Elicitation strategies for fuzzy constraint problems with missing preferences: algorithms and experimental studies

by   Mirco Gelain, et al.

Fuzzy constraints are a popular approach to handle preferences and over-constrained problems in scenarios where one needs to be cautious, such as in medical or space applications. We consider here fuzzy constraint problems where some of the preferences may be missing. This models, for example, settings where agents are distributed and have privacy issues, or where there is an ongoing preference elicitation process. In this setting, we study how to find a solution which is optimal irrespective of the missing preferences. In the process of finding such a solution, we may elicit preferences from the user if necessary. However, our goal is to ask the user as little as possible. We define a combined solving and preference elicitation scheme with a large number of different instantiations, each corresponding to a concrete algorithm which we compare experimentally. We compute both the number of elicited preferences and the "user effort", which may be larger, as it contains all the preference values the user has to compute to be able to respond to the elicitation requests. While the number of elicited preferences is important when the concern is to communicate as little information as possible, the user effort measures also the hidden work the user has to do to be able to communicate the elicited preferences. Our experimental results show that some of our algorithms are very good at finding a necessarily optimal solution while asking the user for only a very small fraction of the missing preferences. The user effort is also very small for the best algorithms. Finally, we test these algorithms on hard constraint problems with possibly missing constraints, where the aim is to find feasible solutions irrespective of the missing constraints.


page 1

page 2

page 3

page 4


Uncertainty in Soft Temporal Constraint Problems:A General Framework and Controllability Algorithms forThe Fuzzy Case

In real-life temporal scenarios, uncertainty and preferences are often e...

Planning with Partial Preference Models

Current work in planning with preferences assume that the user's prefere...

Decomposition Strategies for Constructive Preference Elicitation

We tackle the problem of constructive preference elicitation, that is th...

How to cluster nearest unique nodes from different classes using JJCluster in Wisp application?

The work of finding the best place according to user preference is a ted...

Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms

In mixed-initiative co-creation tasks, where a human and a machine joint...

CPDist: Deep Siamese Networks for Learning Distances Between Structured Preferences

Preference are central to decision making by both machines and humans. R...

Holistic evaluation of XML queries with structural preferences on an annotated strong dataguide

With the emergence of XML as de facto format for storing and exchanging ...

Please sign up or login with your details

Forgot password? Click here to reset