Twelfth International Workshop Modelling and Reasoning in Context

This paper presents a brief overview of requirements for development and evaluation of human centred explainable systems. We propose three perspectives on evaluation models for explainable AI that include intrinsic measures, dialogic measures and impact measures. The paper outlines these different perspectives and looks at how the separa-tion might be used for explanation evaluation bench marking and integration into design and development. We propose several avenues for future work. Abstract EXplainable Artiﬁcial Intelligence (XAI) has recently become a very active domain, mainly due to the extensive development of black-box models such as neural networks. Recent XAI objectives have been deﬁned in the state-of-the-art, for which speciﬁc approaches have been proposed. Implicit links can be found between XAI and other domains, especially related to knowledge and neural networks. We here aim to highlight these implicit links. We present a narrative review of research works in two domains: (i) Knowledge domain with focus on Knowledge Discovery and Representation, and (ii) Representation Learning. We discuss the similarity and joining points between these domains and XAI. We conclude that, in order to make black-boxes more transparent, XAI approaches should be more inspired and take advantage of past and recent works in Knowledge and Representation Learning domains. Through this paper, we offer an entry point to the domain of XAI for both multidisciplinary researchers and specialists in AI, as well for AI knowledgeable users. Abstract The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efﬁciency. We aim to design a novel model representation of the personal context and design a learning process for better integration with machine learning. We aim to implement these elements into a modern system architecture focus in real-life environments. Also, we show how our proposal can improve in speciﬁcally related work papers. Finally, we are moving forward with a better personal context representation with an improved model, the implementation of the learning process, and the architectural design of these components. Abstract The paper provides insights into two main threads of analysis of the BIRAFFE2 dataset concerning the associations between personality and physiological signals and concerning the game logs’ generation and processing. Alongside the presentation of results, we propose the generation of event-marked maps as an important step in the exploratory analysis of game data. The paper concludes with a set of guidelines for using games as a context-rich experimental environment. Abstract Many online comments on social media platforms are hateful, humorous, or sarcastic. The sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments, which leads to misinterpretations by the existing sentiment analysis models. A lot of research has already been done to detect sarcasm in the text using user-based, topical, and conversational information but not much work has been done to use inter-sentence contextual information for detecting the same. This paper proposes a new deep learning architecture that uses a novel Bidirectional Inter-Sentence Contextual Attention mechanism (Bi-ISCA) to capture inter-sentence dependencies for detecting sarcasm in the user-generated short text using only the conversational context. The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words & phrases responsible for invoking sarcasm. Bi-ISCA generates results comparable to the state-of-the-art on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter). To the best of our knowledge, none of the existing models use an inter-sentence contextual attention mechanism to detect sarcasm in the user-generated short text using only conversational context. Abstract With the always increasing availability of sensor devices, there is constant unseen monitoring of our environment. A physical activity has an impact on more sensor modalities than we could imagine. It is so vivid that distinctive patterns in the data look almost interpretable. Such knowledge, which is innate to humans, ought to be encoded and reason upon declaratively. We demonstrate the power of Markov Logic Networks for encoding uncertain knowledge to discover interesting situations from the observed evidence. We formally relate distin-guishable patterns from the sensor data with knowledge about the environment and generate a rule basis for verifying and explaining occurred phenom-ena. We demonstrate an implementation on a real dataset and present our results. Abstract The paper presents the design of a game that will serve as a research environment in the BIRAFFE series experiment planned for autumn 2021, which uses affective and personality computing methods to develop methods for interacting with intelligent assistants. A key aspect is grounding the game design on the taxonomy of player types designed by Bartle. This will allow for an investigation of hypotheses concerning the characteristics of particular types of players or their stability in response to emotionally-charged stimuli occurring during the game.