Titlebook: Cooperative Information Agents VII; 7th International Wo Matthias Klusch,Andrea Omicini,Heimo Laamanen Conference proceedings 2003 Springer

溫順

J.-H. Huang,L.-C. Wang,C.-J. Changt as mixture of reinforced and supervised learning procedures, is developing into a meta-learning architecture that allows learning agents to improve their learning skills by exchanging information with their peers. This paper reports the latest experiments and results in this subject.

enormous

An Information Notification Model with VPC on KODAMA in an Ubiquitous Computing Environment, and Itshe model enables systems to provide an appropriate amount of information to users depending on circumstances and contains a security function. Using this model, we performed a large-scale experiment involving approximately one thousand participants. This paper contains the results of and discussions regarding this experiment.

表否定

The Search for Coalition Formation in Costly Environmentsese strategies can be derived. Efficient algorithms are suggested for a specific size-two variant of the problem, in order to demonstrate how each agent’s computation process can be significantly improved. These algorithms will be used as an infrastructure from which the general case algorithms can be extracted.

變異

AXIOM

insecticide

Database Integration Using Mobile Agentsd achieve the integration process as soon as possible. In this paper we present a solution, known as DIA (Data Integration using Agents), for semantic integration of federated databases using mobile agents and ontologies. Also, we present an itinerary classification which aims to improve the data integration process.

畢業(yè)典禮

Agents for Collaborative Filteringhe collaborative environment. In particular, we propose Personal Assistants to communicate with the user and to acquire users’ models. Then, a Decision Agent uses such models to decide who must receive the incoming documents.

入伍儀式

Exchanging Advice and Learning to Trustt as mixture of reinforced and supervised learning procedures, is developing into a meta-learning architecture that allows learning agents to improve their learning skills by exchanging information with their peers. This paper reports the latest experiments and results in this subject.

責(zé)任

A Peer-to-Peer Approach to Resource Discovery in Multi-agent Systemsalytical models of their performance for both uniformly random resource requests and for requests in the case of hot spots. Finally, we introduce two approaches to the problem of updating the caches: one that uses flooding to propagate the updates and one that builds on the notion of an inverted cac

venous-leak

Proposal-Based Negotiation in Convex Regionsome cases, very efficient. We also investigate the intrinsic limits of the methodology, showing that there are some worst-case scenarios in which the number of exchanged proposals is exponential both in the number of variables and in the number of constraints.