Title: Ideas:
In general – transfer webots implementation of penalty kick to real robots for:
1) Test whether noise from the environment will pose any additional
complications for reinforcement learning
a) If so, how technique can be adjusted/changed
2) Use heterogeneous agents that employ the same memory+emotions model
3) Optimize memory mining techniques to speed up the choice of the next
behavior
4) Constrain the memory mining procedure to some maximum threshold by
which agent has to choose the next behavior, which would not lead to the
most optimal choice of the behavior. Do so by limiting the search space. See
how this will impact the reinforcement learning
5) Build a character that has some neurological disorder (i.e. Alzheimer’s
disorder, which should, on average, lead to selection of not the most optimal
behavior of an agent). Measure the performance of the agent and how it’s
PAD (or similar benchmarking technique) is affected (short term/long term)
---------- Below – network of agents (towards swarm robotics using humanoid robots) ---------
6) Test whether agents (heterogeneous or not) can function together using
same underlying model to sustain overall ‘happy’ emotion of a team thus
maintaining overall homeostatic state.
7) Try to find a universal memory+emotions model that can be used both for
penalty kick situation and team play
Rationale:
1) To be able to analyse different factors that affect memory and/or emotions of
an agent
2) Understand better how memories and emotions are interconnected (with or
without disorders)
3) Human-being decides on a behavior in a split of a second mostly using greedy
approach (less planning involved) due to the ‘artificial timeout’ of an event –
robot can try to copy that behavior from the human
4) Design a faster and more optimal behavior choosing system, which would
resemble that of a human-being
Outline:
Previous dissertations that were read by me propose many different and
interesting techniques and methods for:

models of memory

approaches to learning

measurement techniques

planning approaches

object-handling techniques

navigation techniques

object-recognition techniques

encodings

classification methods
There is enough of the information on various techniques and methods available
for different implementations (and empirical selection of the most appropriate
technique/method for a specific problem)
Existing work: So far I have read only following references from previous
dissertations:
1) Cynthia Breazeal “Function Meets Style: Insights From Emotion Theory
Applied to HRI”
Connection between cognition & emotion, which can be useful in designing autonomous robots to
operate in complex environments, where human can be working along an agent and both will have
to synchronize their progress partially by emotive expressions, which convey a lot of information in
human-human interaction
2) Albert Mehrabian “Pleasure, Arousal, Dominance: A General Framework for
Describing and Measuring Individual Differences in Temperament”
How to use PAD and its rationale. Can use PAD to measure and predict such psychological
conditions as anxiety, depression, panic, empathy, etc.
3) Dolores Canamero “Modeling Motivations and Emotions as a Basis for
Intelligent Behavior”
Cognitive stages of development of an infant, which are driven by emotions. Later on emotions
become deliberately suppressed so that behavior won’t be lead by the emotions any longer –
progression in cognitive stages. Another level of perception – existance not only to survive, but also
to maintain a ‘happy’ state. Aprirori innate structures that support the initial survival in the
environment. Different approaches to learning are mentioned.
4) Donald Norman “How might people interact with agents”
Important factors: 1. People must feel in control; 2. Researchers must be realistic; 3. Safety&Privacy
come first; 4. What is the approapriate form of Human-Agent interaction.
--------- Previous dissertations read (from HWU) of those who worked with NAO -------
5) MSc thesis: Christopher Allan “Implementing a Data Mining Approach to
Emtional Memory Modelling for RoboCup Players”
6) BSc thesis: Scott Carson “An Investigation into developing Search & Approach
behavior using NAO Robots”
Investigation of NAO RoboCup edition robot, various tests on it. Information provided seem to
be outdated (maybe due to the newer versions of SDK provided with the robot)
7) MSc thesis: Guillaume Carre “Visually Impaired and Guide Dog Automated
Helper”
Use of Kintect to map the layout of the room and find differences between images
(prior/posterior). Some good methods mentioned and possibilities for future improvements.
Overall, what is not yet achieved:
1) Objective of acting upon an object in an environment
2) Using agents embedded visual abilities instead of the external tracker
(Kinect)
3) Position and movement of a robot seem to be problematic and not as
good as is desired
4) I suspect previous implementations use planning for future behavior
(which should be avoided to be able to work in complex environments)
5) Some problems in object recognition