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<p> Action selection for an autonomous agent was studied within the confines of truck task scheduling. An experimental setup was established to compare a naive selection approach, a simple market-based optimization approach, and a learning-assisted market-based optimization over a series of scenarios with varying complexity. For sufficiently complex scenarios, the results showed that learning was able to improve the performance of the truck by delaying delivery to a given site until it was the most protable action available. This research adds to the existing autonomous planning research by demonstrating a novel approach for planning under resource constraints. This approach improves upon an existing market-based optimization technique through the use of on-line reinforcement learning for market adjustment.</p>

The release from proactive interference (PI) is a well-studied phenomenon, but its cause is elusive. When a release in PI is caused by changes in the content of to-be-remembered items, the more accurate retrieval is likely a result of changes in context (Watkins & Watkins, 1975). However, changes in context do not readily explain the cause of PI release resulting from a temporal delay. Instead, it could be that during the delay subjects disengage from intrusive information from previous trials. The ability to disengage from no-longer-relevant information is related to fluid intelligence (Gf). I predicted that this ability to disengage, as defined by fluid intelligence, is the driving factor of the time-based release from PI. In order to test this prediction, I administered a free recall task to individuals of high and low Gf. The time between the last two lists was lengthened to cause release. The time manipulation did not cause a release from PI; essentially, this result represents a failure to replicate. Limitations of the study and potential methodological issues are discussed.

The purpose of the study was to determine whether a significant correlation existed between group play and performance on a verbal intelligence test. In addition, the study was designed to test group play as a predictor of performance on a verbal intelligence test.The subjects of the study were kindergarten children enrolled at the Burris Laboratory School in Muncie, Indiana. All the children enrolled in either the morning or afternoon kindergarten program were considered to be included in the population sample but two restrictions eliminated some of the candidates. Those students who were non-white or absent for ten or more observations and/or school days were eliminated. A total of sixteen female and sixteen male students with the age range of sixty-two to seventy-seven months were used in this investigation.Two measuring instruments were used to collect data for the study. An observation instrument was developed from the Parent Social Play Scale to collect data that measured the number of group play episodes observed from each subject.A team of three observers were trained to use the observation instrument and a pilot study was conducted to establish the inter-rater reliability. By using Cohen's coefficient Kappa, it was established that the observation team had an interrater reliability of .99 when measuring between the two-part division of group play and non group play and an inter-rater reliability of .81 when measuring between all six types of social play. Twenty, thirty-second observations were made for each subject. Each observation was classified as one of the six social play types and then further categorized as non group play or group play. The final count included all group play observations for each subject.The other instrument used was the Peabody Picture Vocabulary Test. This instrument provided an estimate of the verbal intelligence of each subject.The group play count, raw score from the Peabody Picture Vocabulary Test, sex, age, and number of siblings in the family of each subject were analyzed to test the following hypothesis at the .05 level of statistical significance: the partial correlation between group play and performance on a verbal intelligence test, partialling out the effects due to sex, age, and number of siblings in the family, is zero.The Statistical Package for the Social Sciences (the SPSS) program for multiple regression was used to test the 3 hypothesis. The value of the partial F ratio from the analysis of the data did not permit -the rejection of the hypothesis at an alpha level of .05. The partial correlation did not approach being significant and the proportion of the residual sum of squares that was accounted for by the addition of group play to the regression could not have been considered different from zero.The following conclusions may be drawn from the study: 1) group play explained very little of the variance in the scores on the verbal intelligence test, and 2) group play had little correlation with performance on the Peabody Picture Vocabulary Test when controlling for number of siblings in the family, age, and sex. Results of the study indicate that use of group play as a predictor or remediator of verbal intelligence is questionable.

The purpose of this study was to determine if the emotional intelligence of gifted adolescents contributes significantly to their social and academic success, and specifically if emotional intelligence was of importance above and beyond traditional psychometric intelligence (IQ). This study tested the claims of Goleman (1995) who argued that emotional intelligence was critical to our understanding of human success, and often times more important than IQ. A group of 39 adolescents (mean age = 16 years 6 months) who were enrolled in a residential high school for gifted youths participated.The Adolescent Multifactor Emotional Intelligence Scale (AMEIS) (Mayer, Salovey, and Caruso, 1996) and the Test of Cognitive Skills (2nd ed.) (CTB MacMillan/McGraw-Hill, 1993) were utilized to attain overall levels of emotional intelligence and IQ, respectively. The Behavior Assessment System for Children - Self-Report - Adolescent Version (BASC-SRP-A) (Reynolds & Kamphaus, 1992) was used to measure two types of social success: interpersonal relations and social stress. Academic success was determined by student grade point averages.The results of hierarchical multiple regression analyses revealed that emotional intelligence did not contribute significantly to the social and academic success for these gifted adolescents. These results suggest that Goleman's argument about the significance of emotional intelligence may be overstated, at least when studying this sample of gifted adolescents. However, future research will need to address the need for improved measurement of emotional intelligence, possibly studying specific components of emotional intelligence. Larger samples that include gifted students from more common settings may also help clarify the importance of emotional intelligence in this population. / Department of Educational Psychology

Sequences of neural activity representing paths in an environment are expressed in the rodent hippocampus at three distinct time scales, with different hypothesized roles in hippocampal function. As an animal moves through an environment and passes through a series of place fields, place cells activate and deactivate in sequence, at the time scale of the animal’s movement (i.e., the behavioral time scale). Moreover, at each moment in time, as the animal’s location in the environment overlaps with the firing fields of many place cells, the active place cells fire in sequence during each cycle of the 4-12 Hz theta oscillation observed in the hippocampal local field potentials (i.e., the theta time scale), such that the neural activity, in general, represents a short path that begins slightly behind the animal and ends slightly ahead of the animal. These sequences have been hypothesized to play a role in the encoding and recall of episodes of behavior. Sequences of neural activity occurring at the third time scale are observed during both sleep and awake but restful states, when animals are paused and generally inattentive, and are associated with sharp wave ripple complexes (SWRs) observed in the hippocampal local field potentials. During the awake state, these sequences have been shown to begin near the animal’s location and extend forward (forward replay) or backward (backward replay), and have been hypothesized to play a role in memory consolidation, path planning, and reinforcement learning. This thesis uses a novel sequence detection method and a novel behavioral spatial decision task to study the functional significance of theta sequences and SWR sequences. The premise of the thesis is that by investigating the behavioral content represented by these sequences, we may further our understanding of how these sequences contribute to hippocampal function. The first part of the thesis presents an analysis of SWR sequences or replays, revealing several novel properties of these sequences. In particular it was found that instead of preferentially representing the more recently experienced parts of the maze, as might be expected for memory consolidation, paths that were not recently experienced were more likely to be replayed. Additionally, paths that were never experienced, including shortcut paths, were observed. These observations suggest that hippocampal replay may play a role in constructing and maintaining a "cognitive map" of the environment. The second part of the thesis investigates the properties of theta sequences. A recent study found that theta sequences extend further forward at choice points on a maze and suggested that these sequences may be partly under cognitive control. In this part of the thesis I present an analysis of theta sequences showing that there is diversity in theta sequences, with some sequences extending more forward and others beginning further backward. Furthermore, certain components of the environment are preferentially represented by theta sequences, suggesting that theta sequences may reflect the cognitive "chunking" of the animal’s environment. The third part of the thesis describes a computational model of the hippocampus which explores how synaptic learning due to neural activity during navigation (i.e., theta sequences) may enable the hippocampal network to produce forward, backward, and shortcut sequences during awake rest states (i.e., SWR sequences).

