Learning from cooperation using justifications
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Summary
In this approach, when an agent decides to collaborate with other agents, in addition to the solution for the current problem, it acquires new domain knowledge that consists on explanations (or justifications) that other agents done for the solution they proposed.
- Type
- article
- Published
- 2006-05-27
- Cited by
- 1
- References
- 13
- OpenAlex
- https://openalex.org/W78678285
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:43445676
Keywords
Computer science, Domain (mathematical analysis), Task (project management), Domain knowledge, Multi-agent system
References
- Scaling Up: Distributed Machine Learning with Cooperation
- Intelligent Data Analysis: An Introduction
- UCI Repository of machine learning databases
- Neural Network Ensembles
- The CN2 Induction Algorithm
- Meta-learning in distributed data mining systems: Issues and approaches
- Justification-based Multiagent Learning
- Weiss, Gerhard. Multiagent Systems a Modern Approach to Distributed Artificial Intelligence
- Remembering Similitude Terms in CBR
- When Networks Disagree: Ensemble Methods for Hybrid Neural Networks
- Lazy Induction of Descriptions for Relational Case-Based Learning
- Ensemble Case-Based Reasoning: Collaboration Policies for Multiagent Cooperative CBR
- Extending Learning to Multiple Agents: Issues and a Model for Multi-Agent Machine Learning (MA-ML)
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