Probability learning - The shortest path hypothesis
Probability learning in human decision tasks supports shortest path hypothesis
Huff, E. M.↗
SEARCH · Engineering Papers
Results for “hypothesis learning”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Induction as Knowledge Integration
Two key issues for induction algorithms are the accuracy of the learned hypothesis and the computational resources consumed in inducing that hypothesis. One of the most promising ways to improve performance along both dimensions is to make use of additional knowledge. Multi-strategy learning algorithms tackle this problem by employing several strategies for handling different kinds of knowledge in different ways. However, integrating knowledge into an induction algorithm can be difficult when the new knowledge differs significantly from the knowledge the algorithm already uses. In many cases the algorithm must be rewritten. This paper presents Knowledge Integration framework for Induction (KII), a KII, that provides a uniform mechanism for integrating knowledge into induction. In theory, arbitrary knowledge can be integrated with this mechanism, but in practice the knowledge representation language determines both the knowledge that can be integrated, and the costs of integration and induction. By instantiating KII with various set representations, algorithms can be generated at different trade-off points along these dimensions. One instantiation of KII, called RS-KII, is presented that can implement hybrid induction algorithms, depending on which knowledge it utilizes. RS-KII is demonstrated to implement AQ-11, as well as a hybrid algorithm that utilizes a domain theory and noisy examples. Other algorithms are also possible.
Smith, Benjamin D.↗
Hypothesis behavior in a concept-learning task with probabilistic feedback.
Hypothesis behavior in concept-learning task with probabilistic feedback
PROBABILITY THEORY↗
Efficient Heuristic Hypothesis Ranking
This paper considers the problem of learning the ranking of a set of stochastic alternatives based upon incomplete information (i.e., a limited number of samples).
machine↗
On the Efficient Allocation of Resources for Hypothesis Evaluation in Machine Learning: A Statistical Approach
In this report we consider a decision-making problem of selecting a strategy from a set of alternatives on the basis of incomplete information (e.g., a finite number of observations): the system can, however, gather additional information at some cost.
machine learning↗
Hypothesis behavior in a concept-learning task with probabilistic feedback.
Behavior predictions in concept learning task with probabilistic feedback, discussing error correlation with mathematical models hypothesis
Haygood, R. C.↗
Technology assessment of advanced automation for space missions
Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology.
Source record↗
Robot Behavior Acquisition Superposition and Composting of Behaviors Learned through Teleoperation
Superposition of a small set of behaviors, learned via teleoperation, can lead to robust completion of a simple articulated reach-and-grasp task. Results support the hypothesis that a set of learned behaviors can be combined to generate new behaviors of a similar type. This supports the hypothesis that a robot can learn to interact purposefully with its environment through a developmental acquisition of sensory-motor coordination. Teleoperation bootstraps the process by enabling the robot to observe its own sensory responses to actions that lead to specific outcomes. A reach-and-grasp task, learned by an articulated robot through a small number of teleoperated trials, can be performed autonomously with success in the face of significant variations in the environment and perturbations of the goal. Superpositioning was performed using the Verbs and Adverbs algorithm that was developed originally for the graphical animation of articulated characters. Work was performed on Robonaut at NASA-JSC.
Peters, Richard Alan, II↗
Category Learning Research in the Interactive Online Environment Second Life
The interactive online environment Second Life allows users to create novel three-dimensional stimuli that can be manipulated in a meaningful yet controlled environment. These features suggest Second Life's utility as a powerful tool for investigating how people learn concepts for unfamiliar objects. The first of two studies was designed to establish that cognitive processes elicited in this virtual world are comparable to those tapped in conventional settings by attempting to replicate the established finding that category learning systematically influences perceived similarity . From the perspective of an avatar, participants navigated a course of unfamiliar three-dimensional stimuli and were trained to classify them into two labeled categories based on two visual features. Participants then gave similarity ratings for pairs of stimuli and their responses were compared to those of control participants who did not learn the categories. Results indicated significant compression, whereby objects classified together were judged to be more similar by learning than control participants, thus supporting the validity of using Second Life as a laboratory for studying human cognition. A second study used Second Life to test the novel hypothesis that effects of learning on perceived similarity do not depend on the presence of verbal labels for categories. We presented the same stimuli but participants classified them by selecting between two complex visual patterns designed to be extremely difficult to label. While learning was more challenging in this condition , those who did learn without labels showed a compression effect identical to that found in the first study using verbal labels. Together these studies establish that at least some forms of human learning in Second Life parallel learning in the actual world and thus open the door to future studies that will make greater use of the enriched variety of objects and interactions possible in simulated environments compared to traditional experimental situations.
