Paper Info
Title
Determining confidence when integrating contributions from multiple agents
Abstract
Integrating contributions received from other agents is an essential activity in multi-agent systems (MASs). Not only must related contributions be integrated together, but the confidence in each integrated contribution must be determined. In this paper we look specifically at the issue of confidence determination and its effect on developing "principled," highly collaborating MASs. Confidence determination is often masked by ad hoc contribution-integration techniques, viewed as being addressed by agent trust and reputation models, or simply assumed away. We present a domain-independent analysis model that can be used to measure the sensitivity of a collaborative problem-solving system to potentially incorrect confidence-integration assumptions. In analyses performed using our model, we focus on the typical assumption of independence among contributions and the effect that unaccounted-for dependencies have on the expected error in the confidence that the answers produced by the MAS are correct. We then demonstrate how the analysis model can be used to determine confidence bounds on integrated contributions and to identify where efforts to improve contribution-dependency estimates lead to the greatest improvement in solution-confidence accuracy.
Year
DOI
Venue
2007
10.1145/1329125.1329212
AAMAS
Keywords
Field
DocType
reputation model,domain-independent analysis model,collaborative problem-solving system,integrating contribution,integrated contribution,analysis model,confidence determination,multiple agent,contribution-dependency estimate,agent trust,confidence bound,confidence,multi agent system
Confidence bounds,Data mining,Computer science,Artificial intelligence,Analysis models,Machine learning,Reputation
Conference
Citations 
PageRank 
References 
4
0.57
3
Authors
2
Name
Order
Citations
PageRank
Raphen Becker135419.40
Daniel D. Corkill2722467.03