Handbook of logic in artificial intelligence and logic programming (vol. 3)
Parameters for Utilitarian Desires in a Qualitative Decision Theory
Applied Intelligence
Towards a Possibilistic Logic Handling of Preferences
Applied Intelligence
Knowledge-Driven versus Data-Driven Logics
Journal of Logic, Language and Information
Autonomous Agents and Multi-Agent Systems
Abstracting soft constraints: framework, properties, examples
Artificial Intelligence
A Practical Approach to Fusing Prioritized Knowledge Bases
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Bipolarity in Flexible Querying
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
Bridging Logical, Comparative, and Graphical Possibilistic Representation Frameworks
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Journal of Artificial Intelligence Research
Weakening conflicting information for iterated revision and knowledge integration
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Introducing variable importance tradeoffs into CP-nets
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Logic-based approaches to information fusion
Information Fusion
A spectrum of compromise aggregation operators for multi-attribute decision making
Artificial Intelligence
Logical handling of uncertain, ontology-based, spatial information
Fuzzy Sets and Systems
Preference-Based Uncertain Data Integration
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Handling conditional preferences in recommender systems
Proceedings of the 14th international conference on Intelligent user interfaces
Comparing sets of positive and negative arguments: Empirical assessment of seven qualitative rules
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Geometry of Spatial Bipolar Fuzzy Sets Based on Bipolar Fuzzy Numbers and Mathematical Morphology
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
On the qualitative comparison of decisions having positive and negative features
Journal of Artificial Intelligence Research
Equivalent bipolar fuzzy relations
Fuzzy Sets and Systems
g-BDI: A Graded Intensional Agent Model for Practical Reasoning
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Uncertainty in bipolar preference problems
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Bipolar preference problems: framework, properties and solving techniques
CSCLP'06 Proceedings of the constraint solving and contraint logic programming 11th annual ERCIM international conference on Recent advances in constraints
A Survey on Uncertainty Management in Data Integration
Journal of Data and Information Quality (JDIQ)
An integrated possibilistic framework for goal generation in cognitive agents
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Integrating Bipolar Fuzzy Mathematical Morphology in Description Logics for Spatial Reasoning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A flexible bipolar querying approach with imprecise data and guaranteed results
Fuzzy Sets and Systems
A possibilistic approach to goal generation in cognitive agents
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Lattices of fuzzy sets and bipolar fuzzy sets, and mathematical morphology
Information Sciences: an International Journal
Preferences in AI: An overview
Artificial Intelligence
A graded BDI agent model to represent and reason about preferences
Artificial Intelligence
Aggregation functions and contradictory information
Fuzzy Sets and Systems
Gradualness, uncertainty and bipolarity: Making sense of fuzzy sets
Fuzzy Sets and Systems
Bipolar representations in reasoning, knowledge extraction and decision processes
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Bipolar queries: An aggregation operator focused perspective
Fuzzy Sets and Systems
Mathematical morphology on bipolar fuzzy sets: general algebraic framework
International Journal of Approximate Reasoning
On a fuzzy bipolar relational algebra
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A fuzzy and bipolar approach to preference modeling with application to need and desire
Fuzzy Sets and Systems
A hybrid fuzzy-based personalized recommender system for telecom products/services
Information Sciences: an International Journal
Multiuser museum interactives for shared cultural experiences: an agent-based approach
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Hi-index | 0.01 |
The bipolar view in preference modeling distinguishes between negative and positive preferences. Negative preferences correspond to what is rejected, considered unacceptable, while positive preferences correspond to what is desired. But what is tolerated (i.e., not rejected) is not necessarily desired. Both negative and positive preferences can be a matter of degree. Bipolar preferences can be represented in possibilistic logic by two separate sets of formulas: prioritized constraints, which describe what is more or less tolerated, and weighted positive preferences, expressing what is particularly desirable. The problem of merging multiple-agent preferences in this bipolar framework is then discussed. Negative and positive preferences are handled separately and are combined in distinct ways. Since negative and positive preferences are stated separately, they may be inconsistent, especially in this context of preference fusion. Consistency can be enforced by restricting what is desirable to what is tolerated. After merging, and once the bipolar consistency is restored, the set of preferred solutions can be logically characterized. Preferred solutions should have the highest possible degree of feasibility, and only constraints with low priority may have to be discarded in case of inconsistency inside negative preferences. Moreover, preferred solutions should satisfy important positive preferences when feasible (positive preferences may be also inconsistent). Two types of preferred solutions can be characterized, either in terms of a disjunctive combination of the weighted positive preferences, or in terms of a cardinality-based evaluation.