Emotional Machines
Presented by
Chittha Ranjani Kalluri
Why Can’t…
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We have a thinking computer?
A machine that performs about a million floating-point
operations per second understand the meaning of shapes?
We build a machine that learns from experience rather than
simply repeat everything that has been programmed into it?
A computer be similar to a person?
The above are some of the questions facing computer
designers and others who are constantly striving to build
more and more ‘intelligent’ machines.
So, what’s intelligence?
According to en.wikipedia.org:
“Intelligence is a general mental capability that
involves the ability to reason, plan, solve problems,
think abstractly, comprehend ideas and language,
and learn.”
What does this mean for current
machines?
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Definitely not that they’re not intelligent!
Some amount of intelligence has to be built in
How can that be done?
Designers looked closely at how humans
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Behave
Express themselves
Process information
Solve problems
Expressing ourselves
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Body language
Facial expressions
Tone of voice
Words we choose
All of them vary based on situation
What we implicitly convey - emotion
What is emotion?
In psychology and common use, emotion is the
language of a person's internal state of being,
normally based in or tied to their internal (physical)
and external (social) sensory feeling. Love, hate,
courage, fear, joy, and sadness can all be described
in both psychological and physiological terms.
Do machines need emotion?
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Machines of today don’t need emotion
Machines of the future would need it to
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Survive
Interact with other machines and humans
Learn
Adapt to circumstances
Emotions are a basis for humans to do all the above
What is an emotional machine?
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An intelligent machine that can recognize emotions
and respond using emotions
Concept proposed by Marvin Minsky about a year
ago in his book ‘The Emotion Machine’
Example: the WE-4RII (Waseda Eye No. 4 Refined
II), being developed at the Waseda University, Japan
The WE-4RII
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Simulates six basic emotions
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Happiness
Fear
Surprise
Sadness
Anger
Disgust
Recognizes certain smells
Detects certain types of touch
Uses 3 personal computers for communication
Still not as close to an emotional machine as we would want
The WE-4RII
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Happiness
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Fear
The WE-4RII
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Surprise
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Sadness
The WE-4RII
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Anger
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Disgust
Do we want…
Maybe…
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We’re not there…yet!
So how do we get from
to
Characteristics of multi-modal
ELIZA
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Based on message passing on blackboard
Input – user’s text string
Output – sentences and facial displays
Processing module consists of
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NLP layer
Emotional recognition layer
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Constructs facial displays
NLP Layer
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String converted to list of words by parser
Spelling checked
Abbreviations replaced
Slang words and codes replaced with correct ones
Some words replaced with synonyms by thesaurus
Input matched with predefined patterns by
syntactic-semantic analyzer
Longest matching string used to generate reply
NLP Layer
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Repetition recognition ensures dialog does not enter
loop
Rules written in AIML (Artificial Intelligence
Markup Language)
Pragmatic analysis module checks reply against user
preferences collected during conversation, and
against goals and states of system
Emotion recognition layer
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Emotive Lexicon Look-up Parser used to extract
emotion eliciting factors
Bases it on a lexicon of words having emotional
content
247 words, each with a natural number intensity
Overall emotional content of a string got from
seven ‘thermometers’ which get updated when an
emotionally rich word is found
Emotion recognition layer
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Emotive Labeled Memory Structure Extraction
labels each pattern and corresponding rules
Two additional AIML tags used – ‘affect’ and
‘concern’: positive, negative, joking, normal
Goal-Based Emotion Reasoning stores user’s
personal data
Two knowledge bases to determine affective state
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Stimulus response to user’s input
Result of cognitive process of conversation to convey
reply
Preference rules - examples
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IF (user is happy) AND (user asks question) AND
(systems reply is sad) AND (situation type of user
is not negative) AND (highest thermo is happy)
THEN reaction is joy.
IF (user is sad) AND (systems reply is sad) AND
(situation type of user is joking) AND (situation
type of the system is negative) AND (maximum
affective thermo is sad) THEN reply is resentment.
Facial display selection
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Intensity of an emotion must exceed a threshold
level before it can be expressed externally
If an emotion is active, system calculates values of
all thermometers
Thermometer having highest value chosen as
emotion
Intensity of emotion determines facial display
Other work in this area
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Emotionally Oriented Programming (EOP)
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Allows programmers to explicitly represent and reason
about emotions
Can build Emotional Machines (EMs) – intelligent
software agents with explicit programming constructs for
concepts like mood, feelings, temperament
Inspiration: thoughts and feelings are intertwined
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Analysis of thought inspires feelings
Feelings inspire creation of thoughts
Other work in this area
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Emotionally Oriented Programming (EOP)
Other work in this area
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Emotional Model for Intelligent Response (EMIR)
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Developed by Mindsystems, an Australian company
Includes simulations for feelings such as boredom!
Methodology:
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Looks at factors influencing a character
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Success at achieving goals
Levels of a character’s control over situation
Compares this “state of mind” to a database of human
responses mapped over time
Was in demo stage in 2002
Other work in this area
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Emotionally Rich Man-machine Intelligent
System (ERMIS)
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Aims to develop a prototype system for humancomputer interaction that can interpret its user’s attitude
or emotional state, e.g., activation/ interest, boredom,
and anger, in terms of their speech and/or their facial
gestures and expressions
Adopted techniques include linguistic speech analysis,
robust speech recognition, and facial expression analysis
Other work in this area
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Net Environment for Embodied, Emotional
Conversational Agents (NECA)
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Promotes concept of multi-modal communication with
animated synthetic personalities
Key challenge - the fruitful combination of different
research strands including situation-based generation of
natural language and speech and the modeling of
emotions and personality.
Conclusion
The question is not whether intelligent machines can
have emotions, but whether machines can be
intelligent without any emotions.
Marvin Minsky, The Society of Mind
Bibliography
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Emotional machines – http://www.emotionalmachines.com
Emotional machines – Do we want them? http://www.zdnet.com.au/news/communications/0,2000061791,202661
34,00.htm
Marvin Minsky Home Page - http://web.media.mit.edu/~minsky/
Multi-Modal ELIZA http://mmi.tudelft.nl/pub/siska/_TSD%20my_eliza.pdf
The WE4-RII - http://www.takanishi.mech.waseda.ac.jp/research/eyes/
Small Wonder - http://www.smallwonder.tv/
The HUMAINE Portal - http://emotion-research.net
ERMIS - http://manolito.image.ece.ntua.gr/ermis
NECA - http://www.oefai.at/NECA