InfoSleuth: agent-based semantic integration of information in open and dynamic environments
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Autonomous Agents that Learn to Better Coordinate
Autonomous Agents and Multi-Agent Systems
Learning and joint deliberation through argumentation in multiagent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Incorporating temporal-bounded CBR techniques in real-time agents
Expert Systems with Applications: An International Journal
Amalgam-Based reuse for multiagent case-based reasoning
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Multiagent and Grid Systems
| Hi-index | 0.00 |
Recent explosion in networked information resources has been attracting much attention to the problem of automated methods for gathering information in response to a query from a user. However, most of this literature deals with locating, gathering and selecting the best response to a query from among a multitude of responses from different sources. In this paper, we deal with a different model of response to a query. No single source of information may contain the complete response to a query and hence may necessitate piecing together mutually related partial responses from disparate and possibly heterogeneous sources. We present a system for cooperative retrieval and composition of a case in which sub-cases are distributed across different agents in a multi-agent system. From a Gestalt perspective, a good overall case may not be the one derived from the summation of best subcases. Each agent''s partial view may result in local cases that are best matches based on the local view. However, these local cases when assembled may not result in the best overall case in terms of global measures. We propose a negotiation-driven case retrieval algorithm as an approach to dynamically resolving inconsistencies between different case pieces during the retrieval process.