In this thesis, we study the topic of Lifelong Robotic Object Perception. We propose, as a long-term goal, a framework to recognize known objects and to discover unknown objects in the environment as the robot operates, for as long as the robot operates. We build the foundations for Lifelong Robotic Object Perception by focusing our study on the two critical components of this framework: 1) how to recognize and register known objects for robotic manipulation, and 2) how to automatically discover novel objects in the environment so that we can recognize them in the future. Our work on Object Recognition and Pose Estimation addresses two main challenges in computer vision for robotics: robust performance in complex scenes, and low latency for real-time operation. We present MOPED, a framework for Multiple Object Pose Estimation and Detection that integrates single-image and multi-image object recognition and pose estimation in one optimized, robust, and scalable framework. We extend MOPED to leverage RGBD images using an adaptive image-depth fusion model based on maximum likelihood estimates. We incorporate this model to each stage of MOPED to achieve object recognition robust to imperfect depth data. In Robotic Object Discovery, we address the challenges of scalability and robustness for long-term operation. As a first step towards Lifelong Robotic Object Perception, we aim to automatically process the raw video stream of an entire workday of a robotic agent to discover novel objects. The key to achieve this goal is to incorporate non-visual information| robotic metadata|in the discovery process. We encode the natural constraints and nonvisual sensory information in service robotics to make long-term object discovery feasible. We introduce an optimized implementation, HerbDisc, that processes a video stream of 6 h 20 min of challenging human environments in under 19 min and discovers 206 novel objects. We tailor our solutions to the sensing capabilities and requirements in service robotics, with the goal of enabling our service robot, HERB, to operate autonomously in human environments.

Surgeons increasingly need to perform complex operations on extremely small anatomy. Many existing and promising new surgeries are effective, but difficult or impossible to perform because humans lack the extraordinary control required at sub-millimeter scales. Using micromanipulators, surgeons gain higher positioning accuracy and additional dexterity as the instrument removes tremor and scales hand motions. While these aids are advantageous, they do not actively consider the goals or intentions of the operator and thus cannot provide context-specific behaviors, such as motion scaling around anatomical targets, prevention of unwanted contact with pre-defined tissue areas, compensation for moving anatomy, and other helpful task-dependent actions. This thesis explores the fusion of visual information with micromanipulator control and enforces task-specific behaviors that respond in synergy with the surgeon’s intentions and motions throughout surgical procedures. By exploiting real-time microscope view observations, a-priori knowledge of surgical operations, and pre-operative data prepared before the surgery, we hypothesize that micromanipulators can employ individualized and targeted aids to further help the surgeon. Specifically, we propose a vision-based control framework of virtual fixtures for handheld micromanipulator robots that naturally incorporates tremor suppression and motion scaling. We develop real-time vision systems to track the surgeon and anatomy and design fast, new algorithms for analysis of the retina. Virtual fixtures constructed from visually tracked anatomy allows for complex task-specific behaviors that monitor the surgeon’s actions and react appropriately to cooperatively accomplish the procedure. Particular focus is given to vitreoretinal surgery as a good choice for vision-based control because several new and promising surgical techniques in the eye depend on fine manipulations of tiny and delicate retinal structures. Experiments with Micron, the fully handheld micromanipulator developed in our lab, show that vision-based virtual fixtures significantly increase pointing precision by reducing positioning error during synthetic, but medically relevant hold-still and tracing tasks. To evaluate the proposed framework in realistic environments, we consider three demanding retinal procedures: membrane peeling, laser photocoagulation, and vessel cannulation. Preclinical trials on artificial phantoms, ex vivo, and in vivo animal models demonstrate that vision-based control of a micromanipulator significantly improves surgeon performance (p < 0.05).

Enabling computers to understand human and animal behavior has the potential to revolutionize many areas that benefit society such as clinical diagnosis, human-computer interaction, and social robotics. Critical to the understanding of human and animal behavior, and any temporally-varying phenomenon in general, is the capability to segment, classify, and cluster time series data. This thesis proposes segment-based Support Vector Machines (Seg-SVMs), a framework for supervised, weakly-supervised, and unsupervised time series analysis. Seg-SVMs outperform state-of-the-art approaches by combining three powerful ideas: energy-based structure prediction, bag-of-words representation, and maximum-margin learning. Energy-based structure prediction provides a principled mechanism for concurrent top-down recognition and bottom-up temporal localization. Bag-of-words representation provides segment-based features that tolerate misalignment errors and are computationally efficient. Maximum-margin learning, such as SVM and Structure Output SVM, has a convex learning formulation; it produces classifiers that are discriminative and less prone to over-fitting. In this thesis, we show how Seg-SVMs outperform state-of-the-art approaches for segmenting, classifying, and clustering human and animal behavior in video and accelerometer data of varying complexity. We illustrate these benefits in the problems of facial event detection, sequence labeling of human actions, and temporal clustering of animal behavior. In addition, the Seg-SVMs framework naturally provides solutions to two novel problems: early detection of human actions and weakly-supervised discovery of discriminative events.