Modeling, Learning and Reasoning with Qualitative Preferences

An Extension of SPARQL for Expressing Qualitative Preferences

Lecture Notes in Computer Science - The Semantic Web – ISWC 2017 ◽

10.1007/978-3-319-68288-4_42 ◽

2017 ◽

pp. 711-727 ◽

Cited By ~ 9

Author(s):

Antonis Troumpoukis ◽

Stasinos Konstantopoulos ◽

Angelos Charalambidis

Keyword(s):

Qualitative Preferences

Download Full-text

Using Temporal Logic to Integrate Goals and Qualitative Preferences into Agent Programming

Lecture Notes in Computer Science - Declarative Agent Languages and Technologies VI ◽

10.1007/978-3-540-93920-7_14 ◽

2009 ◽

pp. 215-232 ◽

Cited By ~ 9

Author(s):

Koen V. Hindriks ◽

M. Birna van Riemsdijk

Keyword(s):

Temporal Logic ◽

Agent Programming ◽

Qualitative Preferences

Download Full-text

TCP − Compose ⋆ – A TCP-Net Based Algorithm for Efficient Composition of Web Services Using Qualitative Preferences

Service-Oriented Computing – ICSOC 2007 - Lecture Notes in Computer Science ◽

10.1007/978-3-540-89652-4_34 ◽

2008 ◽

pp. 453-467 ◽

Cited By ~ 8

Author(s):

Ganesh Ram Santhanam ◽

Samik Basu ◽

Vasant Honavar

Keyword(s):

Web Services ◽

Qualitative Preferences

Download Full-text

Algorithms for Solving Satisfiability Problems with Qualitative Preferences

Correct Reasoning - Lecture Notes in Computer Science ◽

10.1007/978-3-642-30743-0_22 ◽

2012 ◽

pp. 327-344

Author(s):

Enrico Giunchiglia ◽

Marco Maratea

Keyword(s):

Satisfiability Problems ◽

Qualitative Preferences

Download Full-text

Reasoning with Semantic-Enabled Qualitative Preferences

Lecture Notes in Computer Science - Scalable Uncertainty Management ◽

10.1007/978-3-642-40381-1_29 ◽

2013 ◽

pp. 374-386 ◽

Cited By ~ 1

Author(s):

Tommaso Di Noia ◽

Thomas Lukasiewicz ◽

Gerardo I. Simari

Keyword(s):

Qualitative Preferences

Download Full-text

Generating belief functions from qualitative preferences: An approach to eliciting expert judgments and deriving probability functions

Data & Knowledge Engineering ◽

10.1016/s0169-023x(98)00008-1 ◽

1998 ◽

Vol 28 (2) ◽

pp. 145-159 ◽

Cited By ~ 1

Author(s):

Ojelanki K. Ngwenyama ◽

Noel Bryson

Keyword(s):

Belief Functions ◽

Probability Functions ◽

Qualitative Preferences

Download Full-text

Complex optimization in answer set programming

Theory and Practice of Logic Programming ◽

10.1017/s1471068411000329 ◽

2011 ◽

Vol 11 (4-5) ◽

pp. 821-839 ◽

Cited By ~ 26

Author(s):

MARTIN GEBSER ◽

ROLAND KAMINSKI ◽

TORSTEN SCHAUB

Keyword(s):

Computational Complexity ◽

Answer Set Programming ◽

Pareto Efficiency ◽

Modeling Techniques ◽

Complex Optimization ◽

Answer Sets ◽

Implementation Technique ◽

Preference Handling ◽

Answer Set ◽

Qualitative Preferences

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.

Download Full-text