User talk:MaurizioGarbarino
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In my PhD studies I have been investigating whether physiological signals can be used as means for video game adaptation. With the aim of keeping high the level of enjoyment of the player, the first step was building a model for enjoyment estimation from physiological signals. So far, we obtained promising results on the relationship between variation of physiological signals and variation of enjoyment during a TORCS game session. The next step is to study how to exploit the obtained model for the actual dynamic adaptation of it. During my staying at your Lab, I would like to focus on the analysis, design and development of this second step. Here are some key aspects that i would like to investigate:
- Which aspect of a video game can be modified to best stimulate the player? How can these be modified at run-time to follow user physiological signals? Several issues have to be investigated such as the optimal time window for adaptation that can depend on the reactivity of the game controller and on how sensible is player response to the given change of status in the game.
- Is it possible to formalize a general model valid for a large number of video game genres?
- How does the adaptation by physiological signals compare to the adaptation by performance analysis?
- Which video games could be used for the experiment? Our previous work was based on TORCS. However, it might be interesting to demonstrate that the model of entertainment obtained is general enough to be successfully used with other video games as well.
The research questions you pose are interesting indeed. There are three main directions we could investigate while you are here: 1) the adaptation mechanism for TORCS (as you propose) and/or 2) steps towards re-designing the models via dissimilar experimental protocols and alternative modeling techniques and/or 3) investigations for the generalizability of the models. We can discuss the details of these directions while you are here of course.
Dear all, I think all the three lines are perfectly ok. If I should express my *preference* I would rank those as 2, 3, and 1.
It would be great if Maurizio could exploit the experience in game design to @itu and define how at theoretical and practical level issues of affective computing:
- how to design a game that could be adapted using biofeedback (meaning affective computing, eye tracking, image processing, whatever)? - what is the reasonable adaptation rate with this kind of feedback (i.e., how long does it takes to have a steady state responce)? Are we really "controlling" the subject to keep her in the flow? How? - TORCS is one example, he could proceed with that, but it would be great to define things more general and figure out if and how the protocol and the adaptivity could be verified in other games such as MsPacman or Mario or ...
All of this might require or lead to the answers we all are aiming at:
1) affective computing is really worth or performance analysis could be enough for adaptive gaming? 2) are we really measuring the user fun/engagement or just the stimuli? I mean, the heart rate is increased because of engagement or just because different movements are requested to the user?
I know this broaden even more the project proposal, but having all the ingredients on the table might help to understand the recipe ;-)
Quello che manca per dare sostanza al lavoro finale, secondo me, è la parte di adattamento, sia analisi sulle problematiche (tempistiche, possibili parametri su cui agire ecc...), sia dei risultati su un esperimento completo.
Colgo l'occasione per riassumerti come stanno andando le cose. L'ultima settimana ahimé l'ho passata a lavorare sulla relazione per il minore e sulla presentazione per l'ultimo esame (Microcontrollori & co. di Zappa). Lo scorso meeting con Yannakakis abbiamo discusso sulla criticità della qualità della normalizzazione dei segnali dati in ingresso alla rete neurale nell'approcio neuroevolutionary. Infatti, dai primi test che avevo fatto, erano emersi due problemi: diverse esecuzioni dell'algoritmo di feature selection davano sottoinsiemi di features molto disomogenei e con performance che variavano dal 72% all'80%. E inoltre, alcune delle features selezionate erano sospette in quanto "ad occhio" non avremmo mai detto che sarebbero rientrate nella selezione, come ad esempio il delta tra il tempo del valore minimo e il tempo del valore massimo dell'heart rate o della temperatura che sulla carta non dovrebbero essere molto discriminanti. Altro problema, con il numero di features che abbiamo, una rete neurale complessa ci mette molto tempo per convergere ad una soluzione ottima. L'ultimo pensiero va al fatto che le performance variano molto a seconda del fold considerato (durante la crossvalidazione). Questo è sintomo del fatto che potrebbero esserci dei cluster di player per cui un approccio di player modeling potrebe portare a migliori performance. Però su quest'ultimo aspetto non credo di avere tempo a sufficienza per investigare...
