Kinds of conversational cooperation

The Cooperative Principle was the organizing principle in Grice’s pragmatics. More recently, cooperation has played a reduced role in pragmatic theory. The principle has been attacked on the grounds that people are not always or generally cooperative. One response to that objection is to say that there are two kinds of cooperation and Grice’s principle only applies to the narrower kind, which concerns linguistic or formal cooperation. I argue that such a distinction is only defensible if it is accepted that linguistic cooperation can be determined by an extra-linguistic goal. To make distinctions among types of cooperation is helpful but this strategy does not remove all concerns about speakers who are not fully cooperative and in particular the operation of the principle needs to be qualified in situations of conflict of interest. I propose that the principle, once qualified, can have a significant continuing role in pragmatic theory

Conversational Agents, Humorous Act Construction, and Social Intelligence

Humans use humour to ease communication problems in human-human interaction and \ud in a similar way humour can be used to solve communication problems that arise\ud with human-computer interaction. We discuss the role of embodied conversational\ud agents in human-computer interaction and we have observations on the generation\ud of humorous acts and on the appropriateness of displaying them by embodied\ud conversational agents in order to smoothen, when necessary, their interactions\ud with a human partner. The humorous acts we consider are generated spontaneously.\ud They are the product of an appraisal of the conversational situation and the\ud possibility to generate a humorous act from the elements that make up this\ud conversational situation, in particular the interaction history of the\ud conversational partners

An Argumentation-based Perspective over the Social IoT

The crucial role played by social interactions between smart objects in the Internet of Things is being rapidly recognized by the Social Internet of Things (SIoT) vision. In this paper, we build upon the recently introduced vision of Speaking Objects – “things” interacting through argumentation – to show how different forms of human dialogue naturally fit cooperation and coordination requirements of the SIoT. In particular, we show how speaking objects can exchange arguments in order to seek for information, negotiate over an issue, persuade others, deliberate actions, and so on, namely, striving to reach consensus about the state of affairs and their goals. In this context, we illustrate how argumentation naturally enables such a form of conversational coordination through practical examples and a case study scenario

No Grice: Computers that Lie, Deceive and Conceal

In the future our daily life interactions with other people, with computers, robots and smart environments will be recorded and interpreted by computers or embedded intelligence in environments, furniture, robots, displays, and wearables. These sensors record our activities, our behavior, and our interactions. Fusion of such information and reasoning about such information makes it possible, using computational models of human behavior and activities, to provide context- and person-aware interpretations of human behavior and activities, including determination of attitudes, moods, and emotions. Sensors include cameras, microphones, eye trackers, position and proximity sensors, tactile or smell sensors, et cetera. Sensors can be embedded in an environment, but they can also move around, for example, if they are part of a mobile social robot or if they are part of devices we carry around or are embedded in our clothes or body. \ud \ud Our daily life behavior and daily life interactions are recorded and interpreted. How can we use such environments and how can such environments use us? Do we always want to cooperate with these environments; do these environments always want to cooperate with us? In this paper we argue that there are many reasons that users or rather human partners of these environments do want to keep information about their intentions and their emotions hidden from these smart environments. On the other hand, their artificial interaction partner may have similar reasons to not give away all information they have or to treat their human partner as an opponent rather than someone that has to be supported by smart technology.\ud \ud This will be elaborated in this paper. We will survey examples of human-computer interactions where there is not necessarily a goal to be explicit about intentions and feelings. In subsequent sections we will look at (1) the computer as a conversational partner, (2) the computer as a butler or diary companion, (3) the computer as a teacher or a trainer, acting in a virtual training environment (a serious game), (4) sports applications (that are not necessarily different from serious game or education environments), and games and entertainment applications

Training soft skills with software

Most trainings of communicative behavior focus on fostering the observable speech productive behavior (i.e. speaking). The individual cognitive processes underlying speech receptive behavior (hearing and understanding utterances) thus are often neglected. This is due to the fact that speech receptive behavior cannot be accessed in the midst of a conversation and that its training is very time-consuming. Computer-supported learning environments employed as cognitive tools can help to foster speech receptive behavior. This article discusses the fostering of speech receptive behavior and the possibilities of using software for this purpose. The computer-supported learning environment CaiMan© which is based on these ideas is presented. Finally, seven factors of success for the integration of software into the training of soft skills are derived from empirical research.Kommunikationstrainings widmen sich meist der Förderung des beobachtbaren sprachproduktiven Handelns (d.h. des Sprechens). Die individuellen kognitiven Prozesse, die dem sprachrezeptiven Handeln (Hören und Verstehen) zugrunde liegen, werden häufig vernachlässigt. Dies wird dadurch begründet, dass sprachrezeptives Handeln in einer kommunikativen Situation nur schwer zugänglich und die Förderung der individuellen Prozesse sprachrezeptiven Handelns sehr zeitaufwändig ist. Computerunterstützte Lernumgebungen können als kognitive Tools die Förderung sprachrezeptiven Handelns unterstützen. Dieser Forschungsbericht erörtert Möglichkeiten und Probleme der Förderung sprachrezeptiven Handelns und des Einsatzes von computerunterstützten Lernumgebungen für dessen Förderung. Darauf aufbauend wird die computerunterstützte Lernumgebung CaiMan© vorgestellt und beschrieben. Abschließend werden sieben Erfolgsfaktoren aus der empirischen Forschung zur Lernumgebung CaiMan© abgeleitet

Getting to the Core of Role: Defining Interpreters' Role Space

This article describes a new model of interpreted interactions that will help students as well as experienced practitioners define and delineate the decisions that they make. By understanding the dimensions that comprise the concept we call role, interpreters can more effectively allow participants to have successful communicative interactions