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This thesis presents optical characterization of the static and dynamic magnetic interactions in ferromagnetic and multiferroic heterostructures with time-resolved and interface-specific optical techniques. The focus of the thesis is on elucidating the underlying physics of key physical parameters and novel approaches, crucial to the performance of magnetic recording and spintronic devices.;First, time-resolved magneto-optical Kerr effect (TRMOKE) is applied to investigate the spin dynamics in L10 ordered FePt thin films, where perpendicular magnetic anisotropy Ku and intrinsic Gilbert damping alpha0 are determined. Furthermore, the quadratic dependence of Ku and alpha0 on spin-orbit coupling strength xi is demonstrated, where xi is continuously controlled through chemical substitution of Pt with Pd element. In addition, a linear correlation between alpha0 and electron scattering rate 1/T e is experimentally observed through modulating the anti-site disorder c in the L10 ordered structure. The results elucidate the basic physics of magnetic anisotropy and Gilbert damping, and facilitate the design and fabrication of new magnetic alloys with large perpendicular magnetic anisotropy and tailored damping properties.;Second, ultrafast excitation of coherent spin precession is demonstrated in Fe/CoO heterostructures and La0.67Ca0.33MnO 3 thin films using TRMOKE technique. In the Fe/CoO thin films, Instant non-thermal ferromagnet (FM) -- antiferromagnet (AFM) exchange torque on Fe magnetization through ultrafast photo-excited charge transfer possesses in the CoO layer is experimentally demonstrated at room temperature. The efficiency of spin precession excitation is significantly higher and the recovery is notably faster than the demagnetization procedure. In the La0.67Ca 0.33MnO3 thin films, pronounced spin precessions are observed in a geometry with negligible canting of the magnetization, indicating that the transient exchange field is generated by the emergent AFM interactions due to charge transfer and modification of the kinetic energy of eg electrons under optical excitation. The results will help promoting the development of novel device concepts for ultrafast spin manipulation.;Last, the interfacial spin state of the multiferroic heterostructure PbZr0.52Ti0.4803/La0.67Sr0.33MnO 3 and its dependence on ferroelectric polarization is investigated with interface specific magnetization induced second harmonic generation (MSHG). The spin alignment of Mn ions in the first unit cell layer at the heterointerface can be tuned from FM to AFM exchange coupled, while the bulk magnetization remains unchanged as probed with MOKE. The discovery provides new insights into the basic physics of interfacial magneto-electric (ME) coupling.

We have designed and implemented a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. In the context of this research, non-heat generating objects are defined as objects that are not a source for their own emission of thermal energy, and so exclude people, animals, vehicles, etc. The resulting classification model complements an autonomous bot's situational awareness by providing the ability to classify smaller structures commonly found in the immediate operational environment. Since GPS depends on the availability of satellites and onboard terrain maps which are often unable to include enough detail for smaller structures found in an operational environment, bots will require the ability to make decisions such as "go through the hedges" or "go around the brick wall." A thermal infrared imaging modality mounted on a small mobile bot is a favorable choice for receiving enough detailed information to automatically interpret objects at close ranges while unobtrusively traveling alongside pedestrians. The classification of indoor objects and heat generating objects in thermal scenes is a solved problem. A missing and essential piece in the literature has been research involving the automatic characterization of non-heat generating objects in outdoor environments using a thermal infrared imaging modality for mobile bots. Seeking to classify non-heat generating objects in outdoor environments using a thermal infrared imaging system is a complex problem due to the variation of radiance emitted from the objects as a result of the diurnal cycle of solar energy. The model that we present will allow bots to "see beyond vision" to autonomously assess the physical nature of the surrounding structures for making decisions without the need for an interpretation by humans.;Our approach is an application of Bayesian statistical pattern classification where learning involves labeled classes of data (supervised classification), assumes no formal structure regarding the density of the data in the classes (nonparametric density estimation), and makes direct use of prior knowledge regarding an object class's existence in a bot's immediate area of operation when making decisions regarding class assignments for unknown objects. We have used a mobile bot to systematically capture thermal infrared imagery for two categories of non-heat generating objects (extended and compact) in several different geographic locations. The extended objects consist of objects that extend beyond the thermal camera's field of view, such as brick walls, hedges, picket fences, and wood walls. The compact objects consist of objects that are within the thermal camera's field of view, such as steel poles and trees. We used these large representative data sets to explore the behavior of thermal-physical features generated from the signals emitted by the classes of objects and design our Adaptive Bayesian Classification Model. We demonstrate that our novel classification model not only displays exceptional performance in characterizing non-heat generating outdoor objects in thermal scenes but it also outperforms the traditional KNN and Parzen classifiers.

