In affective natural language generation (NLG) a major aim is to be able to influence the emotional effects evoked in the addressee through the intelligent use of language. While previous work has shown that varying the form of the language, while keeping the content the same, can have a measurable effect on the emotions of the addressee, we report here on work which investigated which linguistic techniques to give the text a more or less positive slant contribute to these emotional effects. We report on three studies in which texts that gave positive feedback on an IQ test performance were tested for emotional effects on the recipient. The first study followed a comparison method on the sentence level, and the second study compared the texts as a whole. In both of these, participants were asked to rate the emotional effects that they thought the texts would have. On the other hand, in the third study different types of feedback were evaluated in a context of use, where participants were asked to perform an IQ test, read their feedback and report on their actual emotional state. In the first two studies, participants confirmed that the texts contained essentially the same content. The positive slanting techniques generally resulted in texts that were judged to be either positive or equal to neutral texts, although the effects were less strong than in previous work, which employed a variety of techniques, and there were a number of exceptions which impact on the usefulness of these techniques. However the IQ-test experiment did not show any emotional effects arising from variation in the form of the feedback. We reflect on possible reasons for this outcome and what it might mean for further work on Affective NLG.

The aim of this paper is threefold: (1) it explores methods for the detection of affective states in text, (2) it presents the usage of such affective cues in a conversational system and (3) it evaluates its effectiveness in a virtual reality setting. Valence and arousal values, used for generating facial expressions of users' avatars, are also incorporated into the dialog, helping to bridge the gap between textual and visual modalities. The system is evaluated in terms of its ability to: (i) generate a realistic dialog, (ii) create an enjoyable chatting experience, and (iii) establish an emotional connection with participants. Results show that user ratings for the conversational agent match those obtained in a Wizard of Oz setting.

Since the reinvigoration of emotions research, many computationalmodels of emotion have been developed. None ofthese models, however, fully address the integration of emotiongeneration and emotional effect in the context of cognitiveprocesses. This paper seeks to unify various modelsof computational emotions while fully integrating with workdone in cognitive architectures. We propose a perspective onhow this integration would occur and EmoCog, a cognitivearchitecture with mechanisms for emotion generation and effects.

It has been also demonstrated that emotional In its basic underpinning assumptions, cognitive load states (e.g., negative mood or anxiety) directly influence theory relies on the analogy between the information cognitive task performance and the operation of working processing aspects of evolution by natural selection and memory, while less evidence exists about the effect of the human cognition (Sweller & Sweller, 2006). It considers emotional content of the processed information (e.g., both biological evolution and human cognition as Kensinger & Corkin, 2003).

Affective computing (AC) 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 and emotions) of the user. One of the basic tenets behind AC 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. This special track will serve as a forum to unite researchers from the interdisciplinary arena that encompasses computer science, engineering, HCI, psychology, and education to exchange ideas, frameworks, methods, and tools relating to affective computing. Although the last decade has been ripe with theory and applications relevant to AC, these advances are accompanied by a new set of challenges.

We propose a new interpretation for a certain kind of ASO preference rules to handling prioritized symptoms. Our interpretation is based on the idea of dependence based priority propagation. Then, we present a fixpoint based answer set optimization method under such an interpretation.

Graph-based knowledge discovery has become a powerful tool in the machine learning and data mining areas. It provides a flexible and natural data representation to describe real world domains. In this research work we present a novel algorithm for graph-based approaches to deal with numerical attributes during the data processing phase implemented in the Subdue system. Our experimental results show that the use of numerical attributes increased classification accuracy in the Mutagenesis and PTC domains in 22% compared to the Subdue system when it does not use our numerical attributes handling approach. Our method also outperforms other author's results for the same domains, around 7% for the Mutagenesis domain and around 17% for the PTC domain.

We present research in decision tree analysis that studies a data set and finds new patterns that were not obvious using statistical methods. Our method is applied to a database of accommodative esotropic patients. Accommodative esotropia is an eye disease that when left untreated leads to blindness. Patients whose muscles deteriorate often need corrective surgery, since less invasive methods of treatment tend to fail in these patients. Using a learn and prune methodology, decision tree analysis of 354 accommodative esotropic patients led to the discovery of two conjunctive variables that predicted deterioration in the initial year of treatment better than what was previously determined using standard statistical methods.

Multi-Attribute Reverse Auctions (MARAs) are excellent protocols to automate negotiation among sellers. Eliciting the buyer0s preferences and determining the winner are both challenging problems for MARAs. To solve these problems, we propose two algorithms namely MAUT* and CP-net*, which are respectively the improvement of the Multi-Attribute Utility Theory (MAUT) and constrained CP-net. The buyers can now express conditional, qualitative as well as quantitative preferences over the item attributes. To evaluate the performance in time of the proposed algorithms, we conduct an experimental study on several problem instances. The results favor MAUT* in most of the cases.

We address the problem of simulate democratic elections via a set of competing agents.We propose a logical model based on a set of non-cooperative agents which compete for attracting a maximum number of votes from a population. Each agent builds a set of strategies (formed by the promises, actions and proposals of the agent) used to convince to the potential voters.