=Paper=
{{Paper
|id=Vol-1520/paper2
|storemode=property
|title=Case-based Local and Global Percept Processing for Rebel Agents
|pdfUrl=https://ceur-ws.org/Vol-1520/paper2.pdf
|volume=Vol-1520
|dblpUrl=https://dblp.org/rec/conf/iccbr/ComanGM15
}}
==Case-based Local and Global Percept Processing for Rebel Agents==
https://ceur-ws.org/Vol-1520/paper2.pdf
23
Case-based Local and Global Percept Processing for
Rebel Agents
Alexandra Coman1, Kellen Gillespie 2, Héctor Muñoz-Avila 3
1
Department of Electrical and Computer Engineering and Computer Science,
Ohio Northern University, Ada, OH 45810
2
Knexus Research Corporation, Springfield, VA 22314
3
Department of Computer Science and Engineering, 19 Memorial Drive West,
Lehigh University, Bethlehem, PA 18015
a-coman@onu.edu, kellen.gillespie@knexusresearch.com, hem4@lehigh.edu
Abstract. Rebel Agents are goal-reasoning agents capable of “refusing” a user-
given goal, plan, or subplan that conflicts with the agent’s own internal motiva-
tion. Rebel Agents are intended to enhance character believability, a key aspect
of creating engaging narratives in any medium, among other possible uses. We
propose to implement and expand upon a Rebel Agent prototype in eBotworks,
a cognitive agent framework and simulation platform. To do so, we will make
use of (1) a case-based reasoning approach to motivation-discrepancy-percep-
tion, and (2) user input for creating the agents’ “emotional baggage” potentially
sparking “rebellion”.
Keywords: rebel agents, character believability, local and global perceptual
processing
1 Introduction
Rebel Agents [6] are motivated, goal-reasoning agents capable of “refusing” a goal,
plan, or subplan assigned by a human user or by another agent. This rejection is the
result of a conflict arising between the given goal or plan and the agent’s own internal
motivation. In our previous work, we made the assumption that this motivation is
modeled for the purpose of creating character believability [1], a key aspect of engag-
ing narratives in any medium. However, different motivation models are also applica-
ble. In the context of rebel agents, the term “motivation discrepancies” refers to in-
congruities between a character’s motivation and the character’s assigned goal and/or
course of action. When a motivation discrepancy occurs, depending on the perceived
intensity of the incongruity, the Rebel Agent may generate a new goal that safeguards
its motivations. While so far explored in the context of interactive storytelling and
character believability, the potential applications of rebel agents are by no means
limited to this. Such agents can also be useful, for example, in mixed-initiative situa-
tions in which the Rebel Agent may have access to information unavailable to its
human collaborator, and use this information to decide when to reject a command
received from the collaborator.
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes. In Proceedings of the ICCBR 2015 Workshops. Frankfurt, Germany.
24
We are in the process of developing a conceptual framework for Rebel Agents and
implementing a Rebel Agent prototype in eBotworks, a cognitive agent framework
and simulation platform [15].
In previous work [7], we explained that, for the purpose of detecting and reacting to
“motivation discrepancies”, eBotworks agents should be made able to perceive and
interpret their surroundings in “subjective” ways potentially eliciting “emotion” in-
tense enough to cause rebellion. We showed how eBotworks agent perception, which
is by default omniscient and objective, needs to be modified to more closely mimic
(or appear to mimic) human perception. We also described that this can be achieved
using sensory filters informed by mechanisms of human perception. These mecha-
nisms include gradual perception differentiation, local and global percept processing
and, perhaps most importantly for our purposes, the bidirectional connection between
perception and emotion. That is, perception can elicit emotion and is, in turn, affected
by emotion.
While relying on psychology literature to build these filters, we are ultimately aim-
ing for agents with believable observable behavior, but not based on complex models
of cognition.
We aim to endow our prototype Rebel Agent with motivation based on emotional-
ly-charged autobiographical memories. For example, a bot that reaches a location at
which something “traumatic” happened in the past might undergo a goal change ap-
propriate to the context. The retrieval of autobiographical memories is to initially
occur based on location ecphory [14], that is, location-specific memory cues. They
use exact physical locations (i.e. map coordinates) as memory cues. This choice is
preferable from a practical standpoint, but does not accurately reflect the way location
ecphory works in humans. The characteristics of a location that awaken memories and
incite emotion tend to be the sights, sounds, smells, tastes, and tactile sensations per-
taining to it, not necessarily its map coordinates. However, while location coordinates
are easy to retrieve and to compare, the same cannot be said about complex combina-
tions of percepts.
In previous work [7], we explained how the perception mechanisms of eBotworks
can be modified in order to acquire percepts in a more “human-like” manner.
