In this paper, we describe an interactive environment for the representation, interpretation, and revision of explanatory biological models. We illustrate our approach on the systems biology of aging, a complex topic that involves many interacting components, and discuss our experiences using this environment to codify an informal model of aging. We close by discussing related efforts and directions for future research.

As biological data about genes and their interactions proliferates, scientists have the opportunity to identify sets of proteins whose interactions make them worthy of further investigation. This paper reports on a knowledge discovery technique to support that work. Foretell is an algorithm originally designed to support search for solutions to constraint satisfaction problems. Recent adaptations enable Foretell to detect sets of genes that interact heavily with one another. We provide empirical results, and describe ongoing work on biological meaning and knowledge infusion from the user.

When constructing visualization pipelines using toolkits, developers must understand what sequencing of operators will transform their data from its raw state to some requested visual representation. In some cases, the requested visual representation must be generated from hybrid pipelines, composed of both toolkit-based and custom operators. Traditionally, developers learn about how to construct these visualization pipelines by word of mouth, by reading documentation and by inspecting code examples, all of which can be costly in terms of time and effort expended. The Visualization Knowledge Project (VisKo) is built on a knowledge base of visualization toolkit operators including rules for how operators are chained together to form pipelines. VisKo helps scientists by automatically generating and suggesting fully functional visualization pipelines, alleviating scientists from having to write any pipeline code. This paper reports on the kinds of knowledge required to support automatic pipeline generation as well our successes when applying VisKo to a number of visualizations scenarios spanning geophysics, environmental and materials science.

Causality discovery has been one of the core tasks in scientific research since the beginning of human scientific history. In the age of data tsunami, the causality discovery task involves identification of causality among millions of variables which cannot be done manually by humans. However, the identification of causality relationships using artificial intelligence and statistical techniques in non-experimental settings faces several challenges. In this work, we address three of the challenges regarding Granger causality, one of the most popular causality inference techniques. First, we analyze the consistency of two most popular Granger causality techniques and show that the significance test is not consistent in high dimensions. Second, we review our nonparametric generalization of the Lasso-Granger technique called Generalized Lasso Granger (GLG) to uncover Granger causality relationships among irregularly sampled time series. Finally, we describe two techniques to uncover the casual dependence in non-linear datasets. Extensive experiments are provided to show the significant advantages of the proposed algorithms over their state-of-the-art counterparts.

Artificial Intelligence researchers have long sought to understand and replicate processes of scientific discovery. This article discusses Discovery Informatics as an emerging area of research that builds on that tradition and applies principles of intelligent computing and information systems to understand, automate, improve, and innovate processes of scientific discovery.

In this article some formal, content-related and procedural considerations towards the sense of humor are articulated and the analysis of both everyday humor behavior and of comic styles leads to the initial proposal of a four factor-model of humor (4FMH). This model is tested in a new dataset and it is also examined whether two forms of comic styles (benevolent humor and moral mockery) do fit in. The model seems to be robust but further studies on the structure of the sense of humor as a personality trait are required.

Based on recent experiences between a laughing virtual agent and a human user at the intersection AI and humor and laughter, this paper aims to highlight some of the psychological considerations, when conducting AI and humor experiments. The systematic and standardized approach outlined in this paper will demonstrate how to reduce error variance that may be caused by confound variables such as having poor experimental controls. From the necessity of cover stories, protocols and procedures, the differences to the pros and cons of measuring subjectively and objectively and what is required so that both give valid and reliable results are offered as solutions to achieving this goal. Furthermore, the psychological individual differences that need consideration, such as the appreciation of different types of humor, mood, personality variables, for example, trait and state cheerfulness, and gelotophobia- the fear of being laughed at are discussed.

Some humorous texts can be detected by stereotyped patterns and terminology. But a humorous story or situation is often an exaggeration of patterns that also occur in serious texts: novelty, unusual plot twists, and situations that disrupt normal social conventions. The same methods for detecting novelty in serious texts can be adapted to detecting novelty in a humorous situation, but with additional tests for features that make it humorous. To interpret and reason about natural language texts, VivoMind Research has developed a cognitive architecture based on societies of heterogeneous intercommunicating agents that use conceptual graphs (CGs) as the knowledge representation. CGs are designed for representing semantics at the level of sentences and paragraphs, but they must be related to larger patterns that span an entire story, article, or book. For detecting and analyzing large-scale patterns, catastrophe theoretical semantics has proved to be surprisingly effective. This article discusses applications to both fictional and nonfictional documents of various kinds, both serious and humorous.

Patients with schizophrenia are characterized by humor recognition deficit which is connected with their cognitive disorder such as inability to filter out irrelevant stimuli. As soon as patients with schizotypal and affective disorders easily recognize humor, this may be used as a strong diagnostic criterion in clinical practice. On the other hand humor recognition by artificial intellect became a hot question in computer science in a flow of attempts to bring human-computer communication closer to social. It is argued that schizophrenic and computer thinking have common features. Both have lack of social and emotional context understanding. To compare failures in humor recognition made by patients with schizophrenia vs computer may move forward theory and practice of both clinical psychology and computer science.

In English, “puns” are usually perceived as a subclass of “jokes”. In Japanese, however, this is not necessarily true. In this paper we investigate whether Japanese native speakers perceive dajare (puns) as jooku (jokes). We first summarize existing research in the field of computational humor, both in English and Japanese, focusing on the usage of these two terms. This shows that in works of Japanese native speakers, puns are not commonly treated as jokes. Next we present some dictionary definitions of dajare and jooku, which show that they may actually be used in a similar manner to English. In order to study this issue, we conducted a survey, in which we asked Japanese participants three questions: whether they like jokes (jooku), whether they like puns (dajare) and whether dajare are jooku. The results showed that there is no common agreement regarding dajare being a genre of jokes. We analyze the outcome of this experiment and discuss them from different points of view.