Opioid Awareness Shaping Lives: One Mind, One Heart, One Pill at a Time

Problem: Across the board recognition to decrease the inappropriate, misuse and abuse of opioids has gained real momentum for the past decade. This multifaceted problem is complex, requiring battles to be waged on all fronts. A critical realm in confronting this issue requires solid and effective education for nursing professionals which can then be imparted to patients and caregivers. The PICO for this capstone is: P: Nursing Staff Registered Nurses, I: Pre-test, Didactic, Simulation, Post-test, 30-day Post-test, C: Pre/Post/Post-test current practice knowledge, additional comparison of Naloxone (Narcan) current practice utilization 30 days Pre and Post Implementation and O: Evaluate increased and retained opioid knowledge, skill competency and Naloxone utilization Purpose: The purpose of this study is to increase nursing staff opioid knowledge, strengthen clinical skill competency, raise multi-modal therapy awareness and reduce Naloxone utilization. Goal: The goal of this study is to initiate and standardize yearly nursing staff education, prioritize non-opioid therapies as first-line treatment, decrease inappropriate opioid use and minimize the need for Naloxone. Objectives: This study includes the following objectives: to increase nursing staff opioid education, clinical skill competency, demonstrate knowledge of non-opioid palliative anesthetic techniques and inpatient Naloxone reduction by 50-100%. Plan: Using a quasi-experimental quantitative design, the study’s succession is as follows: 1.) Pretest, 2.) Didactic education, 3.) Enactment of a progressive group simulation scenario, 4.) Post-test and debriefing and 5.) 30-day Post-test initiated for knowledge retention. Additionally, simulation skills were observed by analyzing domains of Noticing, Interpreting, Responding and Action. Inpatient Naloxone utilization reduction was analyzed 30 days pre and post implementation. Outcomes and Results: The results indicate a statistically significant difference in pre-test to post-test and 30-day post-test scores after a combined didactic and simulation session. Results from the post to 30-day post-test were not found to be statistically significant indicating possible knowledge retention post didactic and simulation intervention. While the Naloxone results were not statistically significant, positive data indicators direct the need for continued evaluation noting utilizing the acute pain service consistently may impact inappropriate opioid administration, reduce length of stay and reduce patient transfers to a higher level of care. The simulation observation domains indicated that block techniques from didactic education resulted in learning. Dual intervention, didactic and simulation, provided an evidence-based method to enhance opioid knowledge. Initiating standardized and frequent opioid education is imperative so that nursing professionals provide excellent patient care and contribute to optimal health outcomes with thoughts every pill given wholeheartedly matters

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors

Cloud eLearning - Personalisation of learning using resources from the Cloud

With the advancement of technologies, the usage of alternative eLearning systems as complementary systems to the traditional education systems is becoming part of the everyday activities. At the same time, the creation of learning resources has increased exponentially over time. However, the usability and reusability of these learning resources in various eLearning systems is difficult when they are unstandardised and semi-standardised learning resources. Furthermore, eLearning activities’ lack of suitable personalisation of the overall learning process fails to optimize resources’ and systems’ potentialities. At the same time, the evolution of learning technologies and cloud computing creates new opportunities for traditional eLearning to evolve and place the learner in the center of educational experiences. This thesis contributes to a holistic approach to the field by using a combination of artificial intelligence techniques to automatically generate a personalized learning path for individual learners using Cloud resources. We proposed an advancement of eLearning, named the Cloud eLearning, which recognizes that resources stored in Cloud eLearning can potentially be used for learning purposes. Further, the personalised content shown to Cloud Learners will be offered through automated personalized learning paths. The main issue was to select the most appropriate learning resources from the Cloud and include them in a personalised learning path. This become even more challenging when these potential learning resources were derived from various sources that might be structured, semi- structure or even unstructured, tending to increase the complexity of overall Cloud eLearning retrieval and matching processes. Therefore, this thesis presents an original concept,the Cloud eLearning, its Cloud eLearning Learning Objects as the smallest standardized learning objects, which permits reusing them because of semantic tagging with metadata. Further, it presents the Cloud eLearning Recommender System, that uses hierarchical clustering to select the most appropriate resources and utilise a vector space model to rank these resources in order of relevance for any individual learner. And it concludes with Cloud eLearning automated planner, which generates a personalised learning path using the output of the CeL recommender system

Interactive Learning of Probabilistic Decision Making by Service Robots with Multiple Skill Domains

This thesis makes a contribution to autonomous service robots, centered around two aspects. The first is modeling decision making in the face of incomplete information on top of diverse basic skills of a service robot. Second, based on such a model, it is investigated, how to transfer complex decision-making knowledge into the system. Interactive learning, naturally from both demonstrations of human teachers and in interaction with objects, yields decision-making models applicable by the robot

Towards a crowdsourced solution for the authoring bottleneck in interactive narratives