Andrews, Jan↗
Understanding Oceanic Heavy Precipitation Using Scatterometer, Satellite Precipitation, and Reanalysis Products
The primary aim of this study is to understand the heavy precipitation events over Oceanic regions using vector wind retrievals from space based scatterometers in combination with precipitation products from satellite and model reanalysis products. Heavy precipitation over oceans is a less understood phenomenon and this study tries to fill in the gaps which may lead us to a better understanding of heavy precipitation over oceans. Various phenomenon may lead to intense precipitation viz. MJO (Madden-Julian Oscillation), Extratropical cyclones, MCSs (Mesoscale Convective Systems), that occur inside or outside the tropics and if we can decipher the physical mechanisms behind occurrence of heavy precipitation, then it may lead us to a better understanding of such events which further may help us in building more robust weather and climate models. During a heavy precipitation event, scatterometer wind observations may lead us to understand the governing dynamics behind that event near the surface. We hypothesize that scatterometer winds can observe significant changes in the near-surface circulation and that there are global relationships among these quantities. To the degree to which this hypothesis fails, we will learn about the regional behavior of heavy precipitation-producing systems over the ocean. We use a "precipitation feature" (PF) approach to enable statistical analysis of a large database of raining features.
Winds↗
The real-time learning mechanism of the Scientific Research Associates Advanced Robotic System (SRAARS)
Scientific research associates advanced robotic system (SRAARS) is an intelligent robotic system which has autonomous learning capability in geometric reasoning. The system is equipped with one global intelligence center (GIC) and eight local intelligence centers (LICs). It controls mainly sixteen links with fourteen active joints, which constitute two articulated arms, an extensible lower body, a vision system with two CCD cameras and a mobile base. The on-board knowledge-based system supports the learning controller with model representations of both the robot and the working environment. By consecutive verifying and planning procedures, hypothesis-and-test routines and learning-by-analogy paradigm, the system would autonomously build up its own understanding of the relationship between itself (i.e., the robot) and the focused environment for the purposes of collision avoidance, motion analysis and object manipulation. The intelligence of SRAARS presents a valuable technical advantage to implement robotic systems for space exploration and space station operations.
Chen, Alexander Y.↗
AstroAmpSeq: Microbial Bioinformatics Education with NASA GeneLab’s Amplicon Pipeline
The prevalence and importance of large sequencing datasets in microbiology has led to a movement to share microbial ecology experimental data through open-access databases. This is particularly true of experiments that are difficult to replicate, such as those conducted in the spaceflight environment and shared via NASA GeneLab. It is now possible and indeed valuable for students to access and re-analyze these shared datasets for educational and research purposes. To provide students with experience utilizing microbial bioinformatics tools, GeneLab for Colleges and Universities (GL4U) has designed AstroAmpSeq, a week-long, virtually implemented project-based learning (PBL) minicourse to instruct undergraduate students on 16S amplicon sequencing. AstroAmpSeq was created to be accessible to students without prior bioinformatics or microbial ecology experience. During the minicourse students work in teams to process, analyze, and visualize a subsample of GeneLab dataset GLDS-280 using GeneLab’s standard amplicon processing pipeline, which is based in R. Students develop a hypothesis related to the dataset then generate and analyze figures to evaluate their hypothesis. Formative assessment of student learning is determined via pre- and post-evaluations, peer feedback, and self-reflection. Project and presentation rubrics serve as a summative assessment of student learning. GL4U AstroAmpSeq not only meets American Society for Microbiology Curriculum Guidelines, but also incites student interest in research by an inquiry-based approach and can be made part of a larger semester-long curriculum. GL4U AstroAmpSeq raises awareness of space microbiology and bioinformatics as a field and career path among undergraduates. Further, by using a GeneLab dataset and nesting microbiology techniques into the real-world application of space biology, AstroAmpSeq enforces deeper and longer-lasting student learning.
microbiology↗
The development, assessment and validation of virtual reality for human anatomy instruction
This research project seeks to meet the objective of science training by developing, assessing, validating and utilizing VR as a human anatomy training medium. Current anatomy instruction is primarily in the form of lectures and usage of textbooks. In ideal situations, anatomic models, computer-based instruction, and cadaver dissection are utilized to augment traditional methods of instruction. At many institutions, lack of financial resources limits anatomy instruction to textbooks and lectures. However, human anatomy is three-dimensional, unlike the one-dimensional depiction found in textbooks and the two-dimensional depiction found on the computer. Virtual reality allows one to step through the computer screen into a 3-D artificial world. The primary objective of this project is to produce a virtual reality application of the abdominopelvic region of a human cadaver that can be taken back to the classroom. The hypothesis is that an immersive learning environment affords quicker anatomic recognition and orientation and a greater level of retention in human anatomy instruction. The goal is to augment not replace traditional modes of instruction.
