Planning And Scheduling For Large-scaledistributed Systems

Many applications require computing resources well beyond those available on any single system. Simulations of atomic and subatomic systems with application to material science, computations related to study of natural sciences, and computer-aided design are examples of applications that can benefit from the resource-rich environment provided by a large collection of autonomous systems interconnected by high-speed networks. To transform such a collection of systems into a user\u27s virtual machine, we have to develop new algorithms for coordination, planning, scheduling, resource discovery, and other functions that can be automated. Then we can develop societal services based upon these algorithms, which hide the complexity of the computing system for users. In this dissertation, we address the problem of planning and scheduling for large-scale distributed systems. We discuss a model of the system, analyze the need for planning, scheduling, and plan switching to cope with a dynamically changing environment, present algorithms for the three functions, report the simulation results to study the performance of the algorithms, and introduce an architecture for an intelligent large-scale distributed system

Machine learning techniques for efficient query processing in kowledge base systems

In this dissertation we propose a new technique for efficient query processing in knowledge base systems. Query processing in knowledge base systems poses strong computational challenges because of the presence of combinatorial explosion. This arises because at any point during query processing there may be too many subqueries available for further exploration. Overcoming this difficulty requires effective mechanisms for choosing from among these subqueries good subqueries for further processing. Inspired by existing works on stochastic logic programs, compositional modeling and probabilistic heuristic estimates we create a new, nondeterministic method to accomplish the task of subquery selection for query processing. Specifically, we use probabilistic heuristic estimates to make the necessary decisions. This approach combines subquery and knowledge base properties and previous query processing experience with conditional probability theory to derive a probability of success for each subquery. The probabilities of success are used to select the next subquery for further processing. The underlying, property-specific probabilities of success are learned via a machine learning process involving a set of training sample queries. In this dissertation we present our new methodology and the algorithms used to accomplish both the training and query processing phases of the system. We also present a method for determining the minimum training set size needed to achieve probability estimates with any desired limit on the maximum size of the errors

Question Driven Introductory Programming Instruction: A Pilot Study

For most beginners, learning computer programming is a complex undertaking. Demotivation and learned helplessness have been widely reported. In addition to the subject’s complexity, low in-class involvement has been linked to poor student performance. This work introduces a novel instructional technique called Student-Driven Probe Instruction (SDPI) to address the low levels of in-class involvement in basic programming courses. The concept was straightforward: rather than the teacher lecturing/explaining material to the class and requesting questions, the students were shown a piece of code or other relevant material and given the opportunity to ask questions first. Explanations followed only after the questions had been asked, not before. Participation was tracked through two metrics: the number of questions asked in class and emails/Slack contacts with the instructor. Significant improvements were recorded for in-class participation. Average quiz scores also improved meaningfully. According to a course evaluation survey, students favored SDPI over the conventional lecture format since it piqued their interest in the material and gave them the confidence to ask questions in class

Sample-efficient Learning and Generalization with Text Representations

Humans have a remarkable ability to learn without much supervision. Often, a few labelled instances or a single demonstration is enough for us to learn a new concept. Most of our knowledge is acquired in a weakly unsupervised manner, via reading, perception, and active interaction with the world. Machine learning models, on the other hand, struggle to learn from limited supervision and often need large amounts of labelled data to learn. In many practical instances, however, such supervision is not available. Furthermore, collecting labeled instances for training may be expensive or infeasible due to privacy reasons. This calls for approaches that can adapt to new tasks or new domains without needing a lot of labelled data. In this thesis, I address the limited supervision problem from two perspectives. First, I examine methods that exploit large amounts of unlabelled data to learn useful feature representations in a self-supervised manner. Such representations capture rich prior knowledge about the data, allowing them to be useful across many tasks, and enable data-efficient learning of new tasks. In particular, my work is concerned with the following key questions pertaining to text representations - (i) How do we learn representations of larger units of text, beyond words? (ii) How do we design training objectives that can efficiently learn such representations? (iii) How do we come up with representations that allow efficient knowledge transfer to downstream language understanding tasks? Second, I explore models and algorithms capable of learning from limited supervision. My work studies weakly supervised, few-shot and zero-shot learning settings with applications to text generation, sequence modeling, entity understanding and embodied control. My work demonstrates that text descriptions are an effective means of building models that generalize to new domains and new tasks without needing to experience supervised data for the new domain/task. I believe that the next generation of AI technologies will be driven by models that read and understand text to perform tasks.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169634/1/llajan_1.pd

Computational model of learning

The program described learns to improve its performance in the playing of a game, from experience. The main objectives of the project are that the system should observe the following principles: 1) The program should not rely on any special evaluation functions, which would embody domain-specific information. 2) Initial knowledge of the domain should be minimal, and further knowledge gained should be assimilated in terms of prior knowledge 3) The system of representation employed should as far as possible be independent of the domain, again avoiding the incorporation of domain-specific information. In customary Artificial Intelligence terms, the program is referred to as existing in a domain or environment. The model has a goal within this domain and has available certain actions which it may take in order to achieve its goal. The goal is represented as a Structure. This term will be used throughout to denote a set of objects from the domain, constrained by various domain-pertinent relationships. The actions, goals and objects are the initial known facts of the environment. The program has an innate ability to plan simple sequences of actions to achieve its goals. Inevitably, these plans do not take into account enough of the nature of the domain and prove inadequate. In such events the descriptive abilities of the program are invoked to correct the deficiency, and the program's model of its environment is enriched