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CSAIL Technical Reports (July 1, 2003 - present)

Mostrando recursos 1 - 20 de 1.116

  1. Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation

    Timmons, Eric; Williams, Brian C.
    With the rise of autonomous systems, there is a need for them to have high levels of robustness and safety. This robustness can be achieved through systems that are self-repairing. Underlying this is the ability to diagnose subtle failures. Likewise, online planners can generate novel responses to exceptional situations. These planners require an accurate estimate of state. Estimation methods based on hybrid discrete/continuous state models have emerged as a method of computing precise state estimates, which can be employed for either diagnosis or planning in hybrid domains. However, existing methods have difficulty scaling to systems with more than a handful...

  2. Learning Models of Sequential Decision-Making without Complete State Specification using Bayesian Nonparametric Inference and Active Querying

    Unhelkar, Vaibhav V.; Shah, Julie A.
    Learning models of decision-making behavior during sequential tasks is useful across a variety of applications, including human-machine interaction. In this paper, we present an approach to learning such models within Markovian domains based on observing and querying a decision-making agent. In contrast to classical approaches to behavior learning, we do not assume complete knowledge of the state features that impact an agent's decisions. Using tools from Bayesian nonparametric inference and time series of agents decisions, we first provide an inference algorithm to identify the presence of any unmodeled state features that impact decision making, as well as likely candidate models....

  3. Generalization in Deep Learning

    Kawaguchi, Kenji; Kaelbling, Leslie Pack; Bengio, Yoshua
    With a direct analysis of neural networks, this paper presents a mathematically tight generalization theory to partially address an open problem regarding the generalization of deep learning. Unlike previous bound-based theory, our main theory is quantitatively as tight as possible for every dataset individually, while producing qualitative insights competitively. Our results give insight into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, answering to an open question in the literature. We also discuss limitations of our results and propose additional open problems.

  4. A Natural Language Interface for Mobile Devices

    Katz, Boris; Borchardt, Gary; Felshin, Sue; Mora, Federico
    Creating a robust, automated capability to respond to natural language requests has been a longstanding goal in the development of intelligent systems. This article describes the StartMobile system, originally developed in 2005-2007, which has served as an important precursor to Apple's Siri system and other commercial natural language interfaces to mobile devices and computational resources. The article begins with a discussion of goals in creating natural language interfaces, continues with a description of the general-purpose START information access system, describes the StartMobile system and its capabilities, and concludes with a discussion of current commercial systems and future directions.

  5. continuous Relaxation to Over-constrained Temporal Plans

    Yu, Peng
    When humans fail to understand the capabilities of an autonomous system or its environmental limitations, they can jeopardize their objectives and the system by asking for unrealistic goals. The objective of this thesis is to enable consensus between human and autonomous system, by giving autonomous systems the ability to communicate to the user the reasons for goal failure and the relaxations to goals that archive feasibility. We represent our problem in the context of over-constrained temporal plans. They are commonly encountered while operating autonomous and decision support systems, when user objectives are in conflict with the environment. Over constrained plans...

  6. Risk Allocation for Temporal Risk Assessment

    Wang, Andrew J.
    Temporal uncertainty arises when performing any activity in the natural world. When activities are composed into temporal plans, then, there is a risk of not meeting the plan requirements. Currently, we do not have quantitatively precise methods for assessing temporal risk of a plan. Existing methods that deal with temporal uncertainty either forgo probabilistic models or try to optimize a single objective, rather than satisfy multiple objectives. This thesis offers a method for evaluating whether a schedule exists that meets a set of temporal constraints, with acceptable risk of failure. Our key insight is to assume a form of risk...

  7. Energy-efficient Control of a Smart Grid with Sustainable Homes based on Distributing Risk

    Ono, Masahiro
    The goal of this thesis is to develop a distributed control system for a smart grid with sustainable homes. A central challenge is how to enhance energy efficiency in the presence of uncertainty. A major source of uncertainty in a smart grid is intermittent energy production by renewable energy sources. In the face of global climate change, it is crucial to reduce dependence on fossil fuels and shift to renewable energy sources, such as wind and solar. However, a large-scale introduction of wind and solar generation to an electrical grid poses a significant risk of blackouts since the energy supplied...

  8. Robust, Goal-directed Plan Execution with Bounded Risk

    Ono, Masahiro
    There is an increasing need for robust optimal plan execution for multi-agent systems in uncertain environments, while guaranteeing an acceptable probability of success. For ex- ample, a fleet of unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) are required to operate autonomously for an extensive mission duration in an uncertain environment. Previous work introduced the concept of a model-based executive, which increases the level of autonomy, elevating the level at which systems are commanded. This thesis develops model-based executives that reason explicitly from a stochastic plant model to find the optimal course of action, while ensuring that the probability...

  9. Unsupervised Learning and Recognition of Physical Activity Plans

    Dong, Shuonan
    This thesis desires to enable a new kind of interaction between humans and computational agents, such as robots or computers, by allowing the agent to anticipate and adapt to human intent. In the future, more robots may be deployed in situations that require collaboration with humans, such as scientific exploration, search and rescue, hospital assistance, and even domestic care. These situations require robots to work together with humans, as part of a team, rather than as a stand-alone tool. The intent recognition capability is necessary for computational agents to play a more collaborative role in human-robot interactions, moving beyond the...

  10. Learning and recognition of hybrid manipulation tasks in variable environments using probabilistic flow tubes

    Dong, Shuonan
    Robots can act as proxies for human operators in environments where a human operator is not present or cannot directly perform a task, such as in dangerous or remote situations. Teleoperation is a common interface for controlling robots that are designed to be human proxies. Unfortunately, teleoperation may fail to preserve the natural fluidity of human motions due to interface limitations such as communication delays, non-immersive sensing, and controller uncertainty. I envision a robot that can learn a set of motions that a teleoperator commonly performs, so that it can autonomously execute routine tasks or recognize a user's motion in...

  11. Risk-minimizing program execution in robotic domains

    Effinger, Robert
    In this thesis, we argue that autonomous robots operating in hostile and uncertain environments can improve robustness by computing and reasoning explicitly about risk. Autonomous robots with a keen sensitivity to risk can be trusted with critical missions, such as exploring deep space and assisting on the battlefield. We introduce a novel, risk-minimizing approach to program execution that utilizes program flexibility and estimation of risk in order to make runtime decisions that minimize the probability of program failure. Our risk-minimizing executive, called Murphy, utilizes two forms of program flexibility, 1) flexible scheduling of activity timing, and 2) redundant choice between...

  12. Optimal Temporal Planning at Reactive Time Scales via Dynamic Backtracking Branch and Bound

    Effinger, Robert
    Autonomous robots are being considered for increasingly capable roles in our society, such as urban search and rescue, automation for assisted living, and lunar habitat construction. To fulfill these roles, teams of autonomous robots will need to cooperate together to accomplish complex mission objectives in uncertain and dynamic environments. In these environments, autonomous robots face a host of new challenges, such as responding robustly to timing uncertainties and perturbations, task and coordination failures, and equipment malfunctions. In order to address these challenges, this thesis advocates a novel planning approach, called temporally-flexible contingent planning. A temporally-flexible contingent plan is a compact...

  13. Fast, Approximate State Estimation of Concurrent Probabilistic Hybrid Automata

    Timmons, Eric
    It is an undeniable fact that autonomous systems are simultaneously becoming more common place, more complex, and deployed in more inhospitable environments. Examples include smart homes, smart cars, Mars rovers, unmanned aerial vehicles, and autonomous underwater vehicles. A common theme that all of these autonomous systems share is that in order to appropriately control them and prevent mission failure, they must be able to quickly estimate their internal state and the state of the world. A natural representation of many real world systems is to describe them in terms of a mixture of continuous and discrete variables. Unfortunately, hybrid estimation...

  14. Decision Uncertainty Minimization and Autonomous Information Gathering

    Bush, Lawrence A. M.
    Over the past several decades, technologies for remote sensing and exploration have be- come increasingly powerful but continue to face limitations in the areas of information gathering and analysis. These limitations affect technologies that use autonomous agents, which are devices that can make routine decisions independent of operator instructions. Bandwidth and other communications limitation require that autonomous differentiate between relevant and irrelevant information in a computationally efficient manner.This thesis presents a novel approach to this problem by framing it as an adaptive sensing problem. Adaptive sensing allows agents to modify their information collection strategies in response to the information gathered...

  15. Delay Controllability: Multi-Agent Coordination under Communication Delay

    Bhargava, Nikhil; Muise, Christian; Vaquero, Tiago; Williams, Brian
    Simple Temporal Networks with Uncertainty provide a useful framework for modeling temporal constraints and, importantly, for modeling actions with uncertain durations. To determine whether we can construct a schedule for a given network, we typically consider one of two types of controllability: dynamic or strong. These controllability checks have strict conditions on how uncertainty is resolved; uncertain outcomes are either recognized immediately or not at all. In this paper, we introduce delay controllability, a novel generalization of both strong and dynamic controllability that additionally exposes a large range of controllability classes in between. To do so, we use a delay...

  16. Delay Controllability: Multi-Agent Coordination under Communication Delay

    Bhargava, Nikhil; Muise, Christian; Vaquero, Tiago; Williams, Brian
    Simple Temporal Networks with Uncertainty provide a useful framework for modeling temporal constraints and, importantly, for modeling actions with uncertain durations. To determine whether we can construct a schedule for a given network, we typically consider one of two types of controllability: dynamic or strong. These controllability checks have strict conditions on how uncertainty is resolved; uncertain outcomes are either recognized immediately or not at all. In this paper, we introduce delay controllability, a novel generalization of both strong and dynamic controllability that additionally exposes a large range of controllability classes in between. To do so, we use a delay...

  17. Privacy and Security Risks for National Health Records Systems

    Alawaji, Ahmed; Sollins, Karen
    A review of national health records (NEHR) systems shows that privacy and security risks have a profound impact on the success of such projects. Countries have different approaches when dealing with privacy and security considerations. The aims of this study were to explore how governments can design secure national health records systems. To do that systematically, we developed a framework to analyze NEHR systems. We then applied the framework to investigate the privacy and security risks in these systems. The studied systems demonstrate that getting privacy and security right have a considerable impact on the success of NEHR projects. Also,...

  18. Generating Component-based Supervised Learning Programs From Crowdsourced Examples

    Cambronero, Jose; Rinard, Martin
    We present CrowdLearn, a new system that processes an existing corpus of crowdsourced machine learning programs to learn how to generate effective pipelines for solving supervised machine learning problems. CrowdLearn uses a probabilistic model of program likelihood, conditioned on the current sequence of pipeline components and on the characteristics of the input data to the next component in the pipeline, to predict candidate pipelines. Our results highlight the effectiveness of this technique in leveraging existing crowdsourced programs to generate pipelines that work well on a range of supervised learning problems.

  19. Generating Component-based Supervised Learning Programs From Crowdsourced Examples

    Cambronero, Jose; Rinard, Martin
    We present CrowdLearn, a new system that processes an existing corpus of crowdsourced machine learning programs to learn how to generate effective pipelines for solving supervised machine learning problems. CrowdLearn uses a probabilistic model of program likelihood, conditioned on the current sequence of pipeline components and on the characteristics of the input data to the next component in the pipeline, to predict candidate pipelines. Our results highlight the effectiveness of this technique in leveraging existing crowdsourced programs to generate pipelines that work well on a range of supervised learning problems.

  20. Typesafety for Explicitly-Coded Probabilistic Inference Procedures

    Atkinson, Eric; Carbin, Michael
    Researchers have recently proposed several systems that ease the process of developing Bayesian probabilistic inference algorithms. These include systems for automatic inference algorithm synthesis as well as stronger abstractions for manual algorithm development. However, existing systems whose performance relies on the developer manually constructing a part of the inference algorithm have limited support for reasoning about the correctness of the resulting algorithm. In this paper, we present Shuffle, a programming language for developing manual inference algorithms that enforces 1) the basic rules of probability theory and 2) statistical dependencies of the algorithm's corresponding probabilistic model. We have used Shuffle to...

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