4,699 research outputs found
Decomposition Strategies for Constructive Preference Elicitation
We tackle the problem of constructive preference elicitation, that is the
problem of learning user preferences over very large decision problems,
involving a combinatorial space of possible outcomes. In this setting, the
suggested configuration is synthesized on-the-fly by solving a constrained
optimization problem, while the preferences are learned itera tively by
interacting with the user. Previous work has shown that Coactive Learning is a
suitable method for learning user preferences in constructive scenarios. In
Coactive Learning the user provides feedback to the algorithm in the form of an
improvement to a suggested configuration. When the problem involves many
decision variables and constraints, this type of interaction poses a
significant cognitive burden on the user. We propose a decomposition technique
for large preference-based decision problems relying exclusively on inference
and feedback over partial configurations. This has the clear advantage of
drastically reducing the user cognitive load. Additionally, part-wise inference
can be (up to exponentially) less computationally demanding than inference over
full configurations. We discuss the theoretical implications of working with
parts and present promising empirical results on one synthetic and two
realistic constructive problems.Comment: Accepted at the Thirty-Second AAAI Conference on Artificial
Intelligence (AAAI-18
Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources
The growing deployment of sensors as part of Internet of Things (IoT) is
generating thousands of event streams. Complex Event Processing (CEP) queries
offer a useful paradigm for rapid decision-making over such data sources. While
often centralized in the Cloud, the deployment of capable edge devices on the
field motivates the need for cooperative event analytics that span Edge and
Cloud computing. Here, we identify a novel problem of query placement on edge
and Cloud resources for dynamically arriving and departing analytic dataflows.
We define this as an optimization problem to minimize the total makespan for
all event analytics, while meeting energy and compute constraints of the
resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for
such dynamic dataflows, and validate them using detailed simulations for 100 -
1000 edge devices and VMs. The results show that our heuristics offer
O(seconds) planning time, give a valid and high quality solution in all cases,
and reduce the number of query migrations. Furthermore, rebalance strategies
when applied in these heuristics have significantly reduced the makespan by
around 20 - 25%.Comment: 11 pages, 7 figure
On the decomposition of Generalized Additive Independence models
The GAI (Generalized Additive Independence) model proposed by Fishburn is a
generalization of the additive utility model, which need not satisfy mutual
preferential independence. Its great generality makes however its application
and study difficult. We consider a significant subclass of GAI models, namely
the discrete 2-additive GAI models, and provide for this class a decomposition
into nonnegative monotone terms. This decomposition allows a reduction from
exponential to quadratic complexity in any optimization problem involving
discrete 2-additive models, making them usable in practice
Learning Adaptive Display Exposure for Real-Time Advertising
In E-commerce advertising, where product recommendations and product ads are
presented to users simultaneously, the traditional setting is to display ads at
fixed positions. However, under such a setting, the advertising system loses
the flexibility to control the number and positions of ads, resulting in
sub-optimal platform revenue and user experience. Consequently, major
e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible
ways to display ads. In this paper, we investigate the problem of advertising
with adaptive exposure: can we dynamically determine the number and positions
of ads for each user visit under certain business constraints so that the
platform revenue can be increased? More specifically, we consider two types of
constraints: request-level constraint ensures user experience for each user
visit, and platform-level constraint controls the overall platform monetization
rate. We model this problem as a Constrained Markov Decision Process with
per-state constraint (psCMDP) and propose a constrained two-level reinforcement
learning approach to decompose the original problem into two relatively
independent sub-problems. To accelerate policy learning, we also devise a
constrained hindsight experience replay mechanism. Experimental evaluations on
industry-scale real-world datasets demonstrate the merits of our approach in
both obtaining higher revenue under the constraints and the effectiveness of
the constrained hindsight experience replay mechanism.Comment: accepted by CIKM201
Bayesian decision support for complex systems with many distributed experts
Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems
Georgia Aquarium Design Space Analysis and Optimization
AbstractThe Ocean Voyager exhibit residing at the Georgia Aquarium Inc. (GAI) is one of the largest reef gallon aquariums in the world, with a capacity greater than 6.2M gallons. Reef aquariums are closed systems and must compensate by ‘turning over’ their complete volume of water many times a day through biological, chemical, and mechanical filtration. Due to the Georgia Aquarium being a non-profit organization, GAI sought to investigate ways to maximize efficiency and lower operating costs. This paper will focus on using low-cost software solutions to perform trade space analyses and optimization directed towards the Ocean Voyager exhibit and related GA Aquarium life support and energy systems.The software solution herein demonstrates a top-down System of Systems (SoS) to subsystem modeling approach that provides decision makers with interdisciplinary dashboard-level tools to visualize system design. The goal of the analysis is to provide executive level decision-making support for designing or enhancing existing complex systems and SoS. The analysis was performed as a capstone project by Georgia Tech graduate students progressing from cradle to finish in just 9 weeks to show the benefits of systems engineering to Georgia Aquarium staff. Integrating software SE tools into a single, aggregate model enables project engineers and decision makers to direct design directions with confidence
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