ADVISOR is a machine learning architecture for constructing intelligent tutoring systems (ITS). ADVISOR is able to automate some of the reasoning about how the student will probably perform, and all of the reasoning about which teaching action should be made in a particular context. The benefit of this approach is that it works by observing students using an ITS. By observing students, ADVISOR constructs a model of how a student will respond to a particular teaching action in a given situation. With this model, ADVISOR is able to experiment and determine a policy for presenting teaching actions that tries to achieve a customizable teaching goal. We experimented with a variety of approaches for constructing a model of how students behave, and we found that sophisticated approaches such as Multiple Adaptive Regression Splines (MARS) are only slightly better than linear regression. We also examined a variety of ways ADVISOR can reason with the model of student performance and determine how to teach. We used including temporal difference learning, heuristic search, and the use of rollouts. If little is known a prior about the teaching goal, rollouts are a strong choice as they require little prior knowledge and are robust. Given prior knowledge of the teaching goal, some type of temporal difference learning is a good option since this requires less computation time than using heuristic search or rollouts. ADVISOR was tested in the context of the AnimalWatch tutor for grade school arithmetic. However, the architecture is generic and applicable to a variety of ITS. As part of AnimalWatch, ADVISOR was tested in a grade school and achieved the specified teaching goal of minimizing the amount of time per problem. The ADVISOR architecture is also useful for evaluating what components of the tutoring system are responsible for performance, and what components of ADVISOR are constraining performance. In this way, engineering effort can be directed to where it is most profitable. Thus, the ADVISOR architecture has the potential to benefit a wide range of ITS (and possibly other adaptive systems) in several ways. In addition to determining which components limit performance, our hope is ADVISOR's ability to automate the construction of the knowledge of how to teach will result in a decreased cost to construct ITS.

While formal, decision-theoretic models such as the Markov Decision Process (MDP) have greatly advanced the field of single-agent control, application of similar ideas to multi-agent domains has proven problematic. The advantages of such an approach over traditional heuristic and experimental models of multi-agent systems include a more accurate representation of the underlying problem, a more easily defined notion of optimality and the potential for significantly better solutions. The difficulty often comes from the tradeoff between the expressiveness of the model and the complexity of finding an optimal solution. Much of the research in this area has focused on the extremes of this tradeoff. At one extreme are models where each agent has a global view of the world, and solving these problems is no harder than solving single-agent problems. At the other extreme lie very general, decentralized models, which are also nearly impossible to solve optimally. The work proposed here explores the middle-ground by starting with a general decentralized Markov decision process and introducing structure that can be exploited to reduce the complexity. I present two decision-theoretic models that structure the interactions between agents in two different ways. In the first model the agents are independent except for an extra reward signal that depends on each of the agents' histories. In the second model the agents have independent rewards but there is a structured interaction between their transition probabilities. Both of these models can be optimally and approximately solved using my Coverage Set Algorithm. I also extend the first model by allowing the agents to communicate and I introduce an algorithm that finds an optimal joint communication policy for a fixed joint domain-level policy.

Although an automated reader for the blind first appeared nearly two-hundred years ago, computers can currently "read" document text about as well as a seven-year-old. Scene text recognition brings many new challenges. A central limitation of current approaches is a feed-forward, bottom-up, pipelined architecture that isolates the many tasks and information involved in reading. The result is a system that commits errors from which it cannot recover and has components that lack access to relevant information. We propose a system for scene text reading that in its design, training, and operation is more integrated. First, we present a simple contextual model for text detection that is ignorant of any recognition. Through the use of special features and data context, this model performs well on the detection task, but limitations remain due to the lack of interpretation. We then introduce a recognition model that integrates several information sources, including font consistency and a lexicon, and compare it to approaches using pipelined architectures with similar information. Next we examine a more unified detection and recognition framework where features are selected based on the joint task of detection and recognition, rather than each task individually. This approach yields better results with fewer features. Finally, we demonstrate a model that incorporates segmentation and recognition at both the character and word levels. Text with difficult layouts and low resolution are more accurately recognized by this integrated approach. By more tightly coupling several aspects of detection and recognition, we hope to establish a new unified way of approaching the problem that will lead to improved performance. We would like computers to become accomplished grammar-school level readers.

The broad problem I address in this dissertation is design of autonomous agents that can efficiently learn how to achieve desired behaviors in large, complex environments. I focus on one essential design component: the ability to form new behavioral units, or skills, from existing ones. I propose a characterization of a useful class of skills in terms of general properties of an agent's interaction with its environment—in contrast to specific properties of a particular environment—and I introduce methods that can be used to identify and acquire such skills autonomously.

The decentralized Markov decision process (Dec-POMDP) is a powerful formal model for studying multiagent problems where cooperative, coordinated action is optimal, but each agent acts based on local data alone. Unfortunately, it is known that Dec-POMDPs are fundamentally intractable: they are NEXP-complete in the worst case, and have been empirically observed to be beyond feasible optimal solution. To get around these obstacles, researchers have focused on special classes of the general Dec-POMDP problem, restricting the degree to which agent actions can interact with one another. In some cases, it has been proven that these sorts of structured forms of interaction can in fact reduce worst-case complexity. Where formal proofs have been lacking, empirical observations suggest that this may also be true for other cases, although less is known precisely. This thesis unifies a range of this existing work, extending analysis to establish novel complexity results for some popular restricted-interaction models. We also establish some new results concerning cases for which reduced complexity has been proven, showing correspondences between basic structural features and the potential for dimensionality reduction when employing mathematical programming techniques. As our new complexity results establish that worst-case intractability is more widespread than previously known, we look to new ways of analyzing the potential average-case difficulty of Dec-POMDP instances. As this would be extremely difficult using the tools of traditional complexity theory, we take a more empirical approach. In so doing, we identify new analytical measures that apply to all Dec-POMDPs, whatever their structure. These measures allow us to identify problems that are potentially easier to solve on average, and validate this claim empirically. As we show, the performance of well-known optimal dynamic programming methods correlates with our new measure of difficulty. Finally, we explore the approximate case, showing that our measure works well as a predictor of difficulty there, too, and provides a means of setting algorithm parameters to achieve far more efficient performance.

The resource-constrained scheduling problem (RCSP) involves the assignment of a limited set of resources to a collection of tasks, with the intent of satisfying some particular qualitative objective, under a variety of technological and temporal constraints. Real-world environments, however, introduce a variety of complications to the standard RCSP. The dynamic resource-constrained scheduling problem describes a class of real-world RCSPs that exist within the context of dynamic and unpredictable environments, where the details of the problem are often incomplete, and subject to change over time, without notice. Previous approaches to solving resource-constrained scheduling problems failed to focus on the dynamic nature of real-world environments. The scheduling process occurs away from the environment in which the resulting schedule is executed. Complete prior knowledge of the order set is assumed, and reaction to changes in the environment, if at all, is limited. We have developed a generic, multi-faceted, knowledge-based approach to solving dynamic resource-constrained scheduling problems, which focuses on issues of flexibility during the solution process to enable effective reaction to dynamic environments. Our approach is characterized by a highly opportunistic control scheme that provides the ability to adapt quickly to changes in the environment, a least-commitment scheduling procedure that preserves maneuverability by explicitly incorporating slack time into the developing schedule, and the systematic consultation of a range of relevant scheduling perspectives at key decision-making points that provides an informed view of the current state of problem-solving at all times. The Dynamic Scheduling System (DSS) is a working implementation of our scheduling approach, capable of representing a wide range of dynamic RCSPs, and producing quality schedules under a variety of real-world conditions. It handles a number of additional domain complexities, such as inter-order tasks and mobile resources with significant travel requirements. We discuss our scheduling approach and its application to two different RCSP domains, and evaluate its effectiveness in each, using special application systems built with DSS.