Proceedings of the SAB'06 Workshop on Adaptive Approaches for Optimizing Player Satisfaction in Computer and Physical Games

These proceedings contain the papers presented at the Workshop on Adaptive approaches for Optimizing Player Satisfaction in Computer and Physical Games held at the Ninth international conference on the Simulation of Adaptive Behavior (SAB’06): From Animals to Animats 9 in Rome, Italy on 1 October 2006. We were motivated by the current state-of-the-art in intelligent game design using adaptive approaches. Artificial Intelligence (AI) techniques are mainly focused on generating human-like and intelligent character behaviors. Meanwhile there is generally little further analysis of whether these behaviors contribute to the satisfaction of the player. The implicit hypothesis motivating this research is that intelligent opponent behaviors enable the player to gain more satisfaction from the game. This hypothesis may well be true; however, since no notion of entertainment or enjoyment is explicitly defined, there is therefore little evidence that a specific character behavior generates enjoyable games. Our objective for holding this workshop was to encourage the study, development, integration, and evaluation of adaptive methodologies based on richer forms of humanmachine interaction for augmenting gameplay experiences for the player. We wanted to encourage a dialogue among researchers in AI, human-computer interaction and psychology disciplines who investigate dissimilar methodologies for improving gameplay experiences. We expected that this workshop would yield an understanding of state-ofthe- art approaches for capturing and augmenting player satisfaction in interactive systems such as computer games. Our invited speaker was Hakon Steinø, Technical Producer of IO-Interactive, who discussed applied AI research at IO-Interactive, portrayed the future trends of AI in computer game industry and debated the use of academic-oriented methodologies for augmenting player satisfaction. The sessions of presentations and discussions where classified into three themes: Adaptive Learning, Examples of Adaptive Games and Player Modeling. The Workshop Committee did a great job in providing suggestions and informative reviews for the submissions; thank you! This workshop was in part supported by the Danish National Research Council (project no: 274-05-0511). Finally, thanks to all the participants; we hope you found this to be useful!peer-reviewe

Bases comportementales, moléculaires et cellulaires gouvernant l'apprentissage ambigu et la mémoire chez la drosophile

