Abstract. Most artificial general intelligence (AGI) system developers have been
focused upon intelligence (the ability to achieve goals, perform tasks or solve
problems) rather than motivation (*why* the system does what it does). As a
result, most AGIs have an unhuman-like, and arguably dangerous, top-down hierarchical
goal structure as the sole driver of their choices and actions. On the
other hand, the independent core observer model (ICOM) was specifically designed
to have a human-like “emotional” motivational system. We report here
on the most recent versions of and experiments upon our latest ICOM -based systems.
We have moved from a partial implementation of the abstruse and overly
complex Wilcox model of emotions to a more complete implementation of the
simpler Plutchik model. We have seen responses that, at first glance, were surprising
and seemingly illogical – but which mirror human responses and which
make total sense when considered more fully in the context of surviving in the
real world. For example, in “isolation studies”, we find that any input, even pain,
is preferred over having no input at all. We believe that the fact that the system
generates such unexpected but “humanlike” behavior to be a very good sign that
we are successfully capturing the essence of the only known operational motivational

With the notable exception of the developmental robotics, most artificial general intelligence
(AGI) system development to date has been focused more upon the details of
intelligence rather than the motivational aspects of the systems (i.e. *why* the system
does what it does). As a result, AGI has come to be dominated by systems designed to
solve a wide variety of problems and/or to perform a wide variety of tasks under a wide
variety of circumstances in a wide variety of environments – but with no clue of what
to do with those abilities. In contrast, the independent core observer model (ICOM) [1]
is designed to “solve or create human-like cognition in a software system sufficiently
able to self-motivate, take independent action on that motivation and to further modify
actions based on self-modified needs and desires over time.” As a result, while most
AGIs have an untested, and arguably dangerous, top-down hierarchical goal structure
as their sole motivational driver, ICOM was specifically designed to have a human-like
“emotional” motivational system that follows the 5 S’s (Simple, Safe, Stable, Self-correcting
and Sympathetic to current human thinking, intuition, and feelings) [2].

Looking at the example of human beings [3-6], it is apparent that our decisions are
not always based upon logic and that our core motivations arise from our feelings, emotions
and desires – frequently without our conscious/rational mind even being aware of
that fact. Damasio [7-8] describes how feeling and emotion are necessary to creating
self and consciousness and it is clear that damage reducing emotional capabilities severely
impacts decision-making [9] as well as frequently leading to acquired sociopathy
whether caused by injury [10] or age-related dementia [11]. Clearly, it would be more
consistent with human intelligence if our machine intelligences were implemented in
the relatively well-understood cognitive state space of an emotional self rather than an
unexplored one like unemotional and selfless “rationality”.

While some might scoff at machines feeling pain or emotions or being conscious,
Minsky [12] was clear in his opinion that “The question is not whether intelligent machines
can have any emotions, but whether machines can be intelligent without any
emotions.” Other researchers have presented compelling cases [13-16] for the probability
of sophisticated self-aware machines necessarily having such feelings or analogues
exact enough that any differences are likely irrelevant. There is also increasing
evidence that emotions are critical to implementing human-like morality [17] with disgust
being particularly important [18].


ICOM is focused on how a mind says to itself, “I exist – and here is how I feel about
that”. In its current form, it is not focused on the nuances of decomposing a given set
of sensory input but really on what happens to that input after it’s evaluated or ‘comprehended’
and ready to decide how ‘it’ (being an ICOM implementation) feels about
it. Its thesis statement is that:

Regardless of the standard cognitive architecture used to produce the ‘understanding’
of a thing in context, the ICOM architecture supports assigning
value to that context in a computer system that is self-modifying based
on those value based assessments…

As previously described [19], ICOM is at a fundamental level driven by the idea that
the system is assigning emotional values to ‘context’ as it is perceived by the system to
determine its own feelings. The ICOM core has both a primary/current/conscious and
a secondary/subconscious emotional state — each represented by a series of floating
point values in the lab implementations. Both sets of states along with a needs hierarchy
[20-21] are part of the core calculations for the core to process a single context tree.
Not wanting to reinvent the wheel, we have limited ourselves to existing emotional
models. While the OCC model [22] has seemingly established itself as the standard
model for machine emotion synthesis, it has the demonstrated [23] shortcoming of requiring
intelligence before emotion becomes possible. Since the Willcox “Feelings
Wheel” [24] seemed the most sophisticated and ‘logical’ emotion-first model, we
started with that. Unfortunately, its 72 categories ultimately proved to be over-complex
and descriptive rather than generative.

The Plutchik model [25-27] starts with eight ‘biologically
primitive’ emotions evolved in order to increase fitness and
has been hailed [28] as “one of the most influential classification
approaches for general emotional responses. Emotional
Cognitive Theory [29] combines Plutchik’s model
with Carl Jung’s Theory of Psychological Types and the
Meyers-Briggs Personality Types.

Fig. 1. The Plutchik model


The default Core Context is the key elements pre-defined in the system when it starts
for the first time. These are ‘concept’s that are understood by default and have predefined
emotional context trees associated with them. They are used to associate emotional
context to elements of context as they are passed into the core.
While all of these are hard coded into the research system at the start, they are only
really defined in terms of other context being associated with them and in terms of
emotional context associated with each element which is true of all elements of the
system. Further, these emotional structures or matrixes that can change and evolve over
time as other context is associated with them. Some examples of these variables and
their default values are:

• Action – The need to associate a predisposition for action as the system evolves.
• Input – A key context flag distinguishing internal imaginations vs external input.
• Pattern – A recognition of a pattern built-in to help guide context (based upon humans’
inherent nature to see patterns in things).
• Paradox – A condition where 2 values that should be the same are not or that contradict
each other.

Note that, while we might use these ‘names’ to make this item easily recognizable to
human programmers, the actual internal meaning is only implied and enforced by the
relationship of elements to other emotional values and each other and the emotional
matrix used to apply those emotional relationships (i.e. we recognize that Harnad’s
grounding problem is very relevant).

The context emotional states and the states of the system are treated as ‘sets’ with
matrix rules being applied at each cycle to a quickly-changing ‘conscious’ and a slower moving
‘subconscious’ that more strongly tends towards default emotions. The interplay
between them is the very heart of the system that creates the emotional subjective
experience of the system.

∀{E1,E3, … , E72} ∈ , 1 = 1, 2 = 2, … , 72 = 72 ;
∀{AE1,E3,… , E72} ∈ , 1 = 1, 2 = 2,… , 72 = 72 ;
∀ = (∑) () ,
∀ = () ,
∀{} ∈ ∧∀{E1, E3, … , E72} ∈ , = ( ∈
,{E1, E3, … , E72} ∈ ), = ( ∈ ,{E1, E3, … , E72} ∈
), …, = ( ∈ ,{E1, E3, …, E72} ∈ ) ;
∀{} ∈ ∧∀{E1, E3, … , E72} ∈ , = ( ∈
,{E1, E3, …, E72} ∈ ), = ( ∈ ,{A, B,C,D} ∈
), …, = ( ∈ ,{E1, E3, …, E72} ∈ ) ;
∀{} ∈ ∧∀{E1, E3, … , E72} ∈ , = ( ∈
,{E1, E3, …, E72} ∈ ), = ( ∈ ,{E1, E3, … , E72} ∈
), …, = ( ∈ ,{E1, E3, …, E72} ∈ ) ;
∀{} ∈ ∧∀{E1, E3, … , E72} ∈ , = ( ∈
,{E1, E3, … , E72} ∈ ), = ( ∈ ,{E1, E3, … ,E72} ∈
),… , = ( ∈ ,{E1, E3, …, E72} ∈ ) ;
∀ = () ;
∀{} ∈ = (NewContext, );

Fig. 2. Core Logic Notation/Pseudocode

New context associated with the object map or context tree of the current thought is
executed against every single cycle regardless of whether its origin is external input or
internal thoughts. Essentially the rules are then applied as to the relationships between
those various elements which is after the needs and other adjustments to where it then
falls into this final block which really is where the determination is made and it is in
these rules applied here that we see the matrix of the system affecting the results of the
isolation study.


While investigating how the system behaved under a wide variety of circumstances, we
encountered a series of cases whose results were initially very disturbing when testing
what happened when we stopped all input (while ICOM continued to process how it
felt) and then, finally, restarted the input. Imagine our surprise and initial dismay when
the system, upon being presented only with pain and other negative stimulus upon the
restarting of input, actually “enjoyed” it. Of course, we should have expected this result.
Further examination showed that the initial “conscious” reaction of ICOM was to “get
upset” and to “desire” the input to stop – but that the “subconscious” level, the system
“enjoyed” the input and that this eventually affected the “conscious” perception. This
makes perfect sense because it is not that ICOM really “liked” the “pain” so much as it
was that even “pain” is better than isolation – much like human children will prefer and
even provoke negative reactions in order to avoid being ignored.

Fig. 3. Series 3 Isolation Study (x = input type w/time; y = intensity of emotion)


It’s always great when experiments produce unexpected emergent results that should
have been anticipated because they are exhibited in the original system your model is
based upon. We believe that the fact that the system spontaneously generates such
unexpected but “humanlike” behavior to be a very good sign that we are successfully
capturing the essence of the only known operational motivational system with a human –
like emotional “self”.


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By David J Kelley and Mark Waser – appearing in the 2017 BICA Proceedings http://bica2017.bicasociety.org/wp-content/uploads/2017/08/BICA_2017_paper_136.pdf and http://bica2017.bicasociety.org/bica-proceedings/

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