48,548 research outputs found
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
Learning Features and Abstract Actions for Computing Generalized Plans
Generalized planning is concerned with the computation of plans that solve
not one but multiple instances of a planning domain. Recently, it has been
shown that generalized plans can be expressed as mappings of feature values
into actions, and that they can often be computed with fully observable
non-deterministic (FOND) planners. The actions in such plans, however, are not
the actions in the instances themselves, which are not necessarily common to
other instances, but abstract actions that are defined on a set of common
features. The formulation assumes that the features and the abstract actions
are given. In this work, we address this limitation by showing how to learn
them automatically. The resulting account of generalized planning combines
learning and planning in a novel way: a learner, based on a Max SAT
formulation, yields the features and abstract actions from sampled state
transitions, and a FOND planner uses this information, suitably transformed, to
produce the general plans. Correctness guarantees are given and experimental
results on several domains are reported.Comment: Preprint of paper accepted at AAAI'19 conferenc
Generalized Potential Heuristics for Classical Planning
Generalized planning aims at computing solutions that work for all instances of the same domain. In this paper, we show that several interesting planning domains possess compact generalized heuristics that can guide a greedy search in guaranteed polynomial time to the goal, and which work for any instance of the domain . These heuristics are weighted sums of state features that capture the number of objects satisfying a certain first-order logic property in any given state. These features have a meaningful interpretation and generalize naturally to the whole domain. Additionally, we present an approach based on mixed integer linear programming to compute such heuristics automatically from the observation of small training instances. We develop two variations of the approach that progressively refine the heuristic as new states are encountered. We illustrate the approach empirically on a number of standard domains, where we show that the generated heuristics will correctly generalize to all possible instances
Linguistics Landscape: a Cross Culture Perspective
This paper was to aim in discussing the linguistic landscape. It was the visibility and salience of languages on public and commercial signs in a given territory or region (Landry and Bourhis 1997). The linguistic landscape has been described as being somewhere at the junction of sociolinguistics, sociology, social psychology, geography, and media studies. It is a concept used in sociolinguistics as scholars study how languages are visually used in multilingual societies, from large metropolitan centers to Amazonia. For example, some public signs in Jerusalem are in Hebrew, English, and Arabic (Spolsky and Cooper 1991, Ben-Rafael et al., 2006). Studies of the linguistic landscape have been published from research done around the world. The field of study is relatively recent; the linguistic landscape paradigm has evolved rapidly and while it has some key names associated with it, it currently has no clear orthodoxy or theoretical core
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