SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequences to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.Riverside Reserach Institute; Defense Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI 90-24877, SBE-0345378); Office of Naval Research (N00014-92-J-1309, N00014-91-J-4100, N00014-01-1-0624, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-01-1-0397); National Science Foundation (IRI 90-24877, SBE-0354378); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-95-1-0657, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2

Level of protein supplementation for beef calves and yearlings on winter bluestem pasture

Digitized by Kansas State University Librarie

Understanding adult student stop-out: Perspectives of mid-career online graduate students

Doctor of PhilosophyDepartment of Special Education, Counseling and Student AffairsDoris W CarrollThe purpose of this qualitative case study is to understand the experiences of adult graduate students who stop-out, or take a break from enrollment, but who ultimately persist by reenrolling. The participants in this study were enrolled in an online master’s degree program. Symbolic interactionism was the theoretical framework for this study using criterion sampling. While findings revealed the individualistic nature of the stop-out experience, there were commonalities among participants. They valued flexibility, convenience of process, and receiving timely information. Graduate programs are encouraged to identify central support personnel, implement reenrollment plans, and acknowledge the silent stop-out

Mind the Gap: Student Researchers Use Secondary Data to Explore Disparities in STEM Education

Large data sets offer opportunities for graduate students to become involved in meaningful research, but also comes with a unique set of challenges. This paper seeks to examine that relationship through utilizing the High School Longitudinal Study 2009 – representative of US ninth graders in 2009 (n = 21,444) – to examine a set of research questions about STEM interest and preparation amongst secondary students. Student researchers identified gaps in plans and outcomes with regards to race, gender, exceptionalities, and socioeconomic status. Findings indicated inequities that affect STEM outcomes. A significant interaction was found between students education expectations by gender on science self-efficacy [F(4,1264) = 2.797, p =.025]. This interaction was not observed for math self-efficacy. Females and underrepresented minorities were less likely to pursue computer science courses and computer science careers [Females: Χ2 (2, N = 20,594) = 111.500, p \u3c .0001; Minorities: Χ2 (2, N = 13,069) = 6.455, p = .040]. Students’ expectations for post-secondary education differed by IEP status and socioeconomic status [Χ2 (3, n =165,684) = 26.886, p = 0.001]. Finally, time spent in extracurricular activities impacted academic achievement and students in lower socioeconomic groups were less involved in extracurricular activities [Χ2 (4, n = 20,598) = 132.298, p \u3c .0001]

Signal Enhancement for Magnetic Navigation Challenge Problem

Harnessing the magnetic field of the earth for navigation has shown promise as a viable alternative to other navigation systems. A magnetic navigation system collects its own magnetic field data using a magnetometer and uses magnetic anomaly maps to determine the current location. The greatest challenge with magnetic navigation arises when the magnetic field data from the magnetometer on the navigation system encompass the magnetic field from not just the earth, but also from the vehicle on which it is mounted. It is difficult to separate the earth magnetic anomaly field magnitude, which is crucial for navigation, from the total magnetic field magnitude reading from the sensor. The purpose of this challenge problem is to decouple the earth and aircraft magnetic signals in order to derive a clean signal from which to perform magnetic navigation. Baseline testing on the dataset shows that the earth magnetic field can be extracted from the total magnetic field using machine learning (ML). The challenge is to remove the aircraft magnetic field from the total magnetic field using a trained neural network. These challenges offer an opportunity to construct an effective neural network for removing the aircraft magnetic field from the dataset, using an ML algorithm integrated with physics of magnetic navigation.Comment: 21 pages, 4 figures. See https://github.com/MIT-AI-Accelerator/MagNav.jl for accompanying data and cod