Preference Reasoning

@inproceedings{Rossi2005PreferenceR,
  title={Preference Reasoning},
  author={Francesca Rossi},
  booktitle={CP},
  year={2005}
}
  • F. Rossi
  • Published in CP 1 October 2005
  • Economics, Computer Science
Constraints and preferences are ubiquitous in real-life. Moreover, preferences can be of many kinds: qualitative, quantitative, conditional, positive or negative, to name a few. Our ultimate goal is to define and study formalisms that can model problems with both constraints and many kind of preferences, possibly defined by several agents, and to develop tools to solve such problems efficiently. In this paper we briefly report on recent work towards this goal. 
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References

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mCP Nets: Representing and Reasoning with Preferences of Multiple Agents
We introduce mCP nets, an extension of the CP net formalism to model and handle the qualitative and conditional preferences of multiple agents. We give a number of different semantics for reasoning
Possibility Theory for Reasoning About Uncertain Soft Constraints
TLDR
This paper considers an existing technique to perform an integration with fuzzy preferences and proposes various alternative semantics which allow us to observe both the preference level and the robustness w.r.t. uncertainty of the complete instantiations.
Reasoning about soft constraints and conditional preferences: complexity results and approximation techniques
TLDR
This work proposes a framework, based on both CP-nets and soft constraints, that handles both hard andsoft constraints as well as conditional preferences efficiently and uniformly, and shows how soft constraints can faithfully approximate the semantics of conditional preference statements whilst improving the computational complexity.
Aggregating preferences cannot be fair
TLDR
It is proved that, under certain conditions on the kind of partial orders that are allowed to express the preferences of the agents and of the result, if there are at least two agents and three outcomes to order, no preference aggregation system can be fair.
Acquiring Both Constraint and Solution Preferences in Interactive Constraint Systems
TLDR
This paper defines an interactive framework where it is possible to state preferences both over constraints and over solutions, and proposes a way to build a system with such features by pairing a soft constraint solver and a learning module, which learns preferences over constraints from preferences over solutions.
Controllability of Soft Temporal Constraint Problems
TLDR
This work develops a dynamic execution algorithm that produces plans under uncertainty that are optimal w.r.t. preference and proves that dealing simultaneously with preferences and uncertainty does not increase the complexity beyond that of the separate cases.
Preference‐Based Constrained Optimization with CP‐Nets
TLDR
An approach for constrained optimization based on a set of hard constraints and a preference ordering represented using a CP‐network—a graphical model for representing qualitative preference information offers both pragmatic and computational advantages.
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TLDR
A new algorithm for finding the optimal outcomes of a constrained CP-net which makes use of hard constraint solving and a weighted constraint approach that allows to find good solutions even when optimals do not exist.
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TLDR
It is shown how this framework can be used to model both old and new constraint solving and optimization schemes, thus allowing one to both formally justify many informally taken choices in existing schemes, and to prove that local consistency techniques can beused also in newly defined schemes.
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