On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Measuring Consensus Effectiveness by a Generalized Entropy Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reasoning with belief functions: an analysis of compatibility
International Journal of Approximate Reasoning
Trust, self-confidence, and operators' adaptation to automation
International Journal of Human-Computer Studies
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
External manifestations of trustworthiness in the interface
Communications of the ACM
A logic for uncertain probabilities
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Possibilistic Merging and Distance-Based Fusion of Propositional Information
Annals of Mathematics and Artificial Intelligence
The consensus operator for combining beliefs
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
Fusion of Possibilistic Knowledge Bases from a Postulate Point of View
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Artificial Intelligence - Special issue on nonmonotonic reasoning
Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development (3rd Edition)
Social contraction and belief negotiation
Information Fusion
Collaboration in Software Engineering: A Roadmap
FOSE '07 2007 Future of Software Engineering
A split-combination approach to merging knowledge bases in possibilistic logic
Annals of Mathematics and Artificial Intelligence
An argumentation framework for merging conflicting knowledge bases
International Journal of Approximate Reasoning
On the Collaborative Development of Para-Consistent Conceptual Models
QSIC '07 Proceedings of the Seventh International Conference on Quality Software
Towards a Belief-Theoretic Model for Collaborative Conceptual Model Development
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Conflict Analysis and Merging Operators Selection in Possibility Theory
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
On the Definition of Essential and Contingent Properties of Subjective Belief Bases
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A model-based approach for merging prioritized knowledge bases in possibilistic logic
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Analyzing the degree of conflict among belief functions
Artificial Intelligence
Measuring conflict between possibilistic uncertain information through belief function theory
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Combining multiple knowledge bases by negotiation: a possibilistic approach
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A statistical approach to the representation of uncertainty in beliefs using spread of opinions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Consolidating multiple requirement specifications through argumentation
Proceedings of the 2011 ACM Symposium on Applied Computing
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Belief merging is concerned with the integration of several belief bases such that a coherent belief base is developed. Various belief merging models that use a belief negotiation game have been developed. These models often consist of two key functions, namely, negotiation and weakening. A negotiation function finds and selects the weakest belief bases among the available belief bases, while the weakening function removes the least valuable set of information from the selected belief base. This process is iteratively repeated until a consistent belief base is developed. In this paper, we extend the current game-based belief merging models by introducing the Subjective belief game model. The Subjective belief game model operates over a Subjective belief profile, which consists of belief bases with Subjectively annotated formulas. The Subjective information attached to each formula enables the proposed model to prioritize the formulas in the merging process. One of the advantages of the proposed game is that it provides room for enhancing the content of the weak belief bases, instead of enforcing their further weakening. Trust worthiness of the information sources is also considered. We provide several instantiations of the model. The Subjective belief game model can be useful for formalizing a negotiation process between the human participants of a design process in cases where discrepancies and conflicts arise. We apply this belief game model to an example case study of collaboratively designing some parts of unified modeling language (UML) class diagram for vehicle design.