A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following Agents

Teaching agents to follow complex written instructions has been an important yet elusive goal. One technique for enhancing learning efficiency is language reward shaping (LRS). Within a reinforcement learning (RL) framework, LRS involves training a reward function that rewards behaviours precisely aligned with given language instructions. We argue that the apparent success of LRS is brittle, and prior positive findings can be attributed to weak RL baselines. Specifically, we identified suboptimal LRS designs that reward partially matched trajectories, and we characterised a novel reward perturbation to capture this issue using the concept of loosening task constraints. We provided theoretical and empirical evidence that agents trained using LRS rewards converge more slowly compared to pure RL agents. Our work highlights the brittleness of existing LRS methods, which has been overlooked in the previous studies

Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability

Goal recognition is a fundamental cognitive process that enables individuals to infer intentions based on available cues. Current goal recognition algorithms often take only observed actions as input, but here we use a Bayesian framework to explore the role of actions, timing, and goal solvability in goal recognition. We analyze human responses to goal-recognition problems in the Sokoban domain, and find that actions are assigned most importance, but that timing and solvability also influence goal recognition in some cases, especially when actions are uninformative. We leverage these findings to develop a goal recognition model that matches human inferences more closely than do existing algorithms. Our work provides new insight into human goal recognition and takes a step towards more human-like AI models.Comment: Accepted by AAMAS 202

X-ray characterization of BUSARD chip: A HV-SOI monolithic particle detector with pixel sensors under the buried oxide

This work presents the design of BUSARD, an application specific integrated circuit (ASIC) for the detection of ionizing particles. The ASIC is a monolithic active pixel sensor which has been fabricated in a High-Voltage Silicon-On-Insulator (HV-SOI) process that allows the fabrication of a buried N+ diffusion below the Buried OXide (BOX) as a standard processing step. The first version of the chip, BUSARD-A, takes advantage of this buried diffusion as an ionizing particle sensor. It includes a small array of 13×13 pixels, with a pitch of 80 μm, and each pixel has one buried diffusion with a charge amplifier, discriminator with offset tuning and digital processing. The detector has several operation modes including particle counting and Time-over-Threshold (ToT). An initial X-ray characterization of the detector was carried out, obtaining several pulse height and ToT spectra, which then were used to perform the energy calibration of the device. The Molybdenum $_{α}$ emission was measured with a standard deviation of 127 e$^{-}$ of ENC by using the analog pulse output, and with 276 e$^{-}$ of ENC by using the ToT digital output. The resolution in ToT mode is dominated by the pixel-to-pixel variation

Generalized Planning for the Abstraction and Reasoning Corpus

The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that poses difficulties for pure machine learning methods due to its requirement for fluid intelligence with a focus on reasoning and abstraction. In this work, we introduce an ARC solver, Generalized Planning for Abstract Reasoning (GPAR). It casts an ARC problem as a generalized planning (GP) problem, where a solution is formalized as a planning program with pointers. We express each ARC problem using the standard Planning Domain Definition Language (PDDL) coupled with external functions representing object-centric abstractions. We show how to scale up GP solvers via domain knowledge specific to ARC in the form of restrictions over the actions model, predicates, arguments and valid structure of planning programs. Our experiments demonstrate that GPAR outperforms the state-of-the-art solvers on the object-centric tasks of the ARC, showing the effectiveness of GP and the expressiveness of PDDL to model ARC problems. The challenges provided by the ARC benchmark motivate research to advance existing GP solvers and understand new relations with other planning computational models. Code is available at github.com/you68681/GPAR.Comment: Accepted at AAAI 2024 (extended version

Efectos de radiación ionizante en dispositivos y circuitos MOS

En este tutorial se hace una breve revisión de efectos de radiación en dispositivos y circuitos MOS. Se presentan en primer lugar efectos de dosis ionizante total, describiendo los efectos físicos que dan lugar a la modificación de características eléctricas de los dispositivos y como esa modificación puede afectar el comportamiento de circuitos en tecnologías modernas. Se presenta cómo las modificaciones eléctricas en los dispositivos pueden ser aprovechados para construir sensores en un dosímetro de radiación ionizante. Finalmente se presentan efectos puntuales en el funcionamiento de circuitos causados por el paso de una única partícula.Sección: Tutoriales – ResúmenesCentro de Técnicas Analógico-Digitale

Technological applications of CMOS image sensors as detectors of ionizing radiation

El presente proyecto está orientado al desarrollo de sistemas de detección de radiación ionizante basados en sensores de imágenes tipo CMOS orientados a aplicaciones específicas, aprovechando las técnicas de detección desarrolladas por el grupo de trabajo en los últimos años.This project is oriented to the development of ionizing radiation detection systems based on CMOS type image sensors oriented to specific applications, taking advantage of the detection techniques developed by the working group in recent years

Performance evaluation of GaN and Si based driver circuits for a SiC MOSFET power switch

Silicon Carbide (SiC), new power switches (PSW) require new driver circuits which can take advantage of their new capabilities. In this paper a novel Gallium Nitride (GaN) based gate driver is proposed as a solution to control SiC power switches. The proposed driver is implemented and is performance compared with its silicon (Si) counterparts on a hard switching environment. A thorough evaluation of the energy involved in the switching process is presented showing that the GaN based circuit exhibits similar output losses but reduces the control power needed to operate at a specified frequency.Fil: Carra, Martin Javier. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electrónica; ArgentinaFil: Tacca, Hernán Emilio. Universidad de Buenos Aires; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro | Universidad Nacional de Cuyo. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro; ArgentinaFil: Lipovetzky, José. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad Ejecutora Instituto de Nanociencia y Nanotecnología. Unidad Ejecutora Instituto de Nanociencia y Nanotecnología - Nodo Bariloche | Comisión Nacional de Energía Atómica. Unidad Ejecutora Instituto de Nanociencia y Nanotecnología. Unidad Ejecutora Instituto de Nanociencia y Nanotecnología - Nodo Bariloche; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentin