22,453 research outputs found
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching
Personalization in marketing aims at improving the shopping experience of
customers by tailoring services to individuals. In order to achieve this,
businesses must be able to make personalized predictions regarding the next
purchase. That is, one must forecast the exact list of items that will comprise
the next purchase, i.e., the so-called market basket. Despite its relevance to
firm operations, this problem has received surprisingly little attention in
prior research, largely due to its inherent complexity. In fact,
state-of-the-art approaches are limited to intuitive decision rules for pattern
extraction. However, the simplicity of the pre-coded rules impedes performance,
since decision rules operate in an autoregressive fashion: the rules can only
make inferences from past purchases of a single customer without taking into
account the knowledge transfer that takes place between customers. In contrast,
our research overcomes the limitations of pre-set rules by contributing a novel
predictor of market baskets from sequential purchase histories: our predictions
are based on similarity matching in order to identify similar purchase habits
among the complete shopping histories of all customers. Our contributions are
as follows: (1) We propose similarity matching based on subsequential dynamic
time warping (SDTW) as a novel predictor of market baskets. Thereby, we can
effectively identify cross-customer patterns. (2) We leverage the Wasserstein
distance for measuring the similarity among embedded purchase histories. (3) We
develop a fast approximation algorithm for computing a lower bound of the
Wasserstein distance in our setting. An extensive series of computational
experiments demonstrates the effectiveness of our approach. The accuracy of
identifying the exact market baskets based on state-of-the-art decision rules
from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD 2019
Lifted rule injection for relation embeddings
Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks. Yet, a major challenge is how to efficiently incorporate commonsense knowledge into such models. A recent approach regularizes relation and entity representations by propositionalization of first-order logic rules. However, propositionalization does not scale beyond domains with only few entities and rules. In this paper we present a highly efficient method for incorporating implication rules into distributed representations for automated knowledge base construction. We map entity-tuple embeddings into an approximately Boolean space and encourage a partial ordering over relation embeddings based on implication rules mined from WordNet. Surprisingly, we find that the strong restriction of the entity-tuple embedding space does not hurt the expressiveness of the model and even acts as a regularizer that improves generalization. By incorporating few commonsense rules, we achieve an increase of 2 percentage points mean average precision over a matrix factorization baseline, while observing a negligible increase in runtime
- …