scholarly journals Tutorial on preference handling

Author(s):  
Alexis Tsoukiàs ◽  
Paolo Viappiani
Keyword(s):  
AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 9 ◽  
Author(s):  
Judy Goldsmith ◽  
Ulrich Junker

This article explains the benefits of preferences for AI systems and draws a picture of current AI research on preference handling. It thus provides an introduction to the topics covered by this special issue on preference handling.


2011 ◽  
Vol 11 (4-5) ◽  
pp. 821-839 ◽  
Author(s):  
MARTIN GEBSER ◽  
ROLAND KAMINSKI ◽  
TORSTEN SCHAUB

AbstractPreference handling and optimization are indispensable means for addressing nontrivial applications in Answer Set Programming (ASP). However, their implementation becomes difficult whenever they bring about a significant increase in computational complexity. As a consequence, existing ASP systems do not offer complex optimization capacities, supporting, for instance, inclusion-based minimization or Pareto efficiency. Rather, such complex criteria are typically addressed by resorting to dedicated modeling techniques, likesaturation. Unlike the ease of common ASP modeling, however, these techniques are rather involved and hardly usable by ASP laymen. We address this problem by developing a general implementation technique by means of meta-prpogramming, thus reusing existing ASP systems to capture various forms of qualitative preferences among answer sets. In this way, complex preferences and optimization capacities become readily available for ASP applications.


2015 ◽  
Vol 63 (4) ◽  
pp. 633-652 ◽  
Author(s):  
Kaisa Miettinen ◽  
Dmitry Podkopaev ◽  
Francisco Ruiz ◽  
Mariano Luque

Author(s):  
Michael Bernreiter ◽  
Jan Maly ◽  
Stefan Woltran

Qualitative Choice Logic (QCL) and Conjunctive Choice Logic (CCL) are formalisms for preference handling, with especially QCL being well established in the field of AI. So far, analyses of these logics need to be done on a case-by-case basis, albeit they share several common features. This calls for a more general choice logic framework, with QCL and CCL as well as some of their derivatives being particular instantiations. We provide such a framework, which allows us, on the one hand, to easily define new choice logics and, on the other hand, to examine properties of different choice logics in a uniform setting. In particular, we investigate strong equivalence, a core concept in non-classical logics for understanding formula simplification, and computational complexity. Our analysis also yields new results for QCL and CCL. For example, we show that the main reasoning task regarding preferred models is ϴ₂P-complete for QCL and CCL, while being Δ₂P-complete for a newly introduced choice logic.


AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 37 ◽  
Author(s):  
Yann Chevaleyre ◽  
Ulle Endriss ◽  
Jérôme Lang ◽  
Nicolas Maudet

In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multi-attribute) structure. We give an introduction to preference handling in combinatorial domains in the context of collective decision making, and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains, and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, known as computational social choice, that brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.


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