R. Rzepka, K. Araki, and K. Tochinai (Japan)
affective computing, believable agents, web mining
Many researchers tend to think of WWW as an enormous database, which unfortunately is full of informational garbage that makes the web mining or knowledge-bases creation difficult. However when we started to rummage through those seemingly useless for AI purposes personal homepages with very similar contents, we imagined the human brain cells might look exactly the same. Not only the stored pieces of semantic information are important but also the number of how many times such similar data was stored. We assumed that the Internet is an interesting material for retrieving a common sense, beliefs, opinions and emotional information for various types of agents. Without any sophisticated method our system is able to easily find out that in most cases being cold1 is not pleasant and cold beer almost always “sounds nice” or that one movie star is being loved and another hated. In this paper we introduce ideas for our project (GENTA – GENeral belief reTrieving Agent) and the results of initial experiments with implementing a primitive method of retrieving basic feelings towards human user’s utterances and applying this emotional information in Inductive Learning of the speech acts.
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