2Planning for Contingencies: A Decision-based Approach

A fundamental assumption made by classical AI planners is that there is no uncertainty in the world: the planner has full knowledge of the conditions under which the plan will be executed and the outcome of every action is fully predictable. These planners cannot therefore construct contingency plans, i.e., plans in which different actions are performed in different circumstances. In this paper we discuss some issues that arise in the representation and construction of contingency plans and describe Cassandra, a partial-order contingency planner. Cassandra uses explicit decision-steps that enable the agent executing the plan to decide which plan branch to follow. The decision-steps in a plan result in subgoals to acquire knowledge, which are planned for in the same way as any other subgoals. Cassandra thus distinguishes the process of gathering information from the process of making decisions. The explicit representation of decisions in Cassandra allows a coherent approach to the problems of contingent planning, and provides a solid base for extensions such as the use of different decision-making procedures.Comment: See http://www.jair.org/ for any accompanying file

How experienced nurses gather and use data.

This exploratory study was designed to add to the body of knowledge related to clinical decision-making. It had two purposes. The first was to develop, clarify, and elaborate concepts that describe nurses\u27 clinical decision-making. The second was to observe and describe activities for gathering information used by nurses in the clinical environment. Six experienced nurses were observed while they interacted with patients at the beginning of their shift. Subjects were asked during post-observation interviews to describe what they were thinking about when they asked patients questions. A five-stage model that described the decision-making process evolved from the analysis of data. Experts in decision-making were asked to provide reactions to the findings with respect to its clarity, validity and usefulness. Results of the study indicated that subjects used three modes--scanning mode, focusing mode, and a context building mode--when gathering information at the beginning of their shift in order to plan patient care. Experienced nurses used three activities for gathering information to make clinical decisions--listening or reading report, reading records, and interacting with patients. Subjects described using information from report together with their knowledge of patients\u27 conditions to decide what information they needed from other sources to make decisions about patients\u27 needs. Findings suggested that subjects made decisions related to what information to gather, what information to accept as sufficient to form hypotheses or conclusions, what information area to drop, and what action to take. Subjects\u27 verbalized that knowledge of patients\u27 conditions and patients\u27 responses determined if they used a scanning mode or a focusing mode to gather information

Towards an Understanding of Business Intelligence

Given the wide recognition of business intelligence (BI) over the last 20 years, we performed a literature review on the concept from a managerial perspective. We analysed 103 articles related to BI in the period 1990 to 2010. We found that BI is defined as a process, a product, and as a set of technologies, or a combination of these, which involves data, information, knowledge, decision making, related processes and technologies that support them. Our findings show that the literature focuses mostly on data and information, and less on knowledge and decision making. Moreover, in relation to the processes there is a substantial amount of literature about gathering and storing data and information, but less about analysing and using information and knowledge, and almost nothing about acting (making decisions) based on intelligence. The research literature has mainly focused on technologies and neglecting the role of the decision maker. We conclude by synthesizing a unified definition of BI and identifying possible future research streams

Using K.net: A Knowledge Network Product

As we move into the Knowledge Age, enterprises are becoming cognizant of the importance of their intellectual assets as a source of competitive advantage. These intellectual assets include the knowledge, experience, and insights of the enterprises’ employees, customers, suppliers, and consultants. To capture these intellectual assets, enterprises are using computer and information technologies called knowledge networks. Knowledge networks facilitate setting up systems and processes to collect knowledge from individuals and share that knowledge with others in the enterprise. K.net (which stands for Knowledge Networks), a communications software product, will be demonstrated for gathering knowledge useful in supporting a decision making process in business

Medication decision-making for patients with renal insufficiency in inpatient and outpatient care at a US Veterans Affairs Medical Centre: a qualitative, cognitive task analysis.

BackgroundMany studies identify factors that contribute to renal prescribing errors, but few examine how healthcare professionals (HCPs) detect and recover from an error or potential patient safety concern. Knowledge of this information could inform advanced error detection systems and decision support tools that help prevent prescribing errors.ObjectiveTo examine the cognitive strategies that HCPs used to recognise and manage medication-related problems for patients with renal insufficiency.DesignHCPs submitted documentation about medication-related incidents. We then conducted cognitive task analysis interviews. Qualitative data were analysed inductively.SettingInpatient and outpatient facilities at a major US Veterans Affairs Medical Centre.ParticipantsPhysicians, nurses and pharmacists who took action to prevent or resolve a renal-drug problem in patients with renal insufficiency.OutcomesEmergent themes from interviews, as related to recognition of renal-drug problems and decision-making processes.ResultsWe interviewed 20 HCPs. Results yielded a descriptive model of the decision-making process, comprised of three main stages: detect, gather information and act. These stages often followed a cyclical path due largely to the gradual decline of patients' renal function. Most HCPs relied on being vigilant to detect patients' renal-drug problems rather than relying on systems to detect unanticipated cues. At each stage, HCPs relied on different cognitive cues depending on medication type: for renally eliminated medications, HCPs focused on gathering renal dosing guidelines, while for nephrotoxic medications, HCPs investigated the need for particular medication therapy, and if warranted, safer alternatives.ConclusionsOur model is useful for trainees so they can gain familiarity with managing renal-drug problems. Based on findings, improvements are warranted for three aspects of healthcare systems: (1) supporting the cyclical nature of renal-drug problem management via longitudinal tracking mechanisms, (2) providing tools to alleviate HCPs' heavy reliance on vigilance and (3) supporting HCPs' different decision-making needs for renally eliminated versus nephrotoxic medications

AN EMPIRICAL ANALYSIS OF CUSTOMER SATISFACTION WITH MOBILE NETWORK SERVICE FOR COMPETITIVE BUSINESS ADVANTAGE

Positioning for effective competitive advantage requires taking actions informed by the result of business data analysis. Business Intelligence provides the platform with which Information and knowledge is used to improve business operation. This exploratory study examines customer satisfaction as the basis of competitive advantage enjoyed by information network service providers and those responsible for decision making in related organizations. The aim is to statistically analyse data and complement this with Text mining, in order to have an holistic way of drawing inferences from both structured and unstructured data for the purpose of decision making. Two method of analysis were used to find the level of customer satisfaction. They include, descriptive method, which involves the use of SPSS15.0 and K-means clustering algorithm used to mine the unstructured part of the data gathering instrument (questionnaire). Questionnaire is used as the primary means of data gathering. The finding of this study reveal that competitive intelligence related inferences bring about a better customer service relationship between the network service providers and their customers and therefore improve the profits of the organizations involved

Introduction: The Leading Indicators Project

Policy makers of all ages have sought to ground their decisions in sound knowledge. As early as 1790, President George Washington told Congress that “Knowledge is in every country the surest basis of public happiness. In one in which the measures of government receive their impressions so immediately from the sense of the community as in ours it is proportionably essential.” In our time, generating and disseminating reliable information has become a passion. This modern attitude is captured in buzzwords like “knowledge-based interference,” “data-informed decision making,” “information-driven needs assessment,” and it finds a powerful expression in the Leading Indicators (LI) movement that has been gathering momentum for several decades

Decision Making Under Uncertainty: A Neural Model Based on Partially Observable Markov Decision Processes

A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic representations are used to select actions has remained unclear. Here we propose a neural model of action selection and decision making based on the theory of partially observable Markov decision processes (POMDPs). Actions are selected based not on a single “optimal” estimate of state but on the posterior distribution over states (the “belief” state). We show how such a model provides a unified framework for explaining experimental results in decision making that involve both information gathering and overt actions. The model utilizes temporal difference (TD) learning for maximizing expected reward. The resulting neural architecture posits an active role for the neocortex in belief computation while ascribing a role to the basal ganglia in belief representation, value computation, and action selection. When applied to the random dots motion discrimination task, model neurons representing belief exhibit responses similar to those of LIP neurons in primate neocortex. The appropriate threshold for switching from information gathering to overt actions emerges naturally during reward maximization. Additionally, the time course of reward prediction error in the model shares similarities with dopaminergic responses in the basal ganglia during the random dots task. For tasks with a deadline, the model learns a decision making strategy that changes with elapsed time, predicting a collapsing decision threshold consistent with some experimental studies. The model provides a new framework for understanding neural decision making and suggests an important role for interactions between the neocortex and the basal ganglia in learning the mapping between probabilistic sensory representations and actions that maximize rewards

The Potential Use of Biogs in Nursing Education

Web logs, also known as “blogs,” are an emerging writing tool that are easy to use, are Internet-based, and can enhance health professionals\u27 writing, communication, collaboration, reading, and information-gathering skills. Students from different disciplines, such as medicine, public health, business, library science, and journalism, garner knowledge from blogs as innovative educational tools. Healthcare professionals are expected to be competent in the use of information technology to be able to effectively communicate, manage information, diminish medical error, and support decision making. However, the use of blogs, as an interactive and effective educational method, has not been well documented by nurse educators