7,354 research outputs found
Exploiting Cognitive Structure for Adaptive Learning
Adaptive learning, also known as adaptive teaching, relies on learning path
recommendation, which sequentially recommends personalized learning items
(e.g., lectures, exercises) to satisfy the unique needs of each learner.
Although it is well known that modeling the cognitive structure including
knowledge level of learners and knowledge structure (e.g., the prerequisite
relations) of learning items is important for learning path recommendation,
existing methods for adaptive learning often separately focus on either
knowledge levels of learners or knowledge structure of learning items. To fully
exploit the multifaceted cognitive structure for learning path recommendation,
we propose a Cognitive Structure Enhanced framework for Adaptive Learning,
named CSEAL. By viewing path recommendation as a Markov Decision Process and
applying an actor-critic algorithm, CSEAL can sequentially identify the right
learning items to different learners. Specifically, we first utilize a
recurrent neural network to trace the evolving knowledge levels of learners at
each learning step. Then, we design a navigation algorithm on the knowledge
structure to ensure the logicality of learning paths, which reduces the search
space in the decision process. Finally, the actor-critic algorithm is used to
determine what to learn next and whose parameters are dynamically updated along
the learning path. Extensive experiments on real-world data demonstrate the
effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19
Learning Scheduling Algorithms for Data Processing Clusters
Efficiently scheduling data processing jobs on distributed compute clusters
requires complex algorithms. Current systems, however, use simple generalized
heuristics and ignore workload characteristics, since developing and tuning a
scheduling policy for each workload is infeasible. In this paper, we show that
modern machine learning techniques can generate highly-efficient policies
automatically. Decima uses reinforcement learning (RL) and neural networks to
learn workload-specific scheduling algorithms without any human instruction
beyond a high-level objective such as minimizing average job completion time.
Off-the-shelf RL techniques, however, cannot handle the complexity and scale of
the scheduling problem. To build Decima, we had to develop new representations
for jobs' dependency graphs, design scalable RL models, and invent RL training
methods for dealing with continuous stochastic job arrivals. Our prototype
integration with Spark on a 25-node cluster shows that Decima improves the
average job completion time over hand-tuned scheduling heuristics by at least
21%, achieving up to 2x improvement during periods of high cluster load
Planning spatial networks with Monte Carlo tree search
We tackle the problem of goal-directed graph construction: given a starting graph, finding a set of edges whose addition maximally improves a global objective function. This problem emerges in many transportation and infrastructure networks that are of critical importance to society. We identify two significant shortcomings of present reinforcement learning methods: their exclusive focus on topology to the detriment of spatial characteristics (which are known to influence the growth and density of links), as well as the rapid growth in the action spaces and costs of model training. Our formulation as a deterministic Markov decision process allows us to adopt the Monte Carlo tree search framework, an artificial intelligence decision-time planning method. We propose improvements over the standard upper confidence bounds for trees (UCT) algorithm for this family of problems that addresses their single-agent nature, the trade-off between the cost of edges and their contribution to the objective, and an action space linear in the number of nodes. Our approach yields substantial improvements over UCT for increasing the efficiency and attack resilience of synthetic networks and real-world Internet backbone and metro systems, while using a wall clock time budget similar to other search-based algorithms. We also demonstrate that our approach scales to significantly larger networks than previous reinforcement learning methods, since it does not require training a model
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