The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions. – Marvin Minsky, (Minsky, 1986) p. 163 In every art form it is the emotional content that makes the difference between mere technical skill and true art.

Anvil is a tool for the annotation of audiovisual material containing multimodal dialogue. Annotation takes place on freely definable, multiple layers (tracks) by inserting time-anchored elements that hold a number of typed attribute-value pairs. Higher-level elements (suprasegmental) consist of a sequence of elements. Attributes contain symbols or cross-level links to arbitrary other elements. Anvil is highly generic (usable with different annotation schemes), platform-independent, XMLbased and fitted with an intuitive graphical user interface. For project integration, Anvil offers the import of speech transcription and export of text and table data for further statistical processing.

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This paper promotes social role awareness as a desirable capability of animated agents, that are by now strong affective reasoners, but otherwise often lack the social competence observed with humans. In particular, humans may easily adjust their behavior depending on their respective role in a socio-organizational setting, whereas their synthetic pendants tend to be driven mostly by attitudes, emotions, and personality. Our main contribution is the incorporation of `social filter programs' to mental models of animated agents. Those programs may qualify an agent's expression of its emotional state by the social context, thereby enhancing the agent's believability as a conversational partner or virtual teammate. Our implemented system is entirely webbased and demonstrates socially aware animated agents in an environment similar to Hayes-Roth's Cybercaf'e. Keywords believability, social agents, human-like qualities of synthetic agents, social dimension in communication, affective reaso...

This article builds on insights into how humans cope with emotion to guide the design of virtual humans. Although coping is increasingly viewed in the psychological literature as having a central role in human adaptive behavior, it has been largely ignored in computational models of emotion. In this paper, we show how psychological research on the interplay between human emotion, cognition and coping behavior can serve as a central organizing principle for the behavior of human-like autonomous agents. We present a detailed domain-independent model of coping based on this framework that significantly extends our previous work. We argue that this perspective provides novel insights into realizing adaptive behavior.

In this paper, we describe the Rich Representation Language (RRL) which is used in the NECA system. The NECA system generates interactions between two or more animated characters. The RRL is a formal framework for representing the information that is exchanged at the interfaces between the various NECA system modules.

Application

In this paper we introduce ALMA – A Layered Model of Affect. It integrates three major affective characteristics: emotions, moods and personality that cover short, medium, and long term affect. The use of this model consists of two phases: In the preparation phase appraisal rules and personality profiles for characters must be specified with the help of AffectML – our XML based affect modeling language. In the runtime phase, the specified appraisal rules are used to compute real-time emotions and moods as results of a subjective appraisal of relevant input. The computed affective characteristics are represented in AffectML and can be processed by sub-sequent modules that control the cognitive processes and physical behavior of embodied conversational characters. ALMA is part of the VirtualHuman project which develops interactive virtual characters that serve as dialog partners with human-like conversational skills. ALMA provides our virtual humans with a personality profile and with real-time emotions and moods. These are used by the multimodal behavior generation module to enrich the lifelike and believable qualities.

In this paper, we introduce a toolkit called SceneMaker for authoring scenes for adaptive, interactive performances. These performances are based on automatically generated and prescripted scenes which can be authored with the SceneMaker in a two-step approach: In step one, the scene flow is defined using cascaded finite state machines. In a second step, the content of each scene must be provided. This can be done either manually by using a simple scripting language, or by integrating scenes which are automatically generated at runtime based on a domain and dialogue model. Both scene types can be interweaved in our planbased, distributed platform. The system provides a context memory with access functions that can be used by the author to make scenes user-adaptive. Using CrossTalk as the target application, we describe our models and languages, and illustrate the authoring process. CrossTalk is an interactive installation with animated presentation agents which “live ” beyond the actual presentation and systematically step out of character within the presentation, both to enhance the illusion of life. The context memory enables the system to adapt to user feedback and generates data for later evaluation of user/system behavior. The SceneMaker toolkit should enable the non-expert to compose adaptive, interactive performances in a rapid prototyping approach.

People highlight the intended interpretation of their utterances within a larger discourse by a diverse set of nonverbal signals. These signals represent a key challenge for animated conversational agents because they are pervasive, variable, and need to be coordinated judiciously in an effective contribution to conversation. In this paper, we describe a freely-available cross-platform real-time facial animation system, RUTH, that animates such high-level signals in synchrony with speech and lip movements. RUTH adopts an open, layered architecture in which fine-grained features of the animation can be derived by rule from inferred linguistic structure, allowing us to use RUTH, in conjunction with annotation of observed discourse, to investigate the meaningful high-level elements of conversational facial movement for American English speakers.