This project focuses on artificial social interactions where things get nasty and mean. The purpose is training in social 'facework' -- managing the situation so that participants maintain their social dignity or 'face'. This can be especially delicate in cross-cultural contexts, where assumptions about social protocols and the emotional associations of utterances and gestures may differ. The purpose of this project is two-fold. First, it is intended as a training system, so that users might learn the do's and don'ts of social interactions in different cultures and different situations. The knowledge base draws from existing theories of diplomacy, facework, and (im)politeness theory. The other goal is to provide a platform for observation and experimentation of social interaction in an artificial, virtual setting in order to improve these theories.

Human decision making is a complex process. In the field of Artificial Intelligence, decision making is considered an essential aspect of autonomous agents. Research of human decision behaviour shows that emotions play a decisive role. We present a computational model for creating an emotional memory and an algorithm for decision making based on the collected information in the memory. We concentrate on simulating human behaviour as there is not always one perfect way to reach a goal but alternatives that are more advantageous. For evaluation purposes a gambling task, performed by real subjects, was created for the modelled agent. The results show that the decision behaviour of the modelled agent is comparable with real subjects.

In this paper, we present a programming paradigm for AI problem solving based on computational concepts drawn from Affective Computing. It is believed that emotions participate in human adaptability and reactivity, in behaviour selection and in complex and dynamic environments. We propose to define a mechanism inspired from this observation for general AI problem solving. To this purpose, we synthesize emotions as programming abstractions that represent the perception of the environment's state w.r.t. predefined heuristics such as goal distance, action capability,etc. We first describe the general architecture of this "emotion-oriented" programming model. We define the vocabulary that allows programmers to describe the problem to be solved (i.e. the environment), and the action selection function based on emotion abstractions (i.e. the agent's behaviours). We then present the runtime algorithm that builds emotions out of the environment, stores them in the agent's memory, and selects behaviours accordingly. We present the implementation of a classical labyrinth problem solver in this model. We show that the solutions obtained by this easy-to-implement emotion-oriented program are of good quality while having a reduced computational cost.

Many studies have been conducted on how to detect emotion classes or magnitudes from multimedia information such as text, audio, and images. However, the methods that can use predicted emotion classes and magnitudes to render emotion expressions in Embodied Conversational Agents (ECA) are still unclear. This paper proposes a computer graphics methodology that uses predicted non-linear regression values to render facial expressions using mesh morphing techniques. Results of the rendering technique are presented and discussed.

Researchers have recognized the importance of classifying personality through discourse for many years. However, this line of research tends to focus almost exclusively on the personality categories known as the Big Five factors. Though this information is certainly valuable, it may also be useful to categorize personality based on the Leary’s Interpersonal Circumplex model which emphasizes a predictive function. In this paper we construct the data set for personality annotation among six dimensions (based on a coding scheme developed from Leary’s Interpersonal Circumplex) for players using a chat interaction in an epistemic game, Land Science. Our results indicate that overall personality annotation is reliable (Average Kappa = 0.65) with the highest reliability for the competitive dimension and the lowest reliability for the leading dimension.

Depression and other mood disorders are common and disabling disorders. We present work towards an objective diagnostic aid supporting clinicians using affective sensing technology with a focus on acoustic and statistical features from spontaneous speech. This work investigates differences in expressing positive and negative emotions in depressed and healthy control subjects as well as whether initial gender classification increases the recognition rate. To this end, spontaneous speech from interviews of 30 subjects of each depressed and controls was analysed, with a focus on questions eliciting positive and negative emotions. Using HMMs with GMMs for classification with 30-fold cross-validation, we found that MFCC, energy and intensity features gave highest recognition rates when female and male subjects were analysed together. When the dataset was first split by gender, log energy and shimmer features, respectively, were found to give the highest recognition rates in females, while it was loudness for males. Overall, correct recognition rates from acoustic features for depressed female subjects were higher than for male subjects. Using statistical features, we found that the response time and average syllable duration were longer in depressed subjects, while the interaction involvement and articulation rate were higher in control subjects.

Affective computing is an emerging field that aspires to narrow the communicative gap between the highly emotional human and the emotionally challenged computer by developing computational systems that recognize and respond to the affective states (such as moods, emotions) of the user. One of the basic principles of affective computing is that automatically recognizing and responding to a user's affective states during interactions with a computer can enhance the quality of the interaction, thereby making the computer interface more usable, enjoyable, and effective. For example, an affect-sensitive learning environment that detects and responds to student frustration is expected to increase motivation, engagement, and learning gains. Although the last decade has been ripe with theory and applications relevant to affective computing, these advances are accompanied by a new set of challenges. By providing a framework to discuss and evaluate novel research, we hope to leverage recent advances to speed up future research in this area.

Sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean vectors. Here we present an extension of the original SDM, the Integer SDM that uses modular arithmetic integer vectors rather than binary vectors. This extension preserves many of the desirable properties of the original SDM: auto-associativity, content addressability, distributed storage, and robustness over noisy inputs. In addition, it improves the representation capabilities of the memory and is more robust over normalization. It can also be extended to support forgetting and reliable sequence storage.

One of the challenges for artificial agents is managing the complexity of their environment as they learn tasks especially if they are grounded in the physical world. A scalable solution to address the state explosion problem is thus a prerequisite of physically grounded, agentbased systems. This paper presents a framework for developing grounded, symbolic representations aimed at scaling subsequent learning as well as forming a basis for symbolic reasoning. These symbols partition the environment so the agent need only consider an abstract view of the original space when learning new tasks and allows it to apply acquired symbols to novel situations.

In this paper we present a new method for ontology selection in a reuse context. The novel feature of this method is the iterative selection of the reused ontologies. Ontology selection is guided by the user according to his requirements and his perception to the target domain. Starting from a first selected ontology, the concepts with the weakest density are identified then the ontology developer is enabled to choose among them the ones to be refined in order to cover a specific scope of the domain.