Extraire les liens prédictifs au sein d'un environnement permet d'appréhender la structure logique du monde. Ceci constitue la base des phénomènes d'apprentissage qui permettent d'établir des liens associatifs entre des évènements de notre entourage. Tout environnement naturel englobe une grande diversité de stimuli composés (i.e. intégrant plusieurs éléments). La façon dont ces stimuli composés sont appréhendés et associés à un renforcement éventuel (i.e. évènement plaisant ou aversif) est un thème fondamental de l'apprentissage associatif. Théoriquement, un stimulus composé AB peut être appris comme la somme de ses composants (A+B), un traitement dit élémentaire, comme un stimulus à part entière (traitement configural, AB=X) ou encore comme une entité comportant à la fois certaines caractéristiques de ses composants ainsi que des propriétés uniques (ou Indice Unique, AB = A+B+u). Ces deux dernières théories permettent notamment d'expliquer la résolution de problèmes ambigus tels que le Negative Patterning (NP) au cours duquel les composants du stimulus AB sont renforcés lorsque présentés seuls mais pas lorsqu'ils sont présentés en tant que composé. Bien que les réseaux neuronaux impliqués dans l'apprentissage associatif élémentaire soient bien connus, les mécanismes permettant la résolution d'apprentissages non élémentaires sont encore peu compris. Dans cette étude, nous démontrons pour la première fois que la Drosophile est capable d'apprentissage non-élémentaire de type NP. L'étude comportementale de la résolution du NP par les mouches montre qu'il passe par la répétition de cycles de conditionnement conduisant à un changement de représentation du mélange AB, s'éloignant peu à peu de la représentation de ses composants A et B. Nous développons ensuite un modèle computationnel à partir de données in vivo sur l'architecture et le fonctionnement des réseaux neuronaux de l'apprentissage olfactif chez la Drosophile, ce qui nous permet de proposer un mécanisme théorique permettant d'expliquer l'apprentissage du NP et dont la validité peut être testée grâce à des outils neurogénétiques. Lors d'un apprentissage de NP, les mouches acquièrent tout d'abord un premier lien associatif entre les composants A et B associés au renforcement, créant par la même occasion une ambiguïté avec leur mélange AB, présenté sans renforcement. Au cours des cycles de conditionnement, les représentations de A et B vis-à-vis de AB sont modulées de façon différentielle, inhibant progressivement la réponse neuronale au stimulus non renforcé tout en renforçant la réponse aux stimuli renforcés. Cette modulation augmente le contraste entre A, B et AB et permet aux drosophiles de résoudre la tâche de NP. Nous identifions les neurones APL (Anterior Paired Lateral) comme implémentation plausible de ce mécanisme, car l'engagement de leur activité inhibitrice spécifiquement durant la présentation de AB est nécessaire pour acquérir le NP sans altérer leurs capacités d'apprentissage dans des tâches non-ambiguës. Nous explorons ensuite l'implication des neurones APL dans un contexte plus général de résolution d'apprentissages ambigus. Pour conclure, notre travail établit la Drosophile comme modèle d'étude d'apprentissage non élémentaire, en proposant une première exploration des réseaux neuronaux sous-jacents à l'aide d'outils uniques à ce modèle. Il ouvre la voie à de nombreux projets dédiés à la compréhension des mécanismes neuronaux permettant aux animaux d'extraire des liens associatifs robustes dans un environnement complexe.Animals' survival heavily relies on their ability to establish causal relationships within their environment. That is made possible through learning experiences during which animals build associative links between the events they are exposed to. Most of the encountered stimuli are actually compounds, the constituents of which may have been reinforced (i.e., associated with a pleasant or unpleasant stimulus) in a different, sometimes opposed way. How compounds are perceived and processed is a central topic in the field of associative learning. In theory, a given compound AB may be learnt as the sum of its components (A+B), which is referred to as "Elemental learning", but it may also be learnt as a distinct stimulus (which Is called "Configural learning"). Finally, AB may bear both constituent-related and compound-specific features called "Unique Cues" (AB = A+B+u). Configural and unique cue processing enable the resolution of ambiguous tasks such as Negative Patterning (NP), during which A and B are reinforced when presented alone but not in a compound AB. Although neural correlates of simple associative learning are well described, those involved in non-elemental learning remain unclear. In this project, we rework a typical olfactory conditioning protocol based on semi-automated olfactory/electric shocks association, allowing us to demonstrate for the first time that Drosophila is able to solve NP tasks. Behavioural study of NP solving shows that its resolution relies on training repetition leading to a gradual change in the compound AB representation, shifting away from its constituents and thus becoming easier to distinguish. Next, we develop a computational model of olfactory associative learning in drosophila based on structural and functional in vivo data. Exploratory simulations of the model allow us to identify a theoretical mechanism enabling NP acquisition, the validity of which can be tested in vivo using neurogenetical tools only available in Drosophila. We propose that during a NP training, flies first acquire associative links between A, B and their reinforcement, which induces an ambiguity as the compound AB is presented without reinforcement. However, over the course of training cycles, non-reinforced stimuli representation is inhibited while the reinforced stimuli representation is consolidated. This differential modulation eventually leads to a shift in odours representation allowing flies to better distinguish between the constituents and their compound thus facilitating NP resolution. We identify APL (Anterior Paired Lateral) neurons as a plausible implementation of this theoretical mechanism, as APL inhibitory activity is specifically engaged during the non-reinforced stimulus presentation, which is necessary for NP acquisition but dispensable for non-ambiguous forms of learning. Lastly, we explore APL role in a broader context of ambiguity resolution. In conclusion, our work validates Drosophila as a robust model to investigate non-elementary learning, and present a promising model of the underlying neural mechanisms using a combination of behaviour, modelling and neurogenetical tools. We believe this opens the way to numerous interesting projects focused on understanding how animals extract robust associations in a complex world

Representation in Cognitive Science

"Our thoughts are meaningful. We think about things in the outside world; how can that be so? This is one of the deepest questions in contemporary philosophy. Ever since the 'cognitive revolution', states with meaning-mental representations-have been the key explanatory construct of the cognitive sciences. But there is still no widely accepted theory of how mental representations get their meaning. Powerful new methods in cognitive neuroscience can now reveal information processing in the brain in unprecedented detail. They show how the brain performs complex calculations on neural representations. Drawing on this cutting-edge research, Nicholas Shea uses a series of case studies from the cognitive sciences to develop a naturalistic account of the nature of mental representation. His approach is distinctive in focusing firmly on the 'subpersonal' representations that pervade so much of cognitive science. The diversity and depth of the case studies, illustrated by numerous figures, make this book unlike any previous treatment. It is important reading for philosophers of psychology and philosophers of mind, and of considerable interest to researchers throughout the cognitive sciences.

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion