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Showing 1 to 12 of 12 for “"inverse planning"”.

  1. Scaling Cooperative Intelligence via Inverse Planning and Probabilistic Programming

    … approach to building such systems via inverse planning and probabilistic programming. First, we introduce a probabilistic programming architecture that implements a Bayesian theory of mind. This architecture, Sequential Inverse Plan Search (SIPS), performs online inference of human …

    mit Repository record for Scaling Cooperative Intelligence via Inverse Planning and Probabilistic Programming (opens in a new tab)

  2. Efficient and Intelligent Radiotherapy Planning and Adaptation

    Treatment planning--inverse planning on volumetric tomography images--is the foundation of modern radiotherapy. Contouring planning structures and optimizing plan dose distributions are the two most important components of treatment planning. Treatment planning happens in two stages. The initial …

    utswmed Repository record for Efficient and Intelligent Radiotherapy Planning and Adaptation (opens in a new tab)

  3. Evaluation of Radiobiological Effects in Intensity Modulated Proton Therapy : New Strategies for Inverse Treatment Planning

    Currently, treatment planning for intensity modulated proton therapy (IMPT) usually disregards variations of the relative biological effectiveness (RBE). To investigate the potential clinical relevance of a variable RBE for beam scanning techniques, new strategies for the evaluation of …

    heid-diss Repository record for Evaluation of Radiobiological Effects in Intensity Modulated Proton Therapy : New Strategies for Inverse Treatment Planning (opens in a new tab)

  4. Modeling Human Planning in Maze Orienteering Problems

    … to develop algorithms that build models of human planning given their past decisions. In this thesis project, I focused on modeling human planning in Maze Orienteering Problems (MOP), an optimization problem with the objective to maximize collected rewards within a time limit in a partially known …

    mit Repository record for Modeling Human Planning in Maze Orienteering Problems (opens in a new tab)

  5. PDDL.jl: An Extensible Interpreter and Compiler Interface for Fast and Flexible AI Planning

    The Planning Domain Definition Language (PDDL) is a formal specification language for symbolic planning problems and domains that is widely used by the AI planning community. However, most implementations of PDDL are closely tied to particular planning systems and algorithms, and are not designed …

    mit Repository record for PDDL.jl: An Extensible Interpreter and Compiler Interface for Fast and Flexible AI Planning (opens in a new tab)

  6. Moment Constraints in Radiation Therapy Planning Optimization Incorporating Patient-Specific Anatomical Geometry

    … damage to surrounding healthy structures. In inverse planning, dose-volume histogram (DVH) is a key concept for measuring and restricting collateral radiation damage to healthy tissues. Interpreting the DVH as a probability distribution, a framework is proposed to assess the deviation from a …

    calgary Repository record for Moment Constraints in Radiation Therapy Planning Optimization Incorporating Patient-Specific Anatomical Geometry (opens in a new tab)

  7. Inverse Inverse Graphics

    … if we think of the audience's mind as solving inverse problems—perception as inverse rendering, action understanding as inverse planning—then we can think of expression as solving a kind of *inverse* inverse problem. I then show how to implement such "inverse inverse" methods computationally. …

    mit Repository record for Inverse Inverse Graphics (opens in a new tab)

  8. A computational framework for emotion understanding

    … solution to a large class of ill-posed inverse problems. To interpret someone's expression, or predict how that person would react in a future situation, observers reason over a logically- and causally-structured intuitive theory of other minds. For this work, I chose a domain that is …

    mit Repository record for A computational framework for emotion understanding (opens in a new tab)

  9. Bayesian computational models for inferring preferences

    … utilities over bundles), Machine Learning (inverse reinforcement learning), and cognitive science (theory of mind and inverse planning). Chapter 1 lays the conceptual groundwork for the thesis and introduces key challenges for learning preferences that motivate chapters 2 and 3. I adopt a …

    mit Repository record for Bayesian computational models for inferring preferences (opens in a new tab)

  10. Treatment Planning Automation For Rectal Cancer Radiotherapy

    … Safe radiotherapy treatments require specialized planning expertise and are time-consuming and labor-intensive to produce.</p> <h2>Purpose:</h2> <p>To alleviate the health disparity and promote the safe and quality use of radiotherapy in treating rectal cancers, the entire treatment planning

    uthsc Repository record for Treatment Planning Automation For Rectal Cancer Radiotherapy (opens in a new tab)

  11. Robots as language users: a computational model for pragmatic word learning

    … social interaction. Using techniques for inverse planning and control, the algorithms we have developed seek to understand the goal or purpose driving the behaviors of the interaction. We describe the application of these techniques to a set of human-robot interaction experiments, modeled …

    uiuc Repository record for Robots as language users: a computational model for pragmatic word learning (opens in a new tab)

  12. Radiation therapy treatment plan optimization accounting for random and systematic patient setup uncertainties

    … before the treatment course used in treatment planning. Unfortunately, patient alignment is not perfect and results in residual errors in patient setup. The standard technique for dealing with errors in patient setup is to expand the volume of the target by some margin to ensure the target …

    vcu Repository record for Radiation therapy treatment plan optimization accounting for random and systematic patient setup uncertainties (opens in a new tab)