by Dr. Fun Wey, Principal Lecturer & Consultant, Artificial Intelligence Practice, NUS ISS for the NUS-ISS SkillsFuture Series Seminar: Harnessing the power of Intelligent Software Agents (9 April 2019)

1. © 2018 National University of Singapore. All Rights Reserved 1
Psychological Collaborative Agents
– Frontier of softbot technology
Fun Wey, Ph.D.
Principal lecturer, Analytics and Intelligent Systems
Institute of System Science, National University of Singapore

3. Technical perspective
Fixed in non-
adaptive softbot

5. Full spectrum of human’s mental activities
1. Rational/logical thinking
2. Sensing
3. Emotions
4. Pragmatics (value-measuring)
5. Intuition
6. Analogical mapping
7. Imagination
8. Aesthetics
9. creativity
Currently in commercialized AI

6. Intuitive
Aesthetic
Analogical mapping
Creative
Speed
Precision
Scale
Repetitive
Aim : to make Human-Machine interactions ≥ instinctive as Human-Human interactions
How these strengths
could be seamlessly
combined in a system?
Not limited to smart interfacing
“Intelligence
Augmentation”
(IA)
“Intelligent
Infrastructure”
(II)
Replicate
these to
AI

7. Artificial Intelligence — The Revolution
Hasn’t Happened Yet - Michael I. Jordan, April, 2018
• So far we are just refining
the tools – their speeds,
efficiencies, chance of
success, etc.
• We have done very little on
the real core issue – the
ability of machines to
assess relevance,
usefulness, meaningfulness,
etc.

8. Emotional AI
Emotional
AI
Humans’ facial expressions,
voice’s tune, gestures, gaze
directions, etc.
Humans’
modes and
concerns
Behavioural
knowledge
base
Responds
appropriately
Rational AI
(NLP, text-
mining, logical
reasoning)
Humans’ spoken contents,
contextual information, etc.

9. Many ‘partial’ psychological models based on
behavioural modelling
• So far all achieved very limited success with AI feels like ‘schizophrenic’ and ‘super-dumb’
• The key challenge is that humans(except young kids) won’t reveal their real concerns and
intents to others so directly
=> needs something not just ‘superficial’ but deeper and core
e.g.

10. e.g. behaviour-based agent’s creation in SPADE

11. Pragmatic AI
• Traditional engineering is based on creating machine with competence
for fulfilling a pre-fixed set of purpose
• A person’s judgement of what is useful and relevant changes in
different circumstances – but the core beliefs and values remain
relatively consistent
• Needs AI’s auto-detection of what is “useful”, “relevant”, “meaningful”
and “valuable” from point-of-view of humans in an evolving operating
environment
• For creating smarter machines we need to model how human’s
intention is formed from beliefs and values in different contexts

12. Tell my robot servant:"Pass me that cup of water"
• This command by itself is much under–constrained to the robot
• Robot needs to auto-fill in many implicit constraints related to my intents:
1. to take over the cup with my hand => within hand-reachable distance
2. Minimize spillage => minimizing the jerk + maintain the orientation of the cup
3. to avoid collision
4. Within reasonable time
5. Just enough gripping force to hold the cup but not to crush it
6. If I intend to drink the content & content too hot => warn me(empathetic)
7. Prefers left or right-handed (social etiquette)
8. … etc (many more)
Human house maid does this complex ‘intention-sync’ subconsciously
Physical

13. Urgent need for intention-sync in AI
• In 2017 engineers in Google and Facebook shut down some of their
AI machines after the machines created their own interaction rules
that were not within designed scope
• Machines have already advanced to the state where they could no
longer be bounded by their pre-designed functions
• => need a new approach to root the purpose of future-ready AI
system in order to benefit from its auto-evolution

14. New way of “Rooting” the purpose of machines
Current

15. © 2018 National University of Singapore. All Rights Reserved 15
Intention-synchronising technology
• To aim at achieving maximum flexibility for Auto-competence-acquisition with
minimum set of restraints
• Must-have for achieving commonsense AI
• “commonsense” – we may have different beliefs and values, but we make
decisions based on similar pragmatic decisions based on our beliefs and values
• Core of human-human communications
Intentional stance
(Dennett 1987)
Intentional System
theory
(Dennett 2009)
Belief-Desire-
Intention (BDI)
framework for
software agent
engineering
(Bratman 2010)
Intentioneering
for AI

16. Intentioneering Map of human’s psychology

17. “Human-Emulator” running alongside
Superintelligent AI
“Human Emulator”
Psychological
Collaborative System
Future-ready AI
Output to
operating
environment
(Evolving) Operating Environment
Observeandlearn Monitors the benefits and harm
Stand-alone “human emulator”
softbot running alongside any
automated system, just like a
human constantly giving advice
to its evolution, and closely
watching the benefits or harm it
produces
Auto-discovery of new
algorithms, data structures,
utilizations, etc.
Assesses the usefulness, relevance,
meaningfulness of new competences

18. Kekule’s Dream & Benzene ring
…tried to solve the
geometry of
Benzene molecule
for many years by
equations…
… then had a
dream of a snake
biting its own tail
Benzene ring
Eureka!

19. Model of Analogical mapping in Intuition
Percept of
snake
Percept of Benzene’s
molecular construct
Level of
abstraction
Chain-link
structure
Chain-link
structure
Alternative
Abstract
representation n
Alternative
abstract
representation 1
Alternative
Abstract
representation x
Bridge for two remote
percepts at certain level
of abstraction
Conscious
Sub-conscious

20. Ingredients of Artificial Intuition
1. Analogical mapping
2. Cross-breeding of existing ideas/concepts
3. Extrapolation of real experiences
4. Right mixture of random explorations at
strategic locations
Exploiting
existing knowledge to
find most promising
direction of task-
specific knowledge
acquisition
Exploring
unknown regions of
knowledge space
‘Exploitation-vs-Exploration’ in
new knowledge acquisition

21. • Thank You