1,179 research outputs found
The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference
It is common to view programs as a combination of logic and control: the
logic part defines what the program must do, the control part -- how to do it.
The Logic Programming paradigm was developed with the intention of separating
the logic from the control. Recently, extensive research has been conducted on
automatic generation of control for logic programs. Only a few of these works
considered the issue of automatic generation of control for improving the
efficiency of logic programs. In this paper we present a novel algorithm for
automatic finding of lowest-cost subgoal orderings. The algorithm works using
the divide-and-conquer strategy. The given set of subgoals is partitioned into
smaller sets, based on co-occurrence of free variables. The subsets are ordered
recursively and merged, yielding a provably optimal order. We experimentally
demonstrate the utility of the algorithm by testing it in several domains, and
discuss the possibilities of its cooperation with other existing methods
The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning
This paper presents GRT, a domain-independent heuristic planning system for
STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase,
it estimates the distance between each fact and the goals of the problem, in a
backward direction. Then, in the search phase, these estimates are used in
order to further estimate the distance between each intermediate state and the
goals, guiding so the search process in a forward direction and on a best-first
basis. The paper presents the benefits from the adoption of opposite directions
between the preprocessing and the search phases, discusses some difficulties
that arise in the pre-processing phase and introduces techniques to cope with
them. Moreover, it presents several methods of improving the efficiency of the
heuristic, by enriching the representation and by reducing the size of the
problem. Finally, a method of overcoming local optimal states, based on domain
axioms, is proposed. According to it, difficult problems are decomposed into
easier sub-problems that have to be solved sequentially. The performance
results from various domains, including those of the recent planning
competitions, show that GRT is among the fastest planners
Model compilation: An approach to automated model derivation
An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration
Generalizing to New Tasks via One-Shot Compositional Subgoals
The ability to generalize to previously unseen tasks with little to no
supervision is a key challenge in modern machine learning research. It is also
a cornerstone of a future "General AI". Any artificially intelligent agent
deployed in a real world application, must adapt on the fly to unknown
environments. Researchers often rely on reinforcement and imitation learning to
provide online adaptation to new tasks, through trial and error learning.
However, this can be challenging for complex tasks which require many timesteps
or large numbers of subtasks to complete. These "long horizon" tasks suffer
from sample inefficiency and can require extremely long training times before
the agent can learn to perform the necessary longterm planning. In this work,
we introduce CASE which attempts to address these issues by training an
Imitation Learning agent using adaptive "near future" subgoals. These subgoals
are recalculated at each step using compositional arithmetic in a learned
latent representation space. In addition to improving learning efficiency for
standard long-term tasks, this approach also makes it possible to perform
one-shot generalization to previously unseen tasks, given only a single
reference trajectory for the task in a different environment. Our experiments
show that the proposed approach consistently outperforms the previous
state-of-the-art compositional Imitation Learning approach by 30%.Comment: Present at ICRA 2022 "Compositional Robotics: Mathematics and Tools
Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery
This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator.
These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator.
Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction.
A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time
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