11,613 research outputs found
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
In multi-objective decision planning and learning, much attention is paid to
producing optimal solution sets that contain an optimal policy for every
possible user preference profile. We argue that the step that follows, i.e,
determining which policy to execute by maximising the user's intrinsic utility
function over this (possibly infinite) set, is under-studied. This paper aims
to fill this gap. We build on previous work on Gaussian processes and pairwise
comparisons for preference modelling, extend it to the multi-objective decision
support scenario, and propose new ordered preference elicitation strategies
based on ranking and clustering. Our main contribution is an in-depth
evaluation of these strategies using computer and human-based experiments. We
show that our proposed elicitation strategies outperform the currently used
pairwise methods, and found that users prefer ranking most. Our experiments
further show that utilising monotonicity information in GPs by using a linear
prior mean at the start and virtual comparisons to the nadir and ideal points,
increases performance. We demonstrate our decision support framework in a
real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at
https://github.com/lmzintgraf/gp_pref_elici
Structuring the decision process : an evaluation of methods in the structuring the decision process
This chapter examines the effectiveness of methods that are designed to provide structure and support to decision making. Those that are primarily aimed at individual decision makers are examined first and then attention is turned to groups. In each case weaknesses of unaided decision making are identified and how successful the application of formal methods is likely to be in mitigating these weaknesses is assessed
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
The assessment of usability of electronic shopping: A heuristic evaluation
Today there are thousands of electronic shops accessible via the Web. Some provide user-friendly features whilst others seem not to consider usability factors at all. Yet, it is critical that the electronic shopping interface is user-friendly so as to help users to obtain their desired results. This study applied heuristic evaluation to examine the usability of current electronic shopping. In particular, it focused on four UK-based supermarkets offering electronic services: including ASDA, Iceland, Sainsbury, and Tesco. The evaluation consists of two stages: a free-flow inspection and a task-based inspection. The results indicate that the most significant and common usability problems have been found to lie within the areas of ‘User Control and Freedom’ and ‘Help and Documentation’. The findings of this study are applied to develop a set of usability guidelines to support the future design of effective interfaces for electronic shopping
Learning to Select Cuts for Efficient Mixed-Integer Programming
Cutting plane methods play a significant role in modern solvers for tackling
mixed-integer programming (MIP) problems. Proper selection of cuts would remove
infeasible solutions in the early stage, thus largely reducing the
computational burden without hurting the solution accuracy. However, the major
cut selection approaches heavily rely on heuristics, which strongly depend on
the specific problem at hand and thus limit their generalization capability. In
this paper, we propose a data-driven and generalizable cut selection approach,
named Cut Ranking, in the settings of multiple instance learning. To measure
the quality of the candidate cuts, a scoring function, which takes the
instance-specific cut features as inputs, is trained and applied in cut ranking
and selection. In order to evaluate our method, we conduct extensive
experiments on both synthetic datasets and real-world datasets. Compared with
commonly used heuristics for cut selection, the learning-based policy has shown
to be more effective, and is capable of generalizing over multiple problems
with different properties. Cut Ranking has been deployed in an industrial
solver for large-scale MIPs. In the online A/B testing of the product planning
problems with more than variables and constraints daily, Cut Ranking has
achieved the average speedup ratio of 12.42% over the production solver without
any accuracy loss of solution.Comment: Paper accepted at Pattern Recognition journa
Selecting the Flexible Last-Mile Delivery Models Using Multicriteria Decision-Making
Postal service providers can reorganise the last-mile delivery process within the scope of universal service and apply some of the flexible models for the organisation of the delivery. In this paper, the question of the selection of Flexible Last-Mile Delivery Models (FLMDMs) is treated using multicriteria decision-making. We have identified four different sustainable last-mile delivery models with an emphasis on the number of delivery workers. One postal service provider from Europe was selected, where the proposed FLMDMs were tested. The proposed last-mile delivery models are ranked using Multiple Criteria Decision Analysis (MCDA) techniques. In this context, MCDA techniques are used to make a comparative assessment of alternatives. The obtained results suggest the AB delivery model as the optimal choice for the last-mile delivery and complete allocation of the number of delivery workers
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