Using self-diagnosis to adapt organizational structures
Proceedings of the fifth international conference on Autonomous agents
Improving fault-tolerance by replicating agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
From Active Objects to Autonomous Agents
IEEE Concurrency
Lessons from Designing and Implementing GARF
OBPDC '95 Selected papers from the Workshop, on Object-Based Parallel and Distributed Computation
Cloning for Intelligent Adaptive Information Agents
Revised Papers from the Second Australian Workshop on Distributed Artificial Intelligence: Multi-Agent Systems: Methodologies and Applications
DARX—A Framework For The Fault-Tolerant Support Of Agent Software
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
SELMAS '05 Proceedings of the fourth international workshop on Software engineering for large-scale multi-agent systems
Monitoring teams by overhearing: a multi-agent plan-recognition approach
Journal of Artificial Intelligence Research
Probabilistically survivable MASs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
ABLE: a toolkit for building multiagent autonomic systems
IBM Systems Journal
Dynamic and adaptive replication for large-scale reliable multi-agent systems
Software engineering for large-scale multi-agent systems
Plan-based replication for fault-tolerant multi-agent systems
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Dynamic resource allocation heuristics for providing fault tolerance in multi-agent systems
Proceedings of the 2008 ACM symposium on Applied computing
Computing the fault tolerance of multi-agent deployment
Artificial Intelligence
| Hi-index | 0.00 |
Distributed cooperative applications (e.g.,e-commerce) are now increasingly being designed as a set of autonomous entities, named agents, which interact and coordinate(thus named a multi-agent system). Such applications are often very dynamic: new agents can join or leave, they can change roles, strategies, etc. This high dynamicity creates new challenges to the traditional approaches of fault-tolerance. As relative importance of agents may evolve during the course of computation and problem solving,we need to dynamically and automatically identify the most critical agents and to adapt their replication strategies (e.g., active or passive, number of replicas), in order to maximize their reliability and their availability. One important issue is then: what kind of information could be used to estimate which agents are most critical agents? In this paper, we will first introduce our prototype architecture for adaptive replication. Then, we will discuss various kinds of information and strategies to estimate criticality of agents: static dependences, dynamic dependences, roles, norms, and plans. Some preliminary measurements and future directions will also be presented.