is no method to enforce emotional
consistency of a cluster of agents while allowing
agents to have individual emotions. Our approach
introduces an emotional-state component that is a
separate step in the computational model of
emotion used by individual agents. The
introduction of this emotional-state component
enables multiple architectures for group
emotions. We evaluate these architectures and
conclude that several enable consistent
integration of individual emotions and group
emotions. We believe that our research enables
agent- and scenario designers to benefit from the
individual realism a computational model of
emotion brings to virtual agents, without losing
group consistency. Furthermore, by choosing one
architecture versus another, designers can
trade-off quality of the group emotion for
computational performance. We have implemented
one of the possible architecture in a MeAS
simulation environment. We show, using this
simulation environment, how agent in a group
emotionally influence each other (arriving at a
group-level panic state) and how a simple
strategy of a group leader can influence the
emotion of the agents in the group (effectively
calming the group).
Test Implementation of Architecture 5c (see short
demo) To test and experience the effects of an
emotional communication architecture embedded
within individual agents that form a group we
implemented a test scenario with two types of
agents (normal vs. leader) using architecture 5c.
All agent influence each others emotion directly.
Only leader type agents have a (cognitive)
strategy to control the situation, i.e., keep
calm or stay strong when confronted with a
group of panicked agents.
Overview of the Different Architectures The use
of an emotional-state as emotion currency, made
possible by separating the computational-model of
emotion in three steps appraisal,
emotional-state maintenance and emotional
behaviour (based on the same architecture as
proposed in 4), enables consistent emotions
for dynamic groups of virtual agents. Here below
you see different architectures that are possible
when using an emotional state as emotion
currency. Each architecture has some pros and
cons regarding the criteria as can be seen in
Figure 4.
1) Start a group of A type (normal) agents and
one B type (leader) agent.
2) A few A type agents set to the panic emotion
start to affect the group.
3) Group totally in panic cased by emotional
communication.
4) Agent B (leader) is mixed up in the group and
tries to calm down the panicked A type agents by
showing a very strong emotion (very positive,
un-aroused and dominant).
Figure 1. Architectures 1 (top-left), 2
(bottom-left), 3a (top-right) and 3b (bottom-
right). Subscripts are used to denote different
agents.
Figure 2. Architectures 4a (top) and 4b (bottom).
Both based on appraisal grouping
Figure 3. Architectures 5a (top-left) and 5b
(top-right) and 5c (bottom-left). All three are
based on emotional communication.

• Advantages of Dynamic Emotional State
  Architectures
• Easy integration of the concept of emotion into
  groups of agents.
• Easy simulation of emotional influences from
  different sources (both sources from within the
  agent and sources from other agents nearby).
• Facilitate the simulation of different emotional
  strategies.

Architecture Evaluation
Architectures that are made possible by our
approach range from high-performing to
high-quality and trade-offs are possible between
the two. Communication-based architectures
(Figure 4, column 5a and 5b) that use our
approach have high quality and little extra
design considerations for group-emotions.
Architectures based on group sharing of the
emotional-state or appraisal system (Figure 4,
column 3a,b and 4a) scale better. Future work
includes the extension of a MeAS simulation
environment 3 to test the different
architectures. We believe that our research
enables agent- and scenario designers to benefit
from the realism a computational model of emotion
brings to individual virtual agents, without
losing group consistency. Also, designers can
trade-off quality of the group emotion for
computational performance.
Related Work/References 1 A. Braun, S. R.
Musse, L. P. L. de Oliveira and B. E. J. Bodmann.
"Modeling Individual Behaviours in Crowd
Simulation". In CASA 2003 - Computer Animation
and Social Agents, pp. 143-148, May 2003, New
Jersey, USA. 2 B. Ulicny, D. Thalmann, Crowd
simulation for interactive virtual environments
and VR training systems, Proc. Eurographics
Workshop on Animation and Simulation01, pp.
163-170, Springer-Verlag, 2001.. 3 N. Netten.
Towards Believable Virtual Characters Using A
Computational Model Of Emotion. Master 's Thesis,
LIACS, Leiden University, 2004. 4 D. DeGroot
and J. Broekens. Using Negative Emotions to
Impair Gameplay. BNAIC, 2003.
Figure 4. Comparison of architects (performance,
quality and design effort)