Fault recovery recommendation

Information on Recovery Recommendation System (RECORS) is given in viewgraph form. The system goal is to provide intelligent aiding for monitoring, diagnosis and response to aircraft system failures. Information is given on levels of abstraction, RECORS implementation, and architecture

Perturbance: Unifying Research on Emotion, Intrusive Mentation and Other Psychological Phenomena with AI

Intrusive mentation, rumination, obsession, and worry, referred to by Watkins as "repetitive thought" (RT), are of great interest to psychology. This is partly because every typical adult is subject to "RT". In particular, a critical feature of "RT" is also of transdiagnostic significance—for example obsessive compulsive disorder, insomnia and addictions involve unconstructive "RT". We argue that "RT" cannot be understood in isolation of models of whole minds. Researchers must adopt the designer stance in the tradition of Artificial Intelligence augmented by systematic conceptual analysis. This means developing, exploring and implementing cognitive-affective architectures. Empirical research on "RT" needs to be driven by such theories, and theorizing about "RT" needs to consider such data. We draw attention to H-CogAff theory of mind (motive processing, emotion, etc.) and a class of emotions it posits called perturbance (or tertiary emotions), as a foundation for the research programme we advocate. Briefly, a perturbance is a mental state in which motivators tend to disrupt executive processes. We argue that grief, limerence (the attraction phase of romantic love) and a host of other psychological phenomena involving "RT" should be conceptualized in terms of perturbance and related design-based constructs. We call for new taxonomies of "RT" in terms of information processing architectures such as H-CogAff. We claim general theories of emotion also need to recognize perturbance and other architecture-based aspects of emotion. Meanwhile "cognitive" architectures need to consider requirements of autonomous agency, leading to cognitive affective architectures

Computational Modeling of Emotion: Towards Improving the Inter- and Intradisciplinary Exchange

International audienceThe past years have seen increasing cooperation between psychology and computer science in the field of computational modeling of emotion. However, to realize its potential, the exchange between the two disciplines, as well as the intradisciplinary coordination, should be further improved. We make three proposals for how this could be achieved. The proposals refer to: 1) systematizing and classifying the assumptions of psychological emotion theories; 2) formalizing emotion theories in implementation-independent formal languages (set theory, agent logics); and 3) modeling emotions using general cognitive architectures (such as Soar and ACT-R), general agent architectures (such as the BDI architecture) or general-purpose affective agent architectures. These proposals share two overarching themes. The first is a proposal for modularization: deconstruct emotion theories into basic assumptions; modularize architectures. The second is a proposal for unification and standardization: Translate different emotion theories into a common informal conceptual system or a formal language, or implement them in a common architecture

Affective Game Engines: Motivation and Requirements

The tremendous advances in gaming technologies over the past decade have focused primarily on the physical realism of the game environment and game characters, and the complexity and performance of game simulations and networking. However, current games are still lacking in the affective realism of the game characters, and the social complexity and realism of their interactions. To achieve the next leap in the level of engagement and effectiveness, particularly in the arena of serious games, gaming research needs to focus on enhancing the social and affective complexity and realism of the game characters, their interaction, and the game narrative as a whole. To achieve these goals, games and game development tools will need to provide functionality to support the recognition of user and game character emotions, real-time adaptation and appropriate responses to these emotions, and more realistic expression of emotions in game characters and user avatars. To support these functionalities, the games will need to construct affective models of the players, and include computational models of emotion within the game characters. In this paper, we discuss these functionalities, and suggest a set of requirements for an affective game engine, capable of supporting the development of more affectively realistic, engaging, and effective games. The discussion is organized around the functional requirements and the computational tasks necessary to support them. We emphasize the importance of selecting appropriate semantic primitives, and discuss how existing methods and techniques in affective computing and computational affective modeling contribute to the development of affective game engines and game development tools

Modeling Interaction Between Metacognition and Emotion in a Cognitive Architecture

While research in metacognition has grown significantly in the past 10 years, there has been a relative lack of research devoted to the focused study of the interactions between metacognition and affective processes. Computational models represent a useful tool which can help remedy this situation by constructing causal models of demonstrated correlational relationships, and by generating empirical hypotheses which can be verified experimentally. In this paper we describe enhancements to an existing cognitive–affective architecture that will enable it to perform a subset of metacognitive functions. We focus on modeling the role of a specific metacognitive factor, the feeling of confidence (FOC), and the anxiety-linked metacognitive strategy of emotionfocuse

Modeling the Mechanisms of Emotion Effects on Cognition

Emotions exert a profound influence on cognitive processes, both the fundamental processes mediating cognition, such as attention and memory, and higher-level processes including decision-making and learning. A number of emotion effects on cognition have been identified, but their mechanisms are not yet understood. In this paper I describe a methodology for modeling the effects of emotion on cognition, within a symbolic cognitive-affective architecture. The primary objective of the approach is to facilitate the construction of alternative mechanisms of observed emotion effects. The paper describes how the effects of anxiety are modeled and how alternative mechanisms of these effects can be explored

What Are We Modeling When We Model Emotion?

The past 15 years have witnessed a rapid growth in computational modeling of emotion and cognitive-affective architectures. Architectures are being built both to elucidate mechanisms of emotions, and to enhance believability and effectiveness of synthetic agents and robots. Yet in spite of the many emotion models developed to date, there is a lack of consistency, and clarity, regarding what exactly it means to ‘model emotions’. The purpose of this paper is to attempt to deconstruct the vague term ‘emotion modeling’ by (1) suggesting that we view emotion models in terms of two fundamental categories of processes: emotion generation and emotion effects; and (2) identifying some of the fundamental computational tasks necessary to implement these processes. These ‘model building blocks’ can then provide a basis for the development of more systematic guidelines for the theoretical and data requirements, and the representational and reasoning alternatives, in emotion modeling. Identification of a set of generic computational tasks is also a good starting point for a systematic comparison of alternative approaches