Designing a Story Database for Use in Automatic Story Generation

In this paper we propose a model for the representation of stories in a story database. The use of such a database will enable computational story generation systems to learn from previous stories and associated user feedback, in order to create believable stories with dramatic plots that invoke an emotional response from users. Some of the distinguishing characteristics of our proposal are the inclusion of what we call ‘narratological concepts’ and user feedback in the story database

[risk Factors Associated With The Acquisition Of Multiresistant Bacteria In A Pediatric Nursery]

OBJECTIVE: To identify the risk factors in patients who had a multiresistant bacteria during their staying in a Pediatric Intensive Care Unit and in a pediatric nursery of a tertiary teaching hospital.METHODS: Chart review of the patients who stayed in the units from January, 1995 to July, 1997 and had a multiresistant microorganism isolated (both infection and colonization). A case-control study was done using McNemar test for group comparison and using stepwise logistic regression to select independent risk factors. The following risk factors were tested: prior hospital staying, underlying disease, intensive care unit admission, surgical procedure, urinary catheter, central venous line, ventilator, prior antibiotic therapy and skin lesion.RESULTS: Among 52 patients, 66 multiresistant bacteria were identified (among them, 33 were gram-negative bacilli and 33 were methicillin-resistant S. aureus). The logistic regression analysis of the case-control study identified 2 risk factors: prior antibiotic therapy and skin lesion. A single risk factor was indicated for patients with gram-negative bacilli. Nevertheless, for patients with methicillin-resistant S. aureus, central venous lines and skin lesion were significant.CONCLUSION: Prior antibiotic therapy and skin lesion were the factors associated with the acquisition of multiresistant bacteria. Besides skin lesion, for oxacilin-resistant S. aureus colonized patients, central venous catheter use was a risk factor. The strategies employed to limit the spread of those bacteria in the hospital should consider these three factors.76275-8

P300 and uncertainty reduction in a concept identification task.

The relationship between the amplitude of P300, the mean amplitude of the Slow Wave, and uncertainty reduction after (dis)confirmation of hypotheses was studied in a Concept-Identification task. The subjects had to categorize stimuli according to a conceptual rule (joint denial or exclusion) and to rate the confidence that their classification was correct. Three types of feedback were distinguished: confirming (subject's categorization was correct), disconfirming (subject's categorization was incorrect), and non-informative feedback. The EEG was averaged separately according to the three types of feedback and the two confidence ratings (low, high). The data showed the predicted interaction between type of feedback and confidence level. A larger P300 amplitude turned up after confirming feedback when the subject was less confident, than when he was more confident. The reverse was found after disconfirming feedback. The P300 amplitude after non-informative feedback was not influenced by confidence. The mean amplitude of the Slow Wave showed approximately the same interaction pattern. The results were interpreted in terms of changes in the probability of hypotheses which subjects use to categorize stimuli in a Concept-Identification task

Planning Technologies for Interactive Storytelling

Since AI planning was first proposed for the task of narrative generation in interactive storytelling (IS), it has emerged as the dominant approach in this field. This chapter traces the use of planning technologies in this area, considers the core issues involved in the application of planning technologies in IS, and identifies some of the remaining challenges

Towards a resolution of some outstanding issues in transitive research: an empirical test on middle childhood

Transitive Inference (deduce B > D from B > C and C > D) can help us to understand other areas of sociocognitive development. Across three experiments, learning, memory, and the validity of two transitive paradigms were investigated. In Experiment 1 (N = 121), 7-year-olds completed a three-term nontraining task or a five-term task requiring extensive-training. Performance was superior on the three-term task. Experiment 2 presented 5–10-year-olds with a new five-term task, increasing learning opportunities without lengthening training (N = 71). Inferences improved, suggesting children can learn five-term series rapidly. Regarding memory, the minor (CD) premise was the best predictor of BD-inferential performance in both task-types. However, tasks exhibited different profiles according to associations between the major (BC) premise and BD inference, correlations between the premises, and the role of age. Experiment 3 (N = 227) helped rule out the possible objection that the above findings simply stemmed from three-term tasks with real objects being easier to solve than computer-tasks. It also confirmed that, unlike for five-term task (Experiments 1 & 2), inferences on three-term tasks improve with age, whether the age range is wide (Experiment 3) or narrow (Experiment 2). I conclude that the tasks indexed different routes within a dual-process conception of transitive reasoning: The five-term tasks indexes Type 1 (associative) processing, and the three-term task indexes Type 2 (analytic) processing. As well as demonstrating that both tasks are perfectly valid, these findings open up opportunities to use transitive tasks for educability, to investigate the role of transitivity in other domains of reasoning, and potentially to benefit the lived experiences of persons with developmental issues

The contribution of cause-effect link to representing the core of scientific paper—The role of Semantic Link Network

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing