3,291 research outputs found
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
Robotic grasping of 3D deformable objects is critical for real-world
applications such as food handling and robotic surgery. Unlike rigid and
articulated objects, 3D deformable objects have infinite degrees of freedom.
Fully defining their state requires 3D deformation and stress fields, which are
exceptionally difficult to analytically compute or experimentally measure.
Thus, evaluating grasp candidates for grasp planning typically requires
accurate, but slow 3D finite element method (FEM) simulation. Sampling-based
grasp planning is often impractical, as it requires evaluation of a large
number of grasp candidates. Gradient-based grasp planning can be more
efficient, but requires a differentiable model to synthesize optimal grasps
from initial candidates. Differentiable FEM simulators may fill this role, but
are typically no faster than standard FEM. In this work, we propose learning a
predictive graph neural network (GNN), DefGraspNets, to act as our
differentiable model. We train DefGraspNets to predict 3D stress and
deformation fields based on FEM-based grasp simulations. DefGraspNets not only
runs up to 1500 times faster than the FEM simulator, but also enables fast
gradient-based grasp optimization over 3D stress and deformation metrics. We
design DefGraspNets to align with real-world grasp planning practices and
demonstrate generalization across multiple test sets, including real-world
experiments.Comment: To be published in the IEEE Conference on Robotics and Automation
(ICRA), 202
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