Marshall, Karen Benn↗
Inspiring the Next Generation: The International Space Station Education Accomplishments
The International Space Station (ISS) has a unique ability to capture the imagination of both students and teachers worldwide. Since 2000, the presence of humans onboard ISS has provided a foundation for numerous educational activities aimed at capturing that interest and motivating study in the sciences, technology, engineering and mathematics (STEM). Over 43 million students around the world have participated in ISS-related educational activities. Projects such as YouTube Space Lab, Sally Ride Earth Knowledge-based Acquired by Middle Schools (EarthKAM), SPHERES (Synchronized Position Hold Engage and Reorient Experimental Satellites) Zero-Robotics, Tomatosphere, and MAI-75 events among others have allowed for global student, teacher and public access to space through student classroom investigations and real-time audio and video contacts with crewmembers. Educational activities are not limited to STEM but encompass all aspects of the human condition. This is well illustrated in the Uchu Renshi project, a chain poem initiated by an astronaut while in space and continued and completed by people on Earth. With ISS operations now extended to 2024, projects like these and their accompanying educational materials are available to more students around the world. From very early on in the program's history, students have been provided with a unique opportunity to get involved and participate in science and engineering projects. Many of these projects support inquiry-based learning that allows students to ask questions, develop hypothesis-derived experiments, obtain supporting evidence and identify solutions or explanations. This approach to learning is well-published as one of the most effective ways to inspire students to pursue careers in scientific and technology fields. Ever since the first space station element was launched, a wide range of student experiments and educational activities have been performed, both individually and collaboratively, by all the international partner agencies, National Aeronautics and Space Administration (NASA), Canadian Space Agency (CSA), European Space Agency, (ESA), Japan Aerospace Exploration Agency (JAXA) and Russian Federal Space Agency (Roscosmos), and a number of non-participating countries, some under commercial agreements. Many of these programs still continue, and others are being developed and added to the stations tasks on a regular basis. These diverse student experiments and programs fall into one of the following categories: student-developed experiments; students performing classroom versions of ISS experiments; students participating in ISS investigator experiments; education competitions; students participating in ISS Engineering Education; Education Demonstrations and Cultural Activities. This paper summarizes some of the main student experiments and educational activities that have been conducted on the space station.
Alleyne, Camille W.↗
Sensory Motor Coordination in Robonaut
As a participant of the year 2000 NASA Summer Faculty Fellowship Program, I worked with the engineers of the Dexterous Robotics Laboratory at NASA Johnson Space Center on the Robonaut project. The Robonaut is an articulated torso with two dexterous arms, left and right five-fingered hands, and a head with cameras mounted on an articulated neck. This advanced space robot, now driven only teleoperatively using VR gloves, sensors and helmets, is to be upgraded to a thinking system that can find, interact with and assist humans autonomously, allowing the Crew to work with Robonaut as a (junior) member of their team. Thus, the work performed this summer was toward the goal of enabling Robonaut to operate autonomously as an intelligent assistant to astronauts. Our underlying hypothesis is that a robot can develop intelligence if it learns a set of basic behaviors (i.e., reflexes - actions tightly coupled to sensing) and through experience learns how to sequence these to solve problems or to accomplish higher-level tasks. We describe our approach to the automatic acquisition of basic behaviors as learning sensory-motor coordination (SMC). Although research in the ontogenesis of animals development from the time of conception) supports the approach of learning SMC as the foundation for intelligent, autonomous behavior, we do not know whether it will prove viable for the development of autonomy in robots. The first step in testing the hypothesis is to determine if SMC can be learned by the robot. To do this, we have taken advantage of Robonaut's teleoperated control system. When a person teleoperates Robonaut, the person's own SMC causes the robot to act purposefully. If the sensory signals that the robot detects during teleoperation are recorded over several repetitions of the same task, it should be possible through signal analysis to identify the sensory-motor couplings that accompany purposeful motion. In this report, reasons for suspecting SMC as the basis for intelligent behavior will be reviewed. A robot control system for autonomous behavior that uses learned SMC will be proposed. Techniques for the extraction of salient parameters from sensory and motor data will be discussed. Experiments with Robonaut will be discussed and preliminary data presented.
Peters, Richard Alan, II↗
Evidence for Ancient Life in Mars Meteorites: Lessons Learned
The lines of evidence we first proposed as supporting a hypothesis of early life on Mars are discussed by Treiman, who presents pros and cons of our hypothesis in the light of subsequent research by many groups. Our assessment of the current status of the many controversies over our hypothesis is given in reports by Gibson et al. Rather than repeat or elaborate on that information, I prefer to take an overview and present what I think are some of the "lessons learned" by our team in particular, and by the science community in general.
McKay, D. S.↗
Improving Learning Performance Through Rational Resource Allocation
This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning cost and show that the problem of efficient learning can be cast as a resource optimization problem. Solutions found in this way can be significantly more efficient than the best solutions that do not account for these factors. We introduce a heuristic learning algorithm that approximately solves this optimization problem and document its performance improvements on synthetic and real-world problems.
resource optimization↗
An expert system for monitoring dynamic aspects of RCS
An expert system (ARC) based on the MIMIC(2) framework is being developed for assistance in monitoring the Space Shuttle reaction control system (RCS) and in training RCS mission controllers. ARC has four components: a hypothesis generator, a model-constructor, a model-simulator and a matcher. The hypothesis generator examines current readings of sensors to generate a set of hypotheses. The model constructor generates models from these hypotheses. The simulator predicts future behaviors of the system. The matcher validates hypotheses by testing the predicted states against real data. The hypothesis generator postulates faults using model-based reasoning (MBR), learned rules from real data, and expert-defined rules. The simulator uses a qualitative QSIM model of the RCS which can handle both nominal modes as well as a variety of fault models. This allows the simulator to simulate the system even when one or more components have degraded. Finally, the matcher is responsible for comparing readings against predictions and calculating confidence in a particular hypothesis.
Kulkarni, Deepak↗