Herein, we approach the challenge of retrieving past percepts and comparing them
to current ones using the case-based reasoning model, which is a natural match for
this retrieval process. Case-based reasoning literature offers examples of complex
case structures and associated similarity measures (e.g., [4][13][18]), allowing us to
store and compare complex scene representations, thus taking location ecphory be-
yond mere map coordinates.
In building a case base consisting of “memories” of percepts and associated emo-
tions, one of the challenges is providing the basis upon which the agents associate
emotions to percepts. While this could be accomplished by building a complex inner
model of the agent, herein, we discuss a knowledge-engineering-light alternative. This
new approach could leverage the chat-based interface of eBotworks, through which
users can give agents commands. In our context, human users would be directing the
agent how to “feel” in certain situations. That way, the agent, instead of being provid-
ed with a complex program dictating how it should behave in various contexts, picks
up an "emotional baggage" derived from that human user's personality (or just a
"role" that the human user chooses to play). By getting input from different human
users, we can produce a range of bots roughly exemplifying various personalities.
Our two contributions herein are:
25
(1) Exploring a case-based reasoning context for motivation-discrepancy-per-
ception in eBotworks Rebel Agents.
(2) Proposing the use of chat-based user input for creating the agents’ “emotional
baggage”, potentially sparking “rebellion”.
Finally, it must be mentioned that, although we approach them in this specific con-
text, local and global percept processing are applicable not only to Rebel Agents, but
to any intelligent agents endowed with perception capabilities.
2 Local and Global Percept-Processing and Emotion
Gradual perception differentiation and local and global percept processing have been
shown, in psychology literature, to characterize human perception. Human perception
has also been shown to stand in bidirectional connection with emotion; percepts of
various types can elicit emotional responses [5], while perception can be influenced
by emotion and motivation as explained below [9][12][22].
Perception differentiation deals with the steps of the gradual formation of a percept.
Global-first percept processing begins with global features, with local ones be-
coming increasingly clear in later stages. It has been argued to be induced by positive
emotions, such as happiness. Citing [17] and [20], Navon [16] sees perceptual differ-
entiation as always “proceeding from global structuring towards more and more fine-
grained analysis”. As to what makes a feature global, rather than local, Navon de-
scribes a visual scene as a hierarchical network, each node of which corresponds to a
subscene. Global scenes are higher up in the hierarchy than local ones, and can be
decomposed into local ones. More recently, it seems to be agreed upon that, while a
widespread tendency towards global-first processing is observed, it cannot be estab-
lished as a general rule applying to all individuals at all times [22].
Local-first percept processing begins from or focuses on local features. It has
been argued to be more likely when under the influence of negative emotions, such as
stress and sadness. However, strong motivation has also been shown to be capable of
inducing local-first processing [11]. Individuals with certain personality disorders
have been hypothesized to be inclined towards local precedence. Yovel, Revelle, and
Mineka [21] state that obsessive-compulsive personality disorder has been connected
to “excessive visual attention to small details”, as well as “local interference”: an
excessive focus on small details interfering with the processing of global information.
The same preference for local processing has been associated with autism spectrum
disorders [10].
The tendency towards global or local processing has also been theorized to be cul-
ture-specific: certain cultures have been shown to favor local precedence [8].
Connections between perception, emotion, and motivation are discussed at length
by Zadra and Clore [22]. Their survey covers the effects of emotion and mood on
global vs. local perception, attention, and spatial perception.
26
3 Local and Global Percept Processing for Rebel Agents in eBot-
Works
eBotworks [15] is a software platform for designing and evaluating communicative
autonomous systems in simulated environments. “Communicative” autonomous sys-
tems are those that can interact with the environment, humans, and other agents in
robust and meaningful ways, including the use of natural language. eBotworks tasks
have so far been limited to path-finding and obstacle-avoidance-type tasks (Figure 1),
and have not been concerned with character believability.
Figure 1. An eBotworks scene with an eBot performing obstacle avoidance.
In previous work [7], we designed scenarios showcasing emotion-influenced per-
ception for possible future implementation in eBotworks. We then discussed how the
implementation of these scenarios might be achieved with existing components of the
framework.
We will first reiterate these scenarios before we explain how the newly-proposed
mechanisms can be used to achieve them. The scenarios are based on the following
assumptions: (1) the agent is a Rebel Agent [6] endowed with an autobiographical
memory model in which memories are connected to emotions, (2) default perception
is global-first, (3) agents have current “moods” (emotional states) which can be neu-
tral, positive or negative, with the “neutral” mood being the default one, (4) moods
can change as a result of perceiving scenes evoking autobiographical memories with
emotional associations, (5) mood affects perception in the ways described in Section
2, (6) all scenarios take place on the same map, (7) in all scenarios, the agent has been
assigned a goal that involves movement to a target location on the map; based on its
reaction to scenes perceived on its way to the target, the agent may or may not rebel;
when a rebellion threshold is reached, the agent does rebel, (8) in all scenarios, the
agent perceives two scenes on its way to the target; the perception of the first scene
27
may or may not affect the agent’s current mood, which, in turn, may influence how
the second scene is perceived.
