A survey of normal form covers for context-free grammars

An overview is given of cover results for normal forms of context-free grammars. The emphasis in this paper is on the possibility of constructing ɛ-free grammars, non-left-recursive grammars and grammars in Greibach normal form. Among others it is proved that any ɛ-free context-free grammar can be right covered with a context-free grammar in Greibach normal form. All the cover results concerning the ɛ-free grammars, the non-left-recursive grammars and the grammars in Greibach normal form are listed, with respect to several types of covers, in a cover-table

Do Embodied Conversational Agents Know When to Smile?

We survey the role of humor in particular domains of human-to-human interaction with the aim of seeing whether it is useful for embodied conversational agents to integrate humor capabilities in their models of intelligence, emotions and interaction (verbal and nonverbal) Therefore we first look at the current state of the art of research in embodied conversational agents, affective computing and verbal and nonverbal interaction. We adhere to the 'Computers Are Social Actors' paradigm to assume that human conversational partners of embodied conversational agents assign human properties to these agents, including humor appreciation

Preface to Computational Humor 2012

Like its predecessors in 1996 (University of Twente, the Netherlands) and 2002 (ITC-irst, Trento, Italy), this Third International Workshop on Computational Humor (IWCH 2012) focusses on the possibility to find algorithms that allow understanding and generation of humor. There is the general aim of modeling humor, and if we can do that, it will provide us with lots of information about our cognitive abilities in general, such as reasoning, remembering, understanding situations, and understanding conversational partners. But it also provides us with information about being creative, making associations, storytelling and language use. Many more subtleties in face-to-face and multiparty interaction can be added, such as using humor to persuade and dominate, to soften or avoid a face threatening act, to ease a tense situation or to establish a friendly or romantic relationship. One issue to consider is: when is a humorous act appropriate

On satisfying the LL-iteration theorem

It is shown that the conditions in the iteration theorem for LL-languages are not sufficient to characterize these languages

'Girlfriends and Strawberry Jam’: Tagging Memories, Experiences, and Events for Future Retrieval

In this short paper we have some preliminary thoughts about tagging everyday life events in order to allow future retrieval of events or experiences related to events. Elaboration of these thoughts will be done in the context of the recently started Network of Excellence PetaMedia (Peer-to-Peer Tagged Media) and the Network of Excellence SSPNet (Social Signal Processing), to start in 2009, both funded by the European Commission's Seventh Framework Programme. Descriptions of these networks will be given later in this paper

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

Simple chain grammars

A subclass of the LR(0)-grammars, the class of simple chain grammars is introduced. Although there exist simple chain grammars which are not LL(k) for any k, this new class of grammars is very close related to the class of LL(1) and simple LL(1) grammars. In fact it can be proved (not in this paper) that each simple chain grammar has an equivalent simple LL(1) grammar. A very simple (bottom-up) parsing method is provided. This method follows directly from the definition of a simple chain grammar and can easily be given in terms of the well-known LR(0) parsing method

Towards Simulating Humans in Augmented Multi-party Interaction

Human-computer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in the European AMI research project

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