Fuzzy logic progress

Before meeting w/ Prof. Mendel about my fuzzy logic project, I wanted to collect my thoughts here so that our meeting would be more productive.

First, I'll start by going over the methodology that I used. The web survey was used to collect data from a fair amount of people (32), between 2 different surveys. the stimuli for the first survey was reported in the class final project [ Prof. Mendel's class project]. It used 7 emotion words as stimuli (angry, disgusted, fearful, happy, neutral, sad, surprised) and also 3 modifiers (very, sort of, not). The second experiment [ interspeech 2008 submission ] used 40 words from mood labels of blogs (the site livejournal.com, to be precise). In the interspeech paper submission, I combined these two experiments to make a computing with words type application where the words from the first experiment (excluding the modifiers) were used as inputs and the output were the words from the second experiment. The results agreed pretty well intuitively and based on an small evaluation. However, using the averaged midpoints of the type-2 fuzzy sets to find the euclidean distance gave about the same performance.


Some points that I want to ask him about are:

- is the 3-D approach (valence, activation, and dominance) and the combination methods (sum, product) valid?

- are my conclusions correct: is the fact that Euclidean distance is comparable to the fuzzy/jaccard distance metric evidence that we don't gain much by moving to type-2 fuzzy logic?


Some other ideas:

- use less data, or at least be more person-specific. This would allow me to see more interpersonal differences, which goes well with my user modeling interests. Also, I think it's clear that the data was a bit noisy, especially in the dominance dimension.

Fuzzy Logic, Tues 4 Sept. 2007

This class extended the previous class and brought up combining fuzzy statements by composition. These can be visualized by a relational matrix or a saggital diagram. These compositions can be in the same or different product spaces (if different, the cartesian product is ussed. Eg's... same product space: "x is far away from y or x is close to y"; different product space "x is close to y and y is near z". Also, the composition of two fuzzy relations can also apply when one of the fuzzy relations is just a fuzzy set. This special case is important in the representation of rules.

Fuzzy Logic, Thursday 30 Aug. 2007

In this lecture we went over the transition from crisp sets to fuzzy sets. One key point is to translate the member conditions to a membership function, one can use max() for "or" or min() for "and". Also, 1 - mu_a(x) for ~A (complement). Actually, these functions are a subset of possible functions that have the necessary properties. This brought us into t-norms and t-conorms which seems to be a cool and wierd thru the looking glass type of mathematics.

Fuzzy Logic - 28 Aug 2007

In the first lecture we covered

-some of the history of fuzzy logic: how Zadeh thought it up and had trouble with Western academics/philosophy but it caught on in the east, like Japanese electronics.

- how prof. Mendel got into FL,

- philosophy behind FL

- type 1 and 2 fuzzy systems

- fuzzy sets