- Scenario 1: On the way to its target location, the agent perceives a box. This
evokes no emotions, as there are no connections to the box in the autobio-
graphical memory of the agent. Then, the agent perceives the second scene: a
traffic-cone-lined driving course, using global-precedence perception. The
agent’s emotion changes to a slightly-positive one, as it “enjoys” driving
through traffic-cone-lined driving courses. This does not elicit a goal change.
- Scenario 2: On the way to its target location, the agent perceives a box. In
the agent’s autobiographical memory, the box has positive emotional associ-
ations. This changes the agent’s mood to a positive one. Positive moods fa-
vor global perception, so they do not change the agent’s default perception
type. The agent perceives the traffic-cone-lined driving course using global-
precedence perception. The agent’s mood remains positive. This does not
elicit a goal change.
- Scenario 3: On the way to its target location, the agent perceives a box. In
the agent’s autobiographical memory, the box has negative emotional associ-
ations. Therefore, the agent’s current mood changes to a negative one. Soon
afterwards, the agent perceives the traffic-cone-lined driving course. Due to
the agent’s mood, local interference occurs, and the agent largely ignores the
overall scene, while focusing on the color of the cones (which is similar to
that of the box), which reminds it of a sad occurrence from the past, like a
collision. This changes the agent’s mood to a more intensely negative one,
which causes the rebellion threshold to be reached and the agent to “rebel”.
4 Case-Based Reasoning for Location Ecphory
Ecphory is the remembrance, caused by a memory trigger, of a past event. In the case
of location ecphory, this trigger is a location with which the memory is associated.
Gomes, Martinho, and Paiva [14] use map coordinates as location-ecphory triggers.
While this is easier from a practicality standpoint, the authors admit it does not accu-
rately reflect the way location ecphory works in humans. Location coordinates (unless
physically perceived, with some emotional associations) are unlikely to awaken
memories and incite strong emotion. Instead, it is the sights, sounds, smells, tastes,
and tactile sensations pertaining to a place that work to achieve this recollection.
Thus, if these traits change beyond recognition, the location’s function as a memory
cue is invalidated.
Retrieving stored memories is a natural match for the case-based reasoning model,
which was inspired by the psychological mechanisms underlying memory storage and
recollection. Furthermore, case-based reasoning literature contains ample coverage of
similarity measures between complex case structures that are not trivially comparable,
including graphs, plans, and case structures based on object orientation, which is pre-
cisely what we need for implementing our Rebel Agent prototype in eBotworks. By
28
using case-based reasoning similarity measures, we intend to expand location ecphory
beyond just map coordinates.
4.1 Case Structure and Similarity Measures
To achieve the emotional location-ecphory effect we are aiming for, each case should
contain two essential pieces of information: (1) a past percept, and (2) an emotional
reaction associated with that percept.
The ways in which we model these pieces of information can vary in complexity.
The percept can be a complex scene or a very specific subscene, such as an individual
object or something slightly more general such as a set of objects on a table. The emo-
tional reaction can consist of a simple, basic emotion (e.g. “joy”) or of a complex,
layered conglomerate of emotions, each experienced at a different degree of intensity.
Due to the characteristics of our simulation platform, we are, for now, focusing on
visual ecphory triggers, although triggers of a different nature (e.g. gustatory and
olfactory) certainly function in the real world.
In choosing our case structure, we are influenced by the description that Navon
[16] gives of a visual scene as a hierarchical network, each node of which corresponds
to a subscene. Global scenes are higher up in the hierarchy than local ones, and can be
decomposed into local ones. Global-first processing proceeds from global scenes,
local-first processing from local ones. We do not, however, aim at matching any psy-
chological model of perception differentiation perfectly through our case representa-
tion.
To approximate this hierarchical structure, we propose a model inspired by object-
oriented ([3][2] - Section 4.4) and graph-based ([19][2] - Section 4.5) case structures.
A scene hierarchy is not equivalent to a class inheritance hierarchy, though there
are clear similarities between the two. The reason is that in a class hierarchy, classes
lower down in the hierarchy incorporate the attributes of classes higher up, whereas in
the scene/subscene hierarchy, the inverse takes place: the root scene incorporates
information from all lower nodes, because the complete scene is composed of all
subscenes.
It is to be noted that the rather simple description above does not accurately capture
human perception, in which a global scene is perceived as a general outline with
vague details that become clear while travelling downwards in the hierarchy. There-
fore, the details in the lower nodes are then incorporated (potentially completely) into
the higher nodes. If perception proceeds in a global-first manner and is not prolonged,
these lower levels may not be reached.
