Abstract In this paper we present a concept of a non-task oriented conversational system (chatterbot) for Japanese, able to generate humorous utterances in response to users’ negative moods. Humorous utterances are either jokes (puns) or humorous metaphor misunderstandings. The system first detects user emotions from texts, and if they are negative, it tries to use humor to turn them into positive.... In this paper we present a concept of a non-task oriented conversational system (chatterbot) for Japanese, able to generate humorous utterances in response to users’ negative moods. Humorous utterances are either jokes (puns) or humorous metaphor misunderstandings. The system first detects user emotions from texts, and if they are negative, it tries to use humor to turn them into positive. Such an approach was tested in one of our previous works. Next the system uses one of the humor modules to generate a humorous response. User’s response to this is again analysed to check his/her emotivereaction, and on this basis the system builds an individual model of his/her preferences. In this work we describe the components of the system, its general algorithm and resources necessary to construct it. Read More Read Less

Abstract We begin this paper with revisiting the differences between descriptive and normative approach to ethics and challenge the usefulness of the latter for the field of machine ethics. We continue this reasoning and present our insights on previous trends in this field and highlight the need for a change in the approach. We highlight the need for an experimental approach... We begin this paper with revisiting the differences between descriptive and normative approach to ethics and challenge the usefulness of the latter for the field of machine ethics. We continue this reasoning and present our insights on previous trends in this field and highlight the need for a change in the approach. We highlight the need for an experimental approach to machine ethics by introducing a moral reasoning system based on Aristotelian identification of civic rhetoric with a common-sense base. And present it as a step forward in the machine ethics research bypassing theoretical disputes between philosophers. We finish this paper with the introduction to the CAMILLA project for web-crawling algorithm as the first step towards creating an Aristotelian explicit moral agent. Read More Read Less

Abstract Affect Listeners2 are applied as tools for studying the role of emotions in online communication. They need to interact both in dyads as well as in group settings with multiple users. In this paper, we present the evolution of such affective dialog systems from a focus on dyadic interaction to multi-party interaction on chat networks. Starting from experiments on the... Affect Listeners2 are applied as tools for studying the role of emotions in online communication. They need to interact both in dyads as well as in group settings with multiple users. In this paper, we present the evolution of such affective dialog systems from a focus on dyadic interaction to multi-party interaction on chat networks. Starting from experiments on the use of these dialog systems in virtual dyadic settings, we outline the requirements, design and implementation decisions necessary to apply the systems to affective interactions with multiple users. Finally, we introduce two realisations of Interactive Affective Bots designed for such interaction scenarios that integrate modelling of individuals and groups as part of their decision mechanism. Read More Read Less

Abstract In this paper, we propose a new approach based on multiple modeling by Hidden Markov Models (HMM) for isolated word recognition, which aims to maximize word recognition rate by combining several models coming from different start points. Our approach operates on 2 steps; first we create a large set of candidate markovian models for every vocabulary word by changing in... In this paper, we propose a new approach based on multiple modeling by Hidden Markov Models (HMM) for isolated word recognition, which aims to maximize word recognition rate by combining several models coming from different start points. Our approach operates on 2 steps; first we create a large set of candidate markovian models for every vocabulary word by changing in their initial models in the Expectation-Maximization (EM) training algorithm, and then we select the best subset of models. The selection of appropriate models to be combined is one of the most difficult but important factors in our approach. For this purpose, we have experimented in our previous work [1] three methods: In the first one, we selected models whose give best individual recognition rates. Secondly, we selected random models. Thirdly, we used a Genetic Algorithm (GA) to select the optimal set of models by maximizing the recognition rate of the group and minimizing the number of selected models. In this paper we propose another new selection method, in which we select models whose maximize the difference between the average of likelihoods of the current class and the average of likelihoods of all others. The performance of the proposed approach will be evaluated by comparing its effectiveness against classical markovian approach. Read More Read Less