12,432 research outputs found
Shared Autonomy via Hindsight Optimization
In shared autonomy, user input and robot autonomy are combined to control a
robot to achieve a goal. Often, the robot does not know a priori which goal the
user wants to achieve, and must both predict the user's intended goal, and
assist in achieving that goal. We formulate the problem of shared autonomy as a
Partially Observable Markov Decision Process with uncertainty over the user's
goal. We utilize maximum entropy inverse optimal control to estimate a
distribution over the user's goal based on the history of inputs. Ideally, the
robot assists the user by solving for an action which minimizes the expected
cost-to-go for the (unknown) goal. As solving the POMDP to select the optimal
action is intractable, we use hindsight optimization to approximate the
solution. In a user study, we compare our method to a standard
predict-then-blend approach. We find that our method enables users to
accomplish tasks more quickly while utilizing less input. However, when asked
to rate each system, users were mixed in their assessment, citing a tradeoff
between maintaining control authority and accomplishing tasks quickly
Towards a Holistic Integration of Spreadsheets with Databases: A Scalable Storage Engine for Presentational Data Management
Spreadsheet software is the tool of choice for interactive ad-hoc data
management, with adoption by billions of users. However, spreadsheets are not
scalable, unlike database systems. On the other hand, database systems, while
highly scalable, do not support interactivity as a first-class primitive. We
are developing DataSpread, to holistically integrate spreadsheets as a
front-end interface with databases as a back-end datastore, providing
scalability to spreadsheets, and interactivity to databases, an integration we
term presentational data management (PDM). In this paper, we make a first step
towards this vision: developing a storage engine for PDM, studying how to
flexibly represent spreadsheet data within a database and how to support and
maintain access by position. We first conduct an extensive survey of
spreadsheet use to motivate our functional requirements for a storage engine
for PDM. We develop a natural set of mechanisms for flexibly representing
spreadsheet data and demonstrate that identifying the optimal representation is
NP-Hard; however, we develop an efficient approach to identify the optimal
representation from an important and intuitive subclass of representations. We
extend our mechanisms with positional access mechanisms that don't suffer from
cascading update issues, leading to constant time access and modification
performance. We evaluate these representations on a workload of typical
spreadsheets and spreadsheet operations, providing up to 20% reduction in
storage, and up to 50% reduction in formula evaluation time
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
We present a tutorial on Bayesian optimization, a method of finding the
maximum of expensive cost functions. Bayesian optimization employs the Bayesian
technique of setting a prior over the objective function and combining it with
evidence to get a posterior function. This permits a utility-based selection of
the next observation to make on the objective function, which must take into
account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling areas likely to offer improvement over the current best
observation). We also present two detailed extensions of Bayesian optimization,
with experiments---active user modelling with preferences, and hierarchical
reinforcement learning---and a discussion of the pros and cons of Bayesian
optimization based on our experiences
Intent-Aware Contextual Recommendation System
Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big
Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining
(ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field
cannot be longer than 1,920 characters," the abstract appearing here is
slightly shorter than the one in the PDF fil
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