This paper presents an interactive narrative framework we have designed for games that promote health behavior change. The framework aims to address two key issues: player engagement with the game, and player adherence to the health behavior change-related homework they receive in the game. In this paper, we describe our narrative system that tackles these issues and a prototype game that promotes physical activity in which our narrative system is integrated.

The increased use of large corpora in narrative research has created new opportunities for empirical research and intelligent narrative technologies. To best exploit the value of these corpora, several research groups are eschewing complex discourse analysis techniques in favor of high-level minimalist narrative annotation schemes that can be quickly applied, achieve high inter-rater agreement, and are amenable to automation using machine-learning techniques. In this paper we compare different annotation schemes that have been employed by two groups of researchers to annotate large corpora of narrative text. Using a dual-annotation methodology, we investigate the correlation between narrative clauses distinguished by their structural role (orientation, action, evaluation), their subjectivity, and their narrative level within the discourse. We find that each simple narrative annotation scheme captures a structurally distinct characteristic of real-world narratives, and each combination of labels is evident in a corpus of 19 weblog narratives (951 narrative clauses). We discuss several potential applications of minimalist narrative annotation schemes, noting the combination of label across these two annotation schemes that best support each task.

As a story unfolds, it constructs a depiction of events, and at the same time, it also builds conceptual structure at a higher, interpretive level. This higher-level structure provides the terms for understanding the unfolding story, indicating what kinds of features and consequences characterize it – a story ontology . The process by which a tale constructs a story ontology is not straightforward, and in many ways is just as complex as the action at the event level. It involves an interaction between inferred situations and contexts, each with their own networks of terms and structures, which jostle for dominance. I refer to this interaction as governance . In this work, I demonstrate an example of governance at both levels, using a scene from the series Game of Thrones . When the interpretive terms of a story emerge, an understanding of what kinds of events might come next – the possible causal implications – are also conveyed, even if they are unexpected.

In this paper we address the task of extracting risk events and probabilities from free text, focusing in particular on the biomedical domain. While our initial motivation is to enable the determination of the parameters of a Bayesian belief network, our approach is not specific to that use case. We are the first to investigate this task as a sequence tagging problem where we label spans of text as events A or B that are then used to construct probability statements of the form P(A|B)=x. We show that our approach significantly outperforms an entity extraction baseline on a new annotated medical risk event corpus. We also explore semi-supervised methods that lead to modest improvement, encouraging further work in this direction.

Wikipedia provides an enormous amount of background knowledge to reason about the semantic relatedness between two entities. We propose Wikipedia-based Distributional Semantics for Entity Relatedness (DiSER), which represents the semantics of an entity by its distribution in the high dimensional concept space derived from Wikipedia. DiSER measures the semantic relatedness between two entities by quantifying the distance between the corresponding high-dimensional vectors. DiSER builds the model by taking the annotated entities only, therefore it improves over existing approaches, which do not distinguish between an entity and its surface form. We evaluate the approach on a benchmark that contains the relative entity relatedness scores for 420 entity pairs. Our approach improves the accuracy by 12% on state of the art methods for computing entity relatedness. We also show an evaluation of DiSER in the Entity Disambiguation task on a dataset of 50 sentences with highly ambiguous entity mentions. It shows an improvement of 10% in precision over the best performing methods. In order to provide the resource that can be used to find out all the related entities for a given entity, a graph is constructed, where the nodes represent Wikipedia entities and the relatedness scores are reflected by the edges. Wikipedia contains more than 4.1 millions entities, which required efficient computation of the relatedness scores between the corresponding 17 trillions of entity-pairs.

Hershman and Lieb, 1988) However, Ward et al. (Ward et al. 1999) have convincingly argued an alternative While it is relatively easy to recognize a creative deed, it is view that "[…] creative capacity is an essential property of extremely difficult (as demonstrated by creativity research normative human cognition and […] the relevant processes so far) to define what creativity is. The past (almost 70) are open to investigation". In support of this view, I would years of research definitely shed some light on different like to mention the research of Picciuto and Carruthers aspects of creativity, but we are still far from a commonly (Picciuto and Carruthers, 2012) that put forward the agreed upon definition of it and consequently a deep hypothesis that pretense play might be the key factor in understanding of this phenomenon. For an extended understanding creativity. Pretense play occurs typically in historical overview of creativity research, please refer to children at about the age of 18 months and is universal (Stojanov, 2013). Here are four branches which can be across all human cultures.

The need for health providers to share information is a pressing need in our ever more connected world. A patient's health information should seamlessly flow from labs to hospitals to primary care offices. To address this need, in this paper we present the Health E-Match, which focuses on the matching health terms in support of semantic interoperability. Health E-Match determines the semantic similarity between data items, realizing, for instance, that "BHGC (UR)" and "BETA-HCG (QUAL)" both refer to the same pregnancy test, known as "Beta human chorionic gonadotropin, urine qualitative." Our approach is grounded in probabilistic machine learning, and leverages several sophisticated methods for comparing the similarity between medical data items beyond simple edit distance. We present two large scale, real-world experiments to verify that our approach is both accurate and has the ability to eventually be "universal" in that models trained on one set of data translate to strong performance on data from a completely different provider.

In this paper, we highlight the usage of AI in software development process for Robotic systems, in general and HRI systems, in particular. The software as well as the software development methodology and associated tools are knowledge-based systems. The key challenge is to represent domain knowledge that enables the process and model evolution to built complex software intensive HRI systems.

Motivated by generating personalized recommendations using ordinal (or preference) data, we study the question of learning a mixture of MultiNomial Logit (MNL) model, a parameterized class of distributions over permutations, from partial ordinal or preference data (e.g. pair-wise comparisons). Despite its long standing importance across disciplines including social choice, operations research and revenue management, little is known about this question. In case of single MNL models (no mixture), computationally and statistically tractable learning from pair-wise comparisons is feasible. However, even learning mixture with two MNL components is infeasible in general. Given this state of affairs, we seek conditions under which it is feasible to learn the mixture model in both computationally and statistically efficient manner. We present a sufficient condition as well as an efficient algorithm for learning mixed MNL models from partial preferences/comparisons data. In particular, a mixture of $r$ MNL components over $n$ objects can be learnt using samples whose size scales polynomially in $n$ and $r$ (concretely, $r^{3.5}n^3(log n)^4$, with $r\ll n^{2/7}$ when the model parameters are sufficiently incoherent). The algorithm has two phases: first, learn the pair-wise marginals for each component using tensor decomposition; second, learn the model parameters for each component using Rank Centrality introduced by Negahban et al. In the process of proving these results, we obtain a generalization of existing analysis for tensor decomposition to a more realistic regime where only partial information about each sample is available.

Psychologists and social scientists have been researching creativity in humans for several years, and it has gained the attention of artificial intelligence and robotics researchers as well. In this abstract, we discuss the emotional and conversational interface required for a humanoid robot to socially interact with children in order to learn new creative concepts. We briefly describe the approach we are taking to develop such a humanoid robot that can collaborate with children to discover creative concepts.