The similarity methods of Bergmann and Stahl [3] allow objects at different levels
in the class hierarchy to be compared. This is especially useful, as we have no guaran-
tees that two subscenes we are comparing are at similar hierarchical levels.
However, our situation is even more challenging: not only are the scenes that we
are comparing different and at different hierarchical levels, but even their respective
hierarchies can be different and correspond to varied scenes (unless the scenes that
can be perceived are highly controlled and limited). Despite this challenge, we believe
that the local and global similarity measures proposed by Bergmann and Stahl [3] can
be adapted to be used for local and global perception, respectively. The perception
setting of the agent at a given time (e.g. global after perceiving the box in Scenarios 1
29
and 2; local after perceiving the box in Scenario 3) will determine where in the hierar-
chy we look for the similarity.
For simplification, we can assume that the cases are collections of objects, and do
not take into account the spatial positioning of the objects in a scene in relation to one
another.
4.2 Populating the Case Base
In order to populate a case base consisting of “memories” of percepts and associated
emotions, we must first provide a mechanism allowing agents’ percepts to be associ-
ated with emotions.
Truly human-like agents would be able to generate emotions themselves. This
would be partially based on (1) the personality with which the agent would have been
endowed (which could dictate, for example, that the agent is not easily frightened),
and (2) general rules about ways in which people tend to react to certain situations
(e.g. a gruesome scene tends to cause shock). Thus, making agents able to generate
emotions in response to percepts would require providing them with at least one of
these two models.
We are interested in exploring a knowledge-light alternative to this challenge. This
approach can leverage the chat interface of eBotworks (or alternative eBotworks
mechanisms) and is based on the idea of having human users direct the agent on how
to “feel” in certain situations. Thus, the agent acquires an "emotional baggage" de-
rived from that human user's personality or a "role" that the human user chooses to
play. Some bots, for instance, could be directed to be more “impressionable” than
others.
Let us re-examine Scenario 3, where the agent perceives a box with negative emo-
tional associations. With this approach, this association would not exist because the
bot previously got hurt in the vicinity of the box, but rather because the bot was pre-
viously told that the box should make it “feel sad”.
While we only propose this mechanism for the purpose of attaching specific emo-
tions to scenes, it could later be applied more broadly within the context of motivation
discrepancies and Rebel Agents. For instance, it could also be used to assign meaning
to scenes, so that the agent can match scenes similar in meaning (e.g. “a quarrel”)
rather than just in their constitutive elements. With this ability, agents can then match
emotion to meaning (e.g. witnessing a quarrel causes stress), rather than just to specif-
ic scenes and subscenes.
Currently, the chat interface of eBotworks is used to issue commands to agents in a
simulated environment. For example, a user can enter “Go here” and click on a loca-
tion on the current map; if the command is successful, the agent reacts by moving to
the specified location.
To explain how this system could be used for our purposes, let us first assume that
the bot is facing a scene containing a box. One option would be for the user to simply
say one of several words corresponding to several emotions “understood” by the sys-
tem, e.g. “sad”. In this case, the agent would take a “snapshot” of the scene it is facing
and store it together with the associated emotion, sadness.
However, memories of strong emotions can be associated with very specific
subscenes, rather than to an entire complex scene (e.g. excitement associated with a
logo on the envelope containing a college acceptance letter). Moreover, the subscene
30
that attention ends up focusing on in such situations is not necessarily related to the
emotion itself. Instead, it could contain items that just happen to be there when the
emotionally charged event occurs (e.g., a cup that happens to be on a nearby table
while a severe argument is taking place).
To handle this possibility, we can allow the user to specify an object in the scene to
which to associate the emotion by clicking on the object first, then saying the word
corresponding to the emotion. In Scenario 3, clicking on a box then saying “sad” can
cause the agent to switch to a sad mood and experience local interference in percep-
tion. Another necessary addition to typical eBotworks usage will be to have the agent
convey, through console messages, (and, later, possibly, through visual representa-
tions on the map) what objects it is currently focusing on and what moods it is experi-
encing. This will enhance believability by providing insight into the agent’s motiva-
tions and into the emotional justification behind its actions.
5 Conclusions
We have discussed using the case-based model for the purpose of creating location-
ecphory-based motivation-discrepancy mechanisms for Rebel Agents, addressing the
challenge of retrieving emotionally-charged past percepts and comparing them to
current ones.
Our two main contributions herein are:
(1) Exploring a case-based reasoning context for motivation-discrepancy-
perception in eBotworks Rebel Agents.
(2) Proposing the use of chat-based user input for creating the agents’ “emotional
baggage”, potentially sparking “rebellion”.
Acknowledgements. We thank the reviewers for their comments and suggestions,
some of which have been integrated into the final version of the paper, others of
which will be of help to us in our future work.
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