Interactive Storytelling research has produced a wealth of technologies that can be employed to create personalised narrative experiences, in which the audience takes a participating rather than observing role. But so far this technology has not led to the production of large scale playable interactive story experiences that realise the ambitions of the field. One main reason for this state of affairs is the difficulty of authoring interactive stories, a task that requires describing a huge amount of story building blocks in a machine friendly fashion. This is not only technically and conceptually more challenging than traditional narrative authoring but also a scalability problem. This thesis examines the authoring bottleneck through a case study and a literature survey and advocates a solution based on crowdsourcing. Prior work has already shown that combining a large number of example stories collected from crowd workers with a system that merges these contributions into a single interactive story can be an effective way to reduce the authorial burden. As a refinement of such an approach, this thesis introduces the novel concept of Crowd Task Adaptation. It argues that in order to maximise the usefulness of the collected stories, a system should dynamically and intelligently analyse the corpus of collected stories and based on this analysis modify the tasks handed out to crowd workers. Two authoring systems, ENIGMA and CROSCAT, which show two radically different approaches of using the Crowd Task Adaptation paradigm have been implemented and are described in this thesis. While ENIGMA adapts tasks through a realtime dialog between crowd workers and the system that is based on what has been learned from previously collected stories, CROSCAT modifies the backstory given to crowd workers in order to optimise the distribution of branching points in the tree structure that combines all collected stories. Two experimental studies of crowdsourced authoring are also presented. They lead to guidelines on how to employ crowdsourced authoring effectively, but more importantly the results of one of the studies demonstrate the effectiveness of the Crowd Task Adaptation approach

Spatial representation for planning and executing robot behaviors in complex environments

Robots are already improving our well-being and productivity in different applications such as industry, health-care and indoor service applications. However, we are still far from developing (and releasing) a fully functional robotic agent that can autonomously survive in tasks that require human-level cognitive capabilities. Robotic systems on the market, in fact, are designed to address specific applications, and can only run pre-defined behaviors to robustly repeat few tasks (e.g., assembling objects parts, vacuum cleaning). They internal representation of the world is usually constrained to the task they are performing, and does not allows for generalization to other scenarios. Unfortunately, such a paradigm only apply to a very limited set of domains, where the environment can be assumed to be static, and its dynamics can be handled before deployment. Additionally, robots configured in this way will eventually fail if their "handcrafted'' representation of the environment does not match the external world. Hence, to enable more sophisticated cognitive skills, we investigate how to design robots to properly represent the environment and behave accordingly. To this end, we formalize a representation of the environment that enhances the robot spatial knowledge to explicitly include a representation of its own actions. Spatial knowledge constitutes the core of the robot understanding of the environment, however it is not sufficient to represent what the robot is capable to do in it. To overcome such a limitation, we formalize SK4R, a spatial knowledge representation for robots which enhances spatial knowledge with a novel and "functional" point of view that explicitly models robot actions. To this end, we exploit the concept of affordances, introduced to express opportunities (actions) that objects offer to an agent. To encode affordances within SK4R, we define the "affordance semantics" of actions that is used to annotate an environment, and to represent to which extent robot actions support goal-oriented behaviors. We demonstrate the benefits of a functional representation of the environment in multiple robotic scenarios that traverse and contribute different research topics relating to: robot knowledge representations, social robotics, multi-robot systems and robot learning and planning. We show how a domain-specific representation, that explicitly encodes affordance semantics, provides the robot with a more concrete understanding of the environment and of the effects that its actions have on it. The goal of our work is to design an agent that will no longer execute an action, because of mere pre-defined routine, rather, it will execute an actions because it "knows'' that the resulting state leads one step closer to success in its task

The Common Link: An Exploration of the Social Cognitive Dimensions of Meaning-Making in Algebra and the Visual Arts Using a Case Study Approach

It is commonplace to hold that algebra and the visual arts are mutually exclusive activities. In this thesis, an attempt was made to connect how we learn in algebra and the visual arts from the social cognitive perspective proposed by Bandura (1986, 1997). That is, the personal, social, and behavioural dimensions of learning in algebra and the visual arts were considered. Also, the issue of a connection between algebra and the visual arts was tackled by taking into account the most recent advances in cognitive science, like the situated movement, the notion, in a nutshell, that cognition is extended throughout our social relations and practices. Making the connection between, what Snow (1959) called generally the two cultures (cited in Stent, 2001, p. 31) of art and science, has precedence. There have been attempts, as interpreted in this thesis, to consider what learning in the arts and sciences have in common from various quarters, be they philosophical, psychological, or historical. Identifying the link between algebra and the visual arts involved several things. First, the historical context for the schism between our understanding of learning in algebra and the visual arts was considered. Second, a detailed review-cum-analysis of the literature was undertaken, and this yielded the themes upon which the connections between algebra and the visual arts were made. Turning to the fieldwork, four probing case studies were utilized to explore how those in algebra or the visual arts learn in their fields. By analyzing the data from the case studies, pattern regularities between learning in algebra and the visual arts were extracted. Finally, the theoretical and pedagogical consequences of having made the common link between algebra and the visual arts were addressed. Theoretically, by considering the role of, for instance, aesthetics and identity as reasons to pursue algebra or the visual arts, Bandura\u27s (1986, 1997) social cognitive theory was corroborated and enlarged. Practically, recommendations were offered for the pedagogy of algebra and the visual arts