Information flow in logic for distributed systems: Extending graded consequence
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Summary
This paper has focused on a specific context of decision making by aggregating opinions of agents based on an approach, known as the theory of graded consequence, which converts the non-deterministic nature of consequence to a deterministic graded (four-valued) notion of consequence.
- Type
- article
- Published
- 2019-07-01
- Cited by
- 8
- References
- 37
- OpenAlex
- https://openalex.org/W2935256884
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:133010972
Keywords
Computer science, Information flow, Context (archaeology), Inference, Theoretical computer science
References
- Distributed Decision Making and Control
- Modeling and Reasoning with Paraconsistent Rough Sets
- Theories of Judgment: Psychology, Logic, Phenomenology
- Readings in agents
- Distributed Sensor Networks
- Graded Consequence with Fuzzy Set of Premises
- Interactive computations: toward risk management in interactive intelligent systems
- Knowledge and the Flow of Information
- Graded Consequence: Further Studies
- A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
- Rudiments of rough sets
- Reasoning about knowledge
- Risk Management and Interactive Computational Systems
- Approximate reasoning by agents in distributed environments
- Interactive granular computing
- The Mathematics of Learning: Dealing with Data
- Rough Sets and Interactive Granular Computing
- Thriving Rough Sets
- Interactive Granular Computations in Networks and Systems Engineering: A Practical Perspective
- Ensemble learning: A survey
Cited by
- Information flow in context-dependent hierarchical Bayesian inference
- Granular Generalized Variable Precision Rough Sets and Rational Approximations
- Sequential Measurements, Topological Quantum Field Theories, and Topological Quantum Neural Networks
- Rough Sets Turn 40: From Information Systems to Intelligent Systems
- Sequential measurements, TQFTs, and TQNNs
- Toward a Computing Model Dealing with Complex Phenomena: Interactive Granular Computing
- Interactive Granular Computing Model for Intelligent Systems
- Graded Consequence in Decision-Making: A Few Applications
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