During this meeting Milan presented a paper by Jong-Hwan Kim and Chi-Ho Lee: "Multi-objective evolutionary generation process for specific personalities of artificial creature," IEEE Computational Intelligence Magazine (2008).
The summary of the paper is the following:
- Goal: creating a believable artificial creature based on a genome that contains its personality.
- Personalities (the big 5 dimensions) are user-defined by assigning preference values for internal states and behaviors.
- MOEGPP – multi-objective evolutionary algorithm for maintaining the population of genomes.
- Result: the algorithm can create specific (user-defined) as well as diverse (in-between) personalities.
- Verification: the algorithm is verified by checking that the personality characteristics are maintained on a different perception scenario than what is used for optimization.
Some conclusions/questions about the paper are:
- The topic of creating believable artificial creatures was interesting, and seemed like a digression from agent-based papers discussed until now.
- The paper is not self-contained, i.e. it is missing many implementation details (e.g. what is a mask, what are the activation values αk, what is the perception scenario for testing?), some of which are to be found in other cited papers.
- The paper needs more introduction/explanation on why this particular algorithm has been used. Can we do it some other way?
- Jordan wondered whether this algorithm could be used for other OR applications that require multi-objective optimization.
- Uzay asked why the learning module has a direct input from the external environment, rather than through the perception module.
- How can learning be added to these artificial creatures, and how would it be combined with their personalities?
I also found some information about Rity and one video here:
http://rit.kaist.ac.kr/home/Artificial_Creatures_%22Rity%22_and_%22Humanoid%22
http://rit.kaist.ac.kr/~ritlab/research/Artificial_Creatures/